DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 04/28/2026.
• Claim 9 has been withdrawn.
• Claims 1-8 and 10-17 are currently pending and have been examined.
Election/Restriction
Applicant’s election without traverse of claims 1-8 and 10-17 in the reply filed 04/28/2026 is acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 02/13/2025 has been reviewed and considered.
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 .
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 1-8 and 10-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-8 are directed to a machine and claims 10-17 are directed to a process. Therefore, claims 1-8 and 10-17 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
using a buyer manager agent, select a buyer sub-agent of a plurality of buyer sub-agents;
using the selected buyer sub-agent, generate and send an initial communication to a seller manager agent;
using the seller manager agent, select a seller sub-agent of a plurality of seller sub-agents based on an conversation history, wherein the conversation history includes 1) the initial communication or 2) the initial communication and one or more responses generated by one of the plurality of buyer sub-agents or one of the plurality of seller sub-agents;
using the selected seller sub-agent and a seller model, generate and send a response to the buyer manager agent based on listing data associated with a seller and the conversation history;
using the buyer manager agent, select a buyer sub-agent of the plurality of buyer sub-agents based on the conversation history and listing data associated with a buyer;
using the selected buyer sub-agent and a buyer, generate and send a response to the seller manager agent based on the listing data associated with the buyer and the conversation history;
determine whether an conversation is terminated; and
in response to determining the conversation is terminated, determine whether an agreement is reached; and
in response determining the agreement is reached, convert unstructured conversation into a structured agreement.
The above limitations recite the concept of creating agreements pertaining to listings between buyers and sellers. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, creating an agreement between a seller and buyer is a sale activity. Independent claim 10 recites similar limitations as claim 1 and, as such, fall within the same identified grouping of abstract ideas. Accordingly, under Prong One of , Step 2A of the Alice/Mayo test, claims 1 and 10 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1 and 10 recite additional elements, such as a system, autonomous creation of agreements, an electronic processor, a buyer manager artificial intelligence (AI) agent, a buyer AI sub-agent, a plurality of buyer AI sub-agents, an initial electronic communication, a seller manager AI agent; a seller AI sub-agent, a plurality of seller AI sub-agents, an electronic conversation history, a seller large language model, buyer computer implemented large language model, an electronic conversation, and unstructured electronic conversation. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1 and 10 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1 and 10 merely recite a commonplace business method (i.e., creating agreements pertaining to listings between buyers and sellers) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1 and 10 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1 and 10 specifying that the abstract idea of creating agreements pertaining to listings between buyers and sellers is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1 and 10 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1 and 10 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1 and 10 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1 and 10, these claims recite additional elements, such as a system, autonomous creation of agreements, an electronic processor, a buyer manager artificial intelligence (AI) agent, a buyer AI sub-agent, a plurality of buyer AI sub-agents, an initial electronic communication, a seller manager AI agent; a seller AI sub-agent, a plurality of seller AI sub-agents, an electronic conversation history, a seller large language model, buyer computer implemented large language model, an electronic conversation, and unstructured electronic conversation. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1 and 10 are manual processes, e.g., receiving information, analyzing information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1 and 10 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of creating agreements pertaining to listings between buyers and sellers is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1and 10 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1 and 10 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-8 and 11-17, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-8 and 11-17 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Dependent claims 3-8 and 12-17 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 2 and 11 further identify the additional elements of electronic instructions and an electronic device. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-8 and 11-17 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1 and 10, dependent claims 2-8 and 11-17 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-8 and 10-17 are ineligible.
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, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-8, 10-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Murakhovs'ka et al. (US 20240428068 A1), hereinafter Murakhovs'ka, in view of Krasadakis (US 20170287038 A1), hereinafter Krasadakis.
In regards to claim 1, Murakhovs'ka discloses a system for autonomous creation of agreements, the system comprising (Murakhovs'ka: [abstract]; [0026]):
an electronic processor, the electronic processor configured to (Murakhovs'ka: [0053]):
using a buyer manager artificial intelligence (AI) agent, select a buyer AI sub-agent of a plurality of buyer AI sub-agents (Murakhovs’ka: [0046] – “action decision module may decide which module to use…e.g., from the…response generation module”; [0027] and Fig. 1B – “Shopper bot 114, may be…simulated by an LLM-based system…Each agent gets access to specific content elements that assist them in completing the task of conducting a conversation relating to purchasing a complex item. For example, such content elements include…shopping preferences 118 accessible to the Shopper bot 114”; [0041-0042] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202…response generation module 212 may prompt an LLM acting as Shopper bot 114 with (a) natural language instruction to act as a shopper seeking [PRODUCT] (e.g., a TV), (b) a list of currently revealed shopping preferences 206 or 210, and (c) the chat history 202, at every turn in the conversation, to generate the response 214”; [0058] – “CRS module 430 may further include knowledge search submodule 431 (e.g., similar to knowledge search in FIG. 3), product search submodule 432 (performing functionality similar to product search in FIG. 3), action decision submodule 433 (e.g., similar to 302 in FIG. 3) and response generation submodule 434”; [0040] – “If one or more items are recommended at 204, a list of currently revealed shopping preferences 206 are retrieved. Shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply. It will base this decision on the whole set of preferences (P)”; [0024] and Fig. 1A – “a simplified diagram illustrating a CRS framework 100 built on a Seller bot 112 and a Buyer/Shopper bot 114”; the examiner interprets the response generation module to be a buyer AI sub-agent, it is among other sub-agents like the retriever model);
using the selected buyer AI sub-agent, generate and send an initial electronic communication to a seller manager AI agent (Murakhovs’ka: [0026] – “the Seller bot 112 and the Shopper bot 114 may have a conversation that begins with a Shopper request”; [0042] – “response generation module 212 may prompt an LLM acting as Shopper bot 114 with (a) natural language instruction to act as a shopper seeking [PRODUCT] (e.g., a TV), (b) a list of currently revealed shopping preferences 206 or 210, and (c) the chat history 202, at every turn in the conversation, to generate the response 214”);
using the seller manager AI agent, select a seller AI sub-agent of a plurality of seller AI sub-agents based on an electronic conversation history, wherein the electronic conversation history includes 1) the initial electronic communication or 2) the initial electronic communication and one or more responses generated by one of the plurality of buyer AI sub-agents or one of the plurality of seller AI sub-agents (Murakhovs’ka: [0046] – “The action decision module may decide which module to use based on the current conversation history 202, e.g., from the knowledge search module 310, product search module 320 or response generation module 330. An LLM may be queried to make this choice and provide natural language instructions on when to use each of the available tools in the prompt”; [0051] – “Response Generation with external knowledge at module 330 may be based on chat history 202, action selected, query generated and retrieved results”);
using the selected seller AI sub-agent and a seller large language model, generate and send a response to the buyer manager AI agent based on listing data associated with a seller and the electronic conversation history (Murakhovs’ka: [0028-0029] – “Seller bot 112 may have access of a buying guide 115 and a product catalog 116 as knowledge documents, based on which to generate a response…the product catalog 116 may comprise a list of products that can be recommended to the Shopper 114. Each product entry comprises (1) a unique ID, (2) a product name, (3) a price, (4) a product description, and (5) a feature set”; [0046] – “The action decision module may decide which module to use based on the current conversation history 202, e.g., from the knowledge search module 310, product search module 320 or response generation module 330. An LLM may be queried to make this choice and provide natural language instructions on when to use each of the available tools in the prompt”; [0058] – “the CRS module 430 is configured to conduct a conversation with a user combining both educational and product recommendation objectives…may further include…product search submodule 432 (performing functionality similar to product search in FIG. 3)”; [0096] – “conduct a sales conversation relating to purchasing a complex item”);
using the buyer manager AI agent, select a buyer AI sub-agent of the plurality of buyer AI sub-agents based on the electronic conversation history and listing data associated with a buyer (Murakhovs’ka: [0040] – “Shopper bot 114 may generate responses in accordance with the provided set of preferences (P), comprising several question-answer pairs (q-a). For example, given the current conversation history 202 between Shopper bot and Seller bot, Shopper bot may determine whether Seller bot is recommending items, e.g., from a last utterance of Seller bot in the conversation history 202. If one or more items are recommended at 204, a list of currently revealed shopping preferences 206 are retrieved. Shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply”; [0041-0042] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202”); and
using the selected buyer AI sub-agent and a buyer computer implemented large language model, generate and send a response to the seller manager AI agent based on the listing data associated with the buyer and the electronic conversation history (Murakhovs’ka: [0040] – “Shopper bot 114 may generate responses in accordance with the provided set of preferences (P), comprising several question-answer pairs (q-a). For example, given the current conversation history 202 between Shopper bot and Seller bot, Shopper bot may determine whether Seller bot is recommending items, e.g., from a last utterance of Seller bot in the conversation history 202. If one or more items are recommended at 204, a list of currently revealed shopping preferences 206 are retrieved. Shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply. It will base this decision on the whole set of preferences (P) to ensure consistency”; [0041-0042] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202”; [0042] – “response generation module 212 may prompt an LLM acting as Shopper bot 114”);
determine whether an electronic conversation is terminated (Murakhovs’ka: [0026] – “the Seller bot 112 and the Shopper bot 114 may have a conversation that begins with a Shopper request and ends once the Seller bot 112 makes a product recommendation that the Shopper bot 114 accepts”; [0040] and Fig. 1B – “shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply. It will base this decision on the whole set of preferences (P)…Shopper bot may not accept an item that would not satisfy the whole set P”); and
determine whether an agreement is reached (Murakhovs’ka: [0040] and Fig. 1B – “shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply. It will base this decision on the whole set of preferences (P)…Shopper bot may not accept an item that would not satisfy the whole set P”).
Murakhovs’ka further discloses that the conversations are for the purposes of purchasing (Murakhovs’ka: [0096]), yet Murakhovs’ka does not explicitly disclose that determining an agreement is reached is in response to determining the electronic conversation is terminated, and in response determining the agreement is reached, convert unstructured electronic conversation into a structured agreement.
However, Krasadakis teaches an automated negotiation system (Krasadakis: [abstract]), including
that determining an agreement is reached is in response to determining the electronic conversation is terminated (Krasadakis: [0077] – “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. Alternatively, in examples where the buyer 124 provides the buyer AI negotiator 206 with autonomy to accept offers, purchasing decisions for the product may be made automatically by the buyer AI negotiator 206 without input from the buyer 124”); and
in response determining the agreement is reached, convert unstructured electronic conversation into a structured agreement (Krasadakis: [0094] and Fig. 6 – “When an offer is accepted by the buyer AI negotiator 206, the accepted offer may be registered as ‘under review’ or otherwise pending confirmation by the seller AI negotiator 208, as shown at 626. The seller 174 may then be informed of the offer acceptance, as shown at 628. The seller may be allowed to formally accept the offer, as shown at decision box 630. The seller AI negotiator 208 waits for the seller 174 to accept the offer, as shown by the NO path from decision box 630, until a timeframe expires (in some examples). 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”).
It would have been obvious to one of ordinary skill in the art to include in the automated conversation, as taught by Murakhovs’ka, the response to termination and structured agreement, as taught by Krasadakis, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Murakhovs’ka, to include the teachings of Krasadakis, in order to improve predictions of what users want (Krasadakis: [0002]).
In regards to claim 2, Murakhovs’ka/Krasadakis teaches the system of claim 1. Yet Murakhovs’ka does not explicitly disclose wherein the electronic processor is further configured to: send electronic instructions to an electronic device associated with the seller to automatically begin shipment of a good.
However, Krasadakis teaches a negotiation system (Krasadakis: [abstract]), including
wherein the electronic processor is further configured to: send electronic instructions to an electronic device associated with the seller to automatically begin shipment of a good (Krasadakis: [0017] – “buyer and seller parameters may be set or focused on…shipping information (e.g., when products may be delivered)”; [0068] – “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”; see also [0100]; the examiner notes “to automatically begin shipment of goods” is merely an intended use/result and is accordingly granted little to no patentable weight. Nonetheless, the limitation has been fully examined).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Krasadakis with Murakhovs’ka for the reasons identified above with respect to claim 1.
In regards to claim 3, Murakhovs’ka/Krasadakis teaches the system of claim 1. Murakhovs’ka further discloses wherein the electronic processor is further configured to: receive the listing data associated with the buyer; receive the listing data associated with the seller; and match the listing data associated with the buyer to the listing data associated with the seller (Murakhovs’ka: [0041] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202”; [0042] – “When the salesperson makes a recommendation, you'll see product details with ‘ACCEPT’ and ‘REJECT’ in the message. Please consider whether the product satisfies your assigned preferences. If the recommended product meets your needs, generate [ACCEPT] token in your response”; [0048] – “when a product search module 320 is determined, the module 320 may find relevant items to recommend to the Shopper, which comprises: 1) query generation 327, and 2) retrieval 324. Specifically, each product's information (i.e., title, description, price, and feature list) is embedded with sentence transformer embedding model and top 4 products are retrieved”; see also [0053]).
In regards to claim 4, Murakhovs’ka/Krasadakis teaches the system of claim 1. Murakhovs’ka further discloses wherein the received listing data associated with the buyer and the received listing data associated with the seller include natural language descriptions (Murakhovs’ka: [0042-0043] and Fig. 1B – “prompt an LLM acting as Shopper bot 114 with (a) natural language instruction to act as a shopper seeking [PRODUCT] (e.g., a TV)… learn about shopping preferences 118 through simulated dialogue 120, as simulating the behavior of a human buyer shown in FIG. 1B”; [0041] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202”; the examiner notes Fig. 1B displays natural language listing information for the customer and the seller).
In regards to claim 6, Murakhovs’ka/Krasadakis teaches the system of claim 1. Murakhovs’ka further discloses wherein the electronic processor is further configured to: create a buyer manager AI agent based on a first prompt, wherein the first prompt includes at least one selected from the group consisting of a list and description of available buyer AI sub-agents, guidance and examples of actions to take, response instructions and examples, and an electronic conversation history (Murakhovs’ka: [0042-0043] and Fig. 1B – “esponse generation module 212 may prompt an LLM acting as Shopper bot 114 with (a) natural language instruction to act as a shopper seeking [PRODUCT] (e.g., a TV), (b) a list of currently revealed shopping preferences 206 or 210, and (c) the chat history 202, at every turn in the conversation, to generate the response 214… For example, an example Shopper prompt for an LLM Shopper may take a similar form to:
You are shopping online for a {product}. You haven't done your research on this product and want to speak to a salesperson over chat to learn more and make an informed decision.
Follow these rules:
Chat with the salesperson to learn more about {product}. They will be acting as a product expert, helping you make an informed purchasing decision. They may ask you questions to narrow down your options and find a suitable product recommendation for you.
Use your assigned preferences and incorporate them in your response when appropriate, but do not reveal them to the salesperson right away or all at once. Only share a maximum of 1 assigned preference with the salesperson at a time.
Let the salesperson drive the conversation.
Ask questions when appropriate. Be curious and try to learn more about {product} before making your decision.
Be realistic and stay consistent in your responses.
When the salesperson makes a recommendation, you'll see product details with ‘ACCEPT’ and ‘REJECT’ in the message. Please consider whether the product satisfies your assigned preferences.
If the recommended product meets your needs, generate [ACCEPT] token in your response. For example, “[ACCEPT] Thanks, I'll take it!”. If the recommended product is not a good fit, let the salesperson know (e.g. “this is too expensive”)
If you're not sure about the recommended product, ask follow-up questions (e.g. “could you explain the benefit of this feature?”) Do not generate more than 1 response at a time”); and
create a seller manager AI agent based on a second prompt, wherein the second prompt includes at least one selected from the group consisting of a list and description of available seller AI sub-agents, guidance and examples of actions to take, response instructions and examples, and an electronic conversation history (Murakhovs’ka: [0093] – “the query and the one or more knowledge documents together with a second prompt instructing the second neural network to generate answers using at least one knowledge document as context information may be fed as input to the second neural network model. The second neural network comprises a retriever model (e.g., 320 in FIG. 3) that retrieves the at least one knowledge document as relevant to the query. The interactive simulation is caused by iteratively feeding the response as an input to the first neural network model to generate a next query”; [0051] – “two separate prompts may be written to respond to the shopper. Response Generation with external knowledge at module 330 may be based on chat history 202, action selected, query generated and retrieved results”).
In regards to claim 7, Murakhovs’ka/Krasadakis teaches the system of claim 1. Murakhovs’ka further discloses wherein the plurality of buyer AI sub- agents include a negotiator AI agent and a listing AI agent (Murakhovs’ka: [0046] – “action decision module may decide which module to use…e.g., from the…response generation module”; [0027] and Fig. 1B – “Shopper bot 114, may be…simulated by an LLM-based system…Each agent gets access to specific content elements that assist them in completing the task of conducting a conversation relating to purchasing a complex item. For example, such content elements include…shopping preferences 118 accessible to the Shopper bot 114”; [0041-0042] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202…response generation module 212 may prompt an LLM acting as Shopper bot 114 with (a) natural language instruction to act as a shopper seeking [PRODUCT] (e.g., a TV), (b) a list of currently revealed shopping preferences 206 or 210, and (c) the chat history 202, at every turn in the conversation, to generate the response 214”; [0058] – “CRS module 430 may further include knowledge search submodule 431 (e.g., similar to knowledge search in FIG. 3), product search submodule 432 (performing functionality similar to product search in FIG. 3), action decision submodule 433 (e.g., similar to 302 in FIG. 3) and response generation submodule 434”; [0040] – “If one or more items are recommended at 204, a list of currently revealed shopping preferences 206 are retrieved. Shopper bot is instructed to include [ACCEPT] or [REJECT] token in its reply. It will base this decision on the whole set of preferences (P)”; [0024] and Fig. 1A – “a simplified diagram illustrating a CRS framework 100 built on a Seller bot 112 and a Buyer/Shopper bot 114”).
In regards to claim 8, Murakhovs’ka/Krasadakis teaches the system of claim 1. Murakhovs’ka further discloses wherein the plurality of seller AI sub- agents include a negotiator AI agent and a listing AI agent (Murakhovs’ka: [0046] – “The action decision module may decide which module to use based on the current conversation history 202, e.g., from the knowledge search module 310, product search module 320 or response generation module 330. An LLM may be queried to make this choice and provide natural language instructions on when to use each of the available tools in the prompt”; [0051] – “Response Generation with external knowledge at module 330 may be based on chat history 202, action selected, query generated and retrieved results”).
In regards to claim 10, claim 10 is directed to a method. Claim 10 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The combined system of Murakhovs’ka/Krasadakis teaches the limitations of claim 1 as noted above. Murakhovs’ka further discloses a method for autonomous creation of agreements, the method comprising (Murakhovs’ka: [0056]). Claim 10 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 11-13 and 15-17, all the limitations in method claims 11-13 and 15-17 are closely parallel to the limitations of system claims 2-4 and 6-8 analyzed above and rejected on the same bases.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Murakhovs’ka, in view of Krasadakis, in view of Shah et al. (US 20260094603 A1), hereinafter Shah.
In regards to claim 5, Murakhovs’ka/Krasadakis teaches the system of claim 3. Murakhovs’ka further discloses wherein the electronic processor is configured to match the listing data associated with the buyer to the listing data associated with the seller using structured data filters, keyword-based matching, and embeddings of the listing data ([0048] – “when a product search module 320 is determined, the module 320 may find relevant items to recommend to the Shopper, which comprises: 1) query generation 327, and 2) retrieval 324. Specifically, each product's information (i.e., title, description, price, and feature list) is embedded with sentence transformer embedding model and top 4 products are retrieved based on the query embedding (obtained using the same model). The retrieved results may thus be concatenated and fed to response generation module 330”; [0041] – “a retriever model 208 may be used to retrieve relevant shopping preferences 210 that may be relevant to the conversation history 202”; [0042] – “When the salesperson makes a recommendation, you'll see product details with ‘ACCEPT’ and ‘REJECT’ in the message. Please consider whether the product satisfies your assigned preferences. If the recommended product meets your needs, generate [ACCEPT] token in your response”; [0048] – “when a product search module 320 is determined, the module 320 may find relevant items to recommend to the Shopper, which comprises: 1) query generation 327, and 2) retrieval 324. Specifically, each product's information (i.e., title, description, price, and feature list) is embedded with sentence transformer embedding model and top 4 products are retrieved”; see also [0053]).
Murakhovs’ka further discloses determining the top four products based on embeddings (Murakhovs’ka: [0048]), yet Murakhovs’ka/Krasadakis does not explicitly teach distance metrics between vector embeddings.
However, Shah teaches an conversation system (Shah: [abstract]), including
distance metrics between vector embeddings (Shah: [0024] – “a semantic model (referenced elsewhere herein as “topic-trained similarity model”) that has been trained to recognize relations between different topics. For example, the semantic model encodes different portions of a hierarchical ontology as different embeddings in a vector space in which spatial proximity between the embeddings is correlated with similarity between the associated terms”).
It would have been obvious to one of ordinary skill in the art to include in the automated conversation, as taught by Murakhovs’ka/Krasadakis, the distance, as taught by Shah, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art at the time of filing to modify Murakhovs’ka/Krasadakis, to include the teachings of Shah, in order to improve identification of related information (Shah: [0024]).
In regards to claim 14, all the limitations in method claim 14 are closely parallel to the limitations of system claim 5 analyzed above and rejected on the same bases.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL reference U teaches using chatbots to perform transactions. Commercial negotiations with vendors may be done via AI. A negotiation chatbot takes over and autonomously conducts the negotiation.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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