DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
All pending claims 1-10 and 12-20 filed December 11, 2025 are examined in this final office action. Claim 11 was cancel by Applicant.
Response to Arguments
Applicant's arguments filed December 11, 2025 have been fully considered but they are not persuasive. The teachings of Krasadakis-Awoyemi have been augmented/clarified to address amendments to each independent claim.
Applicant
However, a user specifying boundaries is not the generative AI "analyzing the buyer preference data to generate a set of bounds," as recited in amended claim 1. At best, as noted in the Present Action, if the buyer has not defined their pricing limits, the buyer AI negotiator 206 uses generic market data to infer the limits. However, this market data does not indicate buyer preferences.
Examiner
The undersigned examiner respectfully disagrees with the Applicant’s characterization of Krasadakis. The AI negotiator evaluates the buyer’s preferences and will set new boundaries when necessary. The following excerpts from Krasadakis are recited below:
[Krasadakis: 0019] … For instance, if at the start of the time window, a few seller AI negotiators achieve unexpectedly high profit margins for product A, then other AI negotiators for the same or other products, may increase the elasticity regarding profit margins because the shared goal is most likely going to be met. In this manner, multiple seller AI negotiators may work together on shared goals of the seller. Please note: Bounds can be set by the AI negotiator(s).
[Krasadakis: 0046] For example, the buyer negotiation parameters 116 may include a collection of specifications that the buyer 124 initially requested, but through negotiations with all available AI seller agents at a particular time, only a subset of specifications are available in for-sale products and the difference between what the buyer requested and the reality of product offerings exceeds a preset buyer negotiation elasticity 118. Consequently, constraints placed by the buyer negotiation elasticity 118 may be relaxed (thereby increasing the elasticity) and brought in line with the reality of the AI seller agents, … Please note: Buyer negotiation boundaries can be changed by the AI negotiator.
[Krasadakis: 0057] … Alternatively, the buyer and/or seller elasticity may be set or adjusted based on market conditions instead of being preset by the buyer and seller, respectively.
[Krasadakis: 0072] … The goal-sharing AI negotiators 206 and 208 may be configured to adjust elasticity thresholds for particular negotiations based on achievement of the shared buying and selling goals.
See below for more details.
35 USC § 101
All independent claims require a generative artificial intelligence model to be trained on transactional data associated with vehicle purchases. Given the autonomous negotiations driven by the trained AI model integral to claims as a whole, execution of each independent claim and respective dependents effectively improve computing efficiency by reducing processing cycles and network traffic, and therefore render independent claims and dependents as a practical application under Step 2A (second prong).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 4, 5, 8-10, 12-14, 17, 18 and 20 are rejected under 35 USC 103 as being unpatentable over Krasadakis, US 2017/0287038, in view of Awoyemi et al., US 2022/0044289 “Awoyemi.”
In Krasadakis see at least (underlined text is for emphasis):
Regarding claim 1: A computer-implemented method for implementing generative artificial intelligence to negotiate for a purchase of a vehicle, the method comprising:
[Krasadakis: 0004] Some examples are directed to a framework in which artificial intelligence (AI) negotiation agents (otherwise known as robots or “bots”) are used to identify products or services and negotiate offer terms for buyers and sellers.
[Krasadakis: 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 buyers and sellers may communicate using the disclosed framework in an optimized way that allows various aspects of purchasing deals to be negotiated and adjusted nearly instantly. In some examples, the consumers use their client devices (e.g., laptop, smart phone, tablet, etc.) to anonymously organize and set up automated, on-going buying plans executed by multi-criteria decision-making negotiation agents on a server, referred to herein as “buyer AI negotiators” (“AI buyers,” “AI buyer agent,” “consumer bot,” or “purchasing bot” for short), for purchasing particular products (e.g., 2016 Tesla model S) or types of products (e.g., compact sedan automobile). At the same time, some examples allow retailers to set up automated, on-going selling plans or selling campaigns executed by multi-criteria decision-making negotiation agents on a server, referred to herein as “seller AI negotiators” (or “AI sellers” “AI seller agent” for short), that offer the seller's products or services and automatically negotiate purchasing deals with the buyer AI negotiators.
detecting, by one or more processors, a signal that a buyer would like to purchase a vehicle, wherein the signal indicates a vehicle type of the vehicle; and
[Krasadakis: 0014] for purchasing particular products (e.g., 2016 Tesla model S) or types of products (e.g., compact sedan automobile).
[Krasadakis: 0023] Additionally or alternatively, some parameters may be defined as “blockers,” meaning that, regardless of whether other parameters or elasticity thresholds are met, if a blocker parameter is detected, an offering from the seller AI negotiators are not to be considered. For example, if a user is looking for a particular vehicle that has certain specifications (e.g., horsepower) but only wants to purchase a new vehicle, … Please note: New vehicle qualifies as a vehicle type.
[Krasadakis: 0044] The buyer 124 may specify the particulars of the product he or she is looking to purchase, including any combination of the following: product name, specifications, price (target or range), delivery, availability (e.g., whether the product is needed now or the buyer 124 is willing to wait a specific, configurable, amount of time), trendiness (as judged by the proliferation of the product being mentioned online through web sites or in social media), supply, demand, seasonality (e.g., spring collection of a particular shoe), or the like. In another example, the buyer 124 may add a set of products within the same category (e.g., cars) that he or she is interested in purchasing. In still another example, the buyer 124 may set the product category, a description related to the product, or a product to exclude when entering information about the product to be purchased.
[Krasadakis: 0056] The buyers 124 may search for abstract product categories (e.g., car, phone, guitar, etc.) or down to specific product instances (e.g., a Tesla model S, Samsung Galaxy Note 5, Lumia 950™, Fender American Select Stratocaster, Gibson 1978, etc.) through a web page or software application. The buyers 124 may create and submit buying plans for particular products that include any of the aforementioned buying parameters and elasticity.
obtaining, by the one or more processor, buyer preference data; and
[Krasadakis: 0004] … The buyer AI negotiation agent is aware of the buyer's overall budget, needs, preferences and buying patterns etc. which can utilize to achieve better deals for the buyer.
[Krasadakis: 0016] … Users are anonymously represented online by their respective AI (buyer or seller) negotiator, which is able to understand what the user wants (through the pre-defined buying and selling parameters) and what sort of negotiation elasticity either side will accept
inputting, by the one or more processors, the vehicle type and the buyer preference data into a generative artificial intelligence (AI) model, wherein the generative AI model is trained on transactional data associated with vehicle purchases to learn a relationship between vehicle purchase terms and buyer characteristics, and is configured to:
Rejection is based in part on the teachings applied to claim 1 by Krasadakis and further upon the combination of Krasadakis-Awoyemi.
In Krasadakis see at least:
[Krasadakis: 0004] Some examples are directed to a framework in which artificial intelligence (AI) negotiation agents (otherwise known as robots or “bots”) are used to identify products or services and negotiate offer terms for buyers and sellers.
[Krasadakis: 0055] Additionally, the database cluster 204 stores user profile data 222 that includes unique identifiers of the buyers 123 (e.g., IDs, emails, account numbers, globally unique identifiers (GUIDs), demographics, gender, location, etc.), offers and purchasing history, negotiated offers and finalized deal terms of the users' buyer AI negotiators 206, and the like. For example, the user profile data 222 may be stored for a buyer 124 that indicates the buyer 124 has historically purchased products with premium product features (e.g., luxury vehicle options) at premium prices (e.g., more than a threshold amount than other buyers 124 purchasing the same luxury vehicle). In some example, offer and accepted deal terms between the buyers 124 and the sellers 174 are stored in relation to the buyers 124, sellers 174, or both as the user profile data 222, and this stored user profile data 222 may be exposed to buyer AI negotiators 206 and/or seller AI negotiators 208 to enhance product negotiations. Please note: User profile data 222 stores buyer characteristics e.g. purchase history, accepted deal terms, and seller transactional sales data, e.g. vehicle make/model, pricing and negotiated deal term.
[Krasadakis: 0074] A check may be performed to determine whether the buyer 124 specified elasticity thresholds or boundaries that the buyer AI negotiator 206 may adhere to when negotiating deal terms with the seller AI negotiators 208, as shown at decision box 308. For example, the buyer 124 may indicate particular product specifications that are necessary, unnecessary, or more/less important than other buying parameters, may indicate a particular delivery timeframe for receiving the product, may indicate pricing limits or percentages that are dependent on the market place (e.g., no more than 10% more than the average price of the last 50 purchases of a product from one or more sellers 174), and the like. If the buyer 124 specified elasticity thresholds, such limits are applied to the buying parameters by the buyer AI negotiator 124, as shown by the YES path from decision box 308 and 312. Please note: Buyer preferences are factored in the negotiations.
[Krasadakis: 0075] If the buyer does not specify buyer elasticity thresholds, as shown by the NO path from decision box 308, the buyer elasticity may be determined, in some examples, from market conditions. To do so, the buyer AI negotiator 206 may use the market intelligence component 210 to access the product data 212, pricing data 214, trends and social data 216, supply and demand data 218, industry news 220, and/or user profile data 222 on the database cluster 204 and determine standard, average, dependent, conditional, or relative elasticity thresholds for the buying parameters based on product sales (historical or prospective) or different market or buyer characteristics.
[Krasadakis: 0068] … 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. Please note: Year/make indicate a type of vehicle.
[Krasadakis: 0124] Machine learning, natural language processing, data mining, statistical modeling, predictive modeling, deep learning, neural networks, and game theory components may be implemented on the server-side components discussed herein. Some or all of these will be used to handle the multiple inputs. For instance, entities may be extracted from unstructured data, used to predict prices, used for certain optimization tasks, used to enable the AI negotiation agents disclosed herein to learn etc. Please note: Output created by the AI-powered model(s) is the result of generative AI consistent with description of generative AI in the instant specification. A negotiated purchase agreement is an example of content created by the machine learning model.
Although Krasadakis’ AI-powered framework uses autonomous AI agents to negotiate deals on behalf of buyers and sellers using historic vehicle sales data and buyer characteristics, e.g. negotiated terms, Krasadakis does not expressly mention training machine learning models using seller and buyer data. Awoyemi on the other hand would have taught Krasadakis such techniques.
In Awoyemi see at least:
[Awoyemi: 0021] The data associated with the set of contractual documents may include data identifying one or more terms within a contractual document (e.g., a key term, another term, etc.), data identifying whether a contractual document was accepted by a user, data identifying a date at which a contractual document was accepted, and/or the like. A key term, as used herein, may refer to a term that is material to an offer made within a contractual document, a name of a seller, a product or service identifier, a price of a product or a service, a problematic key term or phrase (e.g., a penalty clause, such as a liquidated damages clause), and/or the like. The user data may include user profile data identifying a user (e.g., a name, contact information, an address, etc.), transactional data identifying past transactions made by the user (e.g., data identifying past contractual documents that the user has accepted, rejected, etc.), preferences data identifying one or more user preferences (e.g., purchasing preferences, product or service preferences, brand preferences, etc.), financial data of the user (e.g., credit information, bank information, etc.), and/or the like. The vendor data may include data identifying a vendor, data identifying one or more products or services offered by a vendor, price data identifying one or more prices offered by a vendor for a product or a service, and/or the like.
[Awoyemi: 0022] As an example, the contractual documents may include sales contracts, such as contracts directed to sale of vehicles. In this example, the historical data may include data associated with a set of vehicle purchasing agreements, user data for users that were purchasers or offerees for offers made in the set of vehicle purchasing agreements, vendor data for a set of car dealerships, and/or the like. The data associated with the set of vehicle purchasing agreements may include data identifying key terms within a vehicle purchasing agreement, data identifying whether an offer for a sale of a vehicle was accepted, and/or the like. The key terms may include a second key term identifying a seller, a third key term identifying a date on which the offer was made, a fourth key term identifying a date that the offer was accepted, a set of key terms relating to vehicle information for a vehicle (e.g., a year, a make, a model, a current mileage, and/or the like), a set of key terms relating to purchasing information (e.g., a sales price, a down payment amount, an annual percentage rate (ARP) associated with a loan for the vehicle, a term of years for the loan, and/or the like), and/or the like.
[Awoyemi: 0025] In some implementations, the document management platform may identify key terms within the historical data. For example, the document management platform may identify key terms by analyzing the historical data included in the documents using one or more term identification techniques, such as a term matching technique, one or more natural language processing techniques, a machine learning technique, and/or the like, as each described below. This may allow the document management platform to use the identified key terms when identifying features that may be used when training the data model.
[Awoyemi: 0039] As shown by reference number 115, the document management platform may train the data model. For example, the document management platform may train the data model on the historical data, the set of features, and and/or the like, using one or more machine learning techniques. This may allow the data model to be trained to analyze particular inputs (e.g., a contractual document, key terms of the contract document, user data, and/or the like) to generate a set of term scores that correspond to one or more likelihoods of particular key terms being favorable to a user. The one or more machine learning techniques may include a technique using a neural network (e.g., a Word2Vec technique, a convolutional neural network (CNN), and/or the like), a classification-based technique, a regression-based technique, and/or another type of machine learning technique.
[Awoyemi: 0103] As further shown in FIG. 4, process 400 may include generating, based on the set of term scores, a recommendation for the recipient of the offer, wherein the recommendation includes at least one of: an indication of whether to accept the offer, or one or more recommended modifications to one or more of the one or more key terms (block 450). For example, the document management platform (e.g., using computing resource 235, processor 320, memory 330, storage component 340, and/or the like) may generate, based on the set of term scores, a recommendation for the recipient of the offer, as described above. In some implementations, the recommendation may include an indication of whether to accept the offer, and/or one or more recommended modifications to one or more of the one or more key terms.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Awoyemi, which a) train a machine learning model based on historical vehicle purchase agreement, and b) make recommendations to a recipient of an offer based upon key terms favorable to the recipient, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Awoyemi 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 data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
identify one or more sellers of the vehicle type,
Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0027] In operation, some examples include executable instructions that cause the buyer AI negotiation agents to locate seller AI negotiation agents and identify a list of the most optimal potential product offerings and consumers searching for products based on analysis of the parameter rankings and elasticity. Again, in some examples, the rankings may indicate which parameters the buyer believes are more important (e.g., price, delivery, specifications, availability, etc.), and the negotiations between the buyer and seller AI negotiation agents may be conducted based on such weightings.
[Krasadakis: 0028] 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.
[Krasadakis: Fig. 3 (306); 0073] The buyer AI negotiation agent 206 searches for and locates seller AI negotiation agents 208 offering the product specified by the buyer in the buying plan, as shown at 306.
analyze the buyer preference data to generate a set of bounds;
[Krasadakis: 0019] … For instance, if at the start of the time window, a few seller AI negotiators achieve unexpectedly high profit margins for product A, then other AI negotiators for the same or other products, may increase the elasticity regarding profit margins because the shared goal is most likely going to be met. In this manner, multiple seller AI negotiators may work together on shared goals of the seller. Please note: Bounds can be set by the AI negotiator(s).
[Krasadakis: 0021] For purposes of this disclosure, “elasticity” refers to a difference in at least one of the buying or selling parameters, and an “elasticity threshold” refers to an upper or lower limit for a buying parameter. For example, a buyer may be willing to pay a price for a product that ranges ten percent (e.g., $100-$110). Or a seller may be willing to match whatever the lowest price (or a certain percentage, such as 5%, above the lowest price) of the going price for a particular product in the marketplace. Price is not the only parameter that may be elastic. Any of the disclosed buying and selling parameters may be elastic to some extent. Elasticity may be set by the buyer and seller themselves, by the current market conditions, by product availability, by the seasonality or trends of products, by social media or online commentary, by product reviews, or a combination thereof or by the overall performance and completion rate in reference to the strategic goals and objectives. For instance, buyers and sellers may specify particular elasticity ranges for their respective AI buyer and seller agents.
[Krasadakis: 0025] Also, the buyer and seller AI negotiation agents are described herein as “negotiating” with each other, which perhaps personifies the agents to some extent. It should be noted, however, that in some examples the buyer and seller AI negotiation agents operate autonomously from their respective buyers and retailers in the negotiation of purchase deals for products. In some examples, AI agent negotiation is accomplished by the seller AI negotiation agents respectively taking the predefined buyer and seller parameters and elasticity and identifying buyer-retailer pairings that facilitate the best product transactions. For example, a multitude of seller AI negotiation agents offering the desired products being searched for by an AI negotiation agent within elasticity thresholds of the various of buyer parameters (e.g., within 10% of price, having 90% of the desired product specifications, with an availability within hours or a day of the buyer's request, being mentioned a certain number of times in social media, etc.) cause some of the disclosed examples to create a list of retailer product offerings to present to the buyer, or may automatically purchase the products from one of the sellers in view of the buyer's preset authorization. Please note: Buyer preferences are used to set bounds by AI.
[Krasadakis: 0046] For example, the buyer negotiation parameters 116 may include a collection of specifications that the buyer 124 initially requested, but through negotiations with all available AI seller agents at a particular time, only a subset of specifications are available in for-sale products and the difference between what the buyer requested and the reality of product offerings exceeds a preset buyer negotiation elasticity 118. Consequently, constraints placed by the buyer negotiation elasticity 118 may be relaxed (thereby increasing the elasticity) and brought in line with the reality of the AI seller agents, … Please note: Buyer negotiation boundaries can be changed by the AI negotiator.
[Krasadakis: 0057] … Alternatively, the buyer and/or seller elasticity may be set or adjusted based on market conditions instead of being preset by the buyer and seller, respectively.
[Krasadakis: 0072] … The goal-sharing AI negotiators 206 and 208 may be configured to adjust elasticity thresholds for particular negotiations based on achievement of the shared buying and selling goals.
establish a communication coupling with the one or more sellers of the vehicle type,
Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0015] As a general introduction, the AI negotiators broadcast what the buyers and sellers are looking to purchase and sell, respectively, and in some examples, this is how initial matches between the buyer and seller AI negotiator bots are made.
[Krasadakis: 0040] The network 126 may include any computer network, for example the Internet, a private network, local area network (LAN), wide area network (WAN), or the like. The network 126 may include various network interfaces, adapters, modems, and other networking devices for communicatively connecting the client devices 100, the application server 202, and the external song database cluster 204 referenced in FIG. 2. The network 126 may also include configurations for point-to-point connections.
[Krasadakis: 0050] Some examples implement the AI negotiators discussed herein in a cloud-based scenario. In such examples, the client devices 100 of the buyers 124 and the sellers 174 broadcast their respective buying plans and selling plans to servers that, in turn, create and manage the AI negotiators on behalf of the buyers and sellers.
automatically negotiate, with the one or more sellers, one or more terms within the set bounds for purchasing available vehicles of the vehicle type; and
Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0025] Also, the buyer and seller AI negotiation agents are described herein as “negotiating” with each other, which perhaps personifies the agents to some extent. It should be noted, however, that in some examples the buyer and seller AI negotiation agents operate autonomously from their respective buyers and retailers in the negotiation of purchase deals for products. In some examples, AI agent negotiation is accomplished by the seller AI negotiation agents respectively taking the predefined buyer and seller parameters and elasticity and identifying buyer-retailer pairings that facilitate the best product transactions.
[Krasadakis: 0059] The buyer AI negotiator 206 intelligently, autonomously, and electronically represents the buyer 124 and negotiates deals with the seller AI negotiators 208 of various sellers 174 offering a particular product.
[Krasadakis: 0076] Regardless of whether user-defined or market-dictated, if elasticity thresholds are specified, the elasticity thresholds are used to set limits on the buying parameters controlling the buyer AI negotiator 206 (as shown at 312), and then deal offers are negotiated with the identified seller AI negotiators 208 (as shown 314).
present the one or more terms to the buyer to facilitate purchase of a particular vehicle of the available vehicles from a particular seller.
Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0077] Offer terms are being negotiated through a multi-stage negotiation procedure between the buyer AI negotiator 206 and the identified seller AI negotiators 208. Each next stage in the multi-stage procedure may also quantify the rate of improvement in comparison to the initial offer and using the weight factors defined by the buyer 124. Using the rate of improvement and also the time-box defined, the procedure may be configured or decide to terminate. Negotiated offer terms may then be transmitted to the buyer 124, in some example, as shown at 316.
Regarding claims 14 and 20: Rejections are based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi, see [Krasadakis: Figs. 1A, 1B, 2] for system computing elements, e.g. processors, memory, network, databases etc.
Regarding claims 4, 5, 17 and 18: Rejections are based upon the teachings and rationale applied to claims 1 and 14 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0065] The buyer AI negotiator may communicate potential offers meeting the buyer 124's buying plan back to the client device 100 of the buyer for display to the buyer 124. The buyer 124 may accept; archive; discard; adjust the buying plan being executed by the buyer AI negotiator; adjust the buyer AI negotiator 206 re-negotiate offer terms, such as a specific lower price added by a user; gather market, social or public information about the product; ask for another negotiation round with an optional special request (e.g., price, availability, etc.); or a combination thereof. If the buyer 124 decides to accept an offer, the AI buyer agent may execute the negotiated offer accordingly.
Regarding claim 8: Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi:
[Krasadakis: 0035] The I/O components 104 may include display, audio, haptic, and other presentation devices that visibly, audibly, or otherwise present information to the buyer 124. The I/O components 104 may include various presentation components and corresponding I/O ports and device drivers, including, for example but without limitation, display screens, monitors, touch screens, phone displays, tablet displays, wearable device screens, televisions, speakers, vibrating devices, tactile-morphing screens, headphones and headphone inputs, holographic displays, virtual reality displays, augmented reality displays, and any other devices configured to display, verbally communicate, or otherwise indicate output to a user. Additional presentation components readily apparent to one skilled in the art may also be included.
Regarding claim 9: Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi: see Krasadakis for AI/machine learning; and Awoyemi for chatbot.
Regarding claim 10: Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi regarding elasticity parameters and parameter thresholds:
[Krasadakis: 0074] A check may be performed to determine whether the buyer 124 specified elasticity thresholds or boundaries that the buyer AI negotiator 206 may adhere to when negotiating deal terms with the seller AI negotiators 208, as shown at decision box 308. For example, the buyer 124 may indicate particular product specifications that are necessary, unnecessary, or more/less important than other buying parameters, may indicate a particular delivery timeframe for receiving the product, may indicate pricing limits or percentages that are dependent on the market place (e.g., no more than 10% more than the average price of the last 50 purchases of a product from one or more sellers 174), and the like. If the buyer 124 specified elasticity thresholds, such limits are applied to the buying parameters by the buyer AI negotiator 124, as shown by the YES path from decision box 308 and 312.
Regarding claim 12: Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi regarding use of historical transaction data covered under claim 1.
Regarding claim 13: Rejection is based upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi regarding term ranking:
[Krasadakis: 0027] In operation, some examples include executable instructions that cause the buyer AI negotiation agents to locate seller AI negotiation agents and identify a list of the most optimal potential product offerings and consumers searching for products based on analysis of the parameter rankings and elasticity. Again, in some examples, the rankings may indicate which parameters the buyer believes are more important (e.g., price, delivery, specifications, availability, etc.), and the negotiations between the buyer and seller AI negotiation agents may be conducted based on such weightings. Please note: Price, delivery, specification, availability are purchase agreement terms.
[Aswoyemi: 0057] As an example, a price of a vehicle may be a key term and may have a value of $10,000. A term score may, in one example, be a value between one and ten, where one indicates a low degree of favorability to the user and ten indicates a high degree of favorability to the user. Please note: A scale between one and 10, 10 being a high degree of favorability conveys a ranking scale.
[Awoyemi: 0102] As further shown in FIG. 4, process 400 may include determining a set of term scores that correspond to one or more likelihoods of the one or more key terms being favorable to a recipient of the offer, wherein determining the set of term scores includes analyzing the set of key terms using a data model that has been trained using one or more machine learning techniques (block 440). For example, the document management platform (e.g., using computing resource 235, processor 320, memory 330, storage component 340, and/or the like) may determine a set of term scores that correspond to one or more likelihoods of the one or more key terms being favorable to a recipient of the offer, as described above. In some implementations, determining the set of term scores may include analyzing the set of key terms using a data model that has been trained using one or more machine learning techniques.
Claims 2, 3, 7, 15, 16 and 19 are rejected under 35 USC 103 as being unpatentable over Krasadakis, US 2017/0287038, and Awoyemi, US 2022/0044289, as applied to claims 1, 2, 14 and 15 further in view of Dagley et al., US 2020/0334694 “Dagley.”
Regarding claims 2, 3, 15 and 16: Rejections are based in part upon the teachings and rationale applied to claims 1, 2, 14 and 15 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi-Dagley. Although Krasadakis-Awoyemi autonomously negotiate vehicle purchase agreements between a buyer and seller, and access loan data histories that factor trade-in values, Krasadakis-Awoyemi do not expressly mention including trade-in value of the buyer’s current vehicle. Dagley on the other hand would have taught Krasadakis-Awoyemi such techniques.
In Dagley see at least:
[Dagley: 0020] Accordingly, in addition to supplying the behavioral analytics platform with information relating to a vehicle inventory that can be searched by the user device, the behavioral analytics platform may have visibility into various values that the dealer device submits to the entity that offers vehicle financing to customers of the vehicle dealer during negotiations. For example, in some implementations, a customer may obtain dealer-arranged financing through a process in which information about the customer (e.g., name, address, social security number, income, debts, and/or the like) is entered into the dealer device and sent to a prospective lender associated with the behavioral analytics platform. In such cases, the information sent to the prospective lender may further include an offered price, a down payment value, a trade-in value (if applicable), values for one or more add-on products, miscellaneous fees, and/or the like, which can be used to determine a total value to be financed.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Dagley, which factor buyer trade-in value with loan requests, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Dagley to the teachings of Krasadakis-Awoyemi 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 data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claims 7 and 19: Rejections are based upon the teachings and rationale applied to the combination of Krasadakis-Awoyemi-Dagley regarding loan requests.
Claim 6 is rejected under 35 USC 103 as being unpatentable over Krasadakis, US 2017/0287038, and Awoyemi, US 2022/0044289, as applied to claim 1 further in view of Wang et al., US 2022/0222688 “Wang.”
Rejection is based in part upon the teachings and rationale applied to claim 1 by Krasadakis-Awoyemi and further upon the combination of Krasadakis-Awoyemi-Wang. Although Krasadakis-Awoyemi mention delivery dates, Krasadakis-Awoyemi do not expressly mention automatically scheduling a meeting between the buyer and seller. Wang on the other hand would have taught Krasadakis-Awoyemi such techniques.
In Wang see at least:
[Wang: 0072] At block 518, in some embodiments, the server 140 may adjust the predicted probability of the user action. Such adjustments may be made when additional probability predictions based upon additional user interactions with the vehicle dealer are available. For example, a user may send multiple e-mails to a vehicle dealer, each of which may be separately analyzed. Continuing the example, the user may then place a phone call to the vehicle dealer to confirm information, check that the vehicle is still available, check vehicle dealer hours, or schedule an appointment to meet with a vehicle dealer representative.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Wang, which teach scheduling an appointment to meet with vehicle dealer representative, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Wang to the teachings of Krasadakis-Awoyemi 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 data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2023/0030309 (Christeas et al.) “Vehicle Price Negotiation. Application and Agent,” discloses: [Abstract] A novel cloud-based system is provided comprising at least one or more servers and databases for negotiating a price among competitive sellers for consumer articles such as automobiles whereby a user is provided access to a consumer article price negotiation system and negotiation application such that the price negotiation is conducted by and through a negotiation assistant (i.e., an artificial intelligence agent) that exchanges communications and negotiates, on behalf of but independent from the user, with a variety of vehicle dealerships desiring to fulfill the sale request.
Joshi et al., PTO-892 Item U "Location Identification, Extraction and Disambiguation using Machine Learning in Legal Contracts," discloses: [Page 1] We are building the legal AI technology to support the activities of corporate lawyers engaged in due diligence, risk analysis, litigation, and real estate analysis processes. Typically, corporate lawyers can process large numbers of documents using our efficient analysis technology. Identifying important information such as party location or contract jurisdiction is a time consuming and costly process. Due to recent advancement of NLP (Natural language processing) techniques in conjunction with Artificial intelligence and machine learning, these tasks can be automated. This allows lawyers to focus on high-level analysis, rather than spending time on repetitive data extraction.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 March 20, 2026