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 .
Status of the Claims
Claims 1, 6, 8, and 22 are amended. Claims 1-25 are pending.
Response to Arguments
Applicant's arguments filed 06/01/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
The Claims Recite An Abstract Idea Under Step 2A Prong One
Applicant argues that the claims are not directed to certain methods of organizing human activity nor mental processes. Examiner disagrees.
Step 2A Prong One of the Alice/Mayo framework evaluates whether an abstract idea is set forth or described in the claim. The Federal Circuit has explained that "the 'directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. Here, it is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. Applicant’s specification [009] discloses that “…subscription payments are fixed price payments and may be somewhat inequitable considering that different users have different needs and preferences or provide varying levels of detail and complexity to their queries or inputs. This way, a user seeking more
straightforward information is charged the same as a person asking a more complex or personalized question. Further, according to reports from Al service providers, there is a huge demand for Al services which in many scenarios exceeds the available hardware resources.” [010] provides that “ It is an objective of the present invention to provide improved methods for integrated control systems for providing responses to user queries. In particular, there is a need to distribute available hardware resources appropriately to improve available service, e.g. to enhance responsiveness.” The cited portions of the specification gives the solution to the problem the invention is attempting to solve, and the alleged improvement is an improvement in user experience, i.e., enhancing user responsiveness, etc. This alleged improvement is at best, an improvement in the judicial exception itself and not an improvement in computers or technology. In the applicant’s invention, AI or the large language models are leveraged or used on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. The invention and claims are drawn to providing responses relating to prices in response to user queries (using LLMs) and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions; commercial interactions business relations) as evidenced by limitations detailing the user providing a query and receiving responses associated with the query and generating and transmitting pricing indications. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) considering that the claims involve the observation and evaluation of data and the response being generated based on the observed and evaluated data. The claims recite an abstract idea.
The Judicial Exception Is Not Integrated Into A Practical Application Under Step 2A Prong Two
The applicant argues that the claims are directed to “a practical application that solves the technical problem of providing users with advance price information for queries directed to large language models in advance of and prior to executing the queries” (See Remarks, pg. 10). Examiner disagrees. Providing users with advance price information for queries directed to large language models in advance of and prior to executing the queries is not a “technical” improvement, but at best an improvement in the judicial exception itself. . It is important to keep in mind that an improvement in the judicial exception itself is not an improvement in technology (emphasis added). For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology. Providing the users with advance price information directly corresponds to commercial interactions, managing personal interactions, and is a fundamental economic practice. This is processing data and providing the data to users. Technical improvement focuses on enhancing the tools, software, or machinery, while business process improvement focuses on streamlining the steps, workflows, and methodologies people use to do their work. Applicant’s claims fall in the latter. The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: an integrated control system comprising a processor (claim 1), a memory (claim 1), a communication interface and network (claim 1), at least one database (claim 1), and a first large language model. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the large language model also amount to generally linking the judicial exception to particular field of use (generating responses to user queries, said responses involving pricing indications). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Applicant’s argument that the functions performed cannot be practically performed by a human in a timely manner considering the volume and intricacies of queries processed by large language models is unpersuasive. Applicant relies on the assertion that a human cannot physically perform the claim operations as implemented on a computer. The mental-steps inquiry under Step 2A Prong One isn’t “could a human do this at the same speed/precision.” It’s whether the claim limitation, under BRI, covers performance in the mind (observation, evaluation, judgment, opinion) but for the recitation of generic computer components. MPEP §2106.04(a)(2)(III) recites that Claims can recite a mental process even if they are claimed as being performed on a computer. If the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept, the claim is considered to recite a mental process. Further, regarding certain methods of organizing human activity, the sub-groupings encompass both activity of a single person and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping (MPEP §2106.04(a)(2)(II)). It is the user that is providing the query, the computer (including the large language model) is processing the queries and transmitting the price information to the user, the user is then giving the confirmation. The large language model on the computer is merely being leveraged to process the natural language prompt. “Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015); see also MPEP 2106.05(f).
The 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments with respect to claim(s) 1-5 and 22-25 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see pg. 12, filed 06/01/2026, with respect to claims 6-21 have been fully considered. The 35 U.S.C. 103 rejection of claims 6-21 has been withdrawn.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-5 and 22-25 recite a system (i.e. machine), claims 6-21 recite a method (i.e. process). Therefore claims 1-25 fall within one of the four statutory categories of invention.
Claims 1 and 22 recite the limitations: receive a user query from a user, the user query requesting information about a specific topic to be provided in the form of one or more of text, an image, audio or video data; generate a pricing request comprising a natural-language prompt based on the user query and/or parameters associated with the user; transmit the pricing request to [a first large language model] and request [the large language model] to generate a price indication; receive the price indication generated by [the first large language model]; determine based on the price indication a response price; request a confirmation from the user to allocate the response price; and responsive to receiving the confirmation form the user, transmit a response associated with the user query to the user. The invention and claims are drawn to providing responses relating to prices in response to user queries (using LLMs) and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions; commercial interactions business relations) as evidenced by limitations detailing the user providing a query and receiving responses associated with the query and generating and transmitting pricing indications. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) considering that the claims involve the observation and evaluation of data and the response being generated based on the observed and evaluated data. The claims recite an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: an integrated control system comprising a processor (claim 1), a memory (claim 1), a communication interface and network (claim 1), at least one database (claim 1), and a first large language model. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the large language model also amount to generally linking the judicial exception to particular field of use (generating responses to user queries, said responses involving pricing indications). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to particular field of use (generating responses to user queries, said responses involving pricing indications). Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 2-5 and 23-25 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements (large language model(s)) that have been analyzed in the rejected claims above. Thus, claims 2-5 and 23-25 are also rejected under 35 U.S.C. 101.
Independent claim 6 recites the limitations: receiving a user query from a user, the user query requesting information about a specific topic to be provided in the form of one or more of text, an image, audio or video data; generating a pricing request comprising a natural-language prompt based on the user query and/or parameters attached to the user; transmitting the pricing request to [a first large language model], wherein [the integrated control system] requests [the first large language model] to generate a price indication; receiving the price indication generated by [the first large language model]; determining based on the price indication a response price; requesting confirmation from the user to allocate the response price; receiving a confirmation to allocate the response price; allocating the response price using [a payment system]; responsive to receiving the confirmation form the user, transmitting a response associated with the user query to the user. The invention and claims are drawn to providing responses relating to prices in response to user queries (using LLMs) and the claims recite limitations that directly correspond to certain methods of organizing human activity (managing personal interactions; commercial interactions business relations) as evidenced by limitations detailing the user providing a query and receiving responses associated with the query and generating and transmitting pricing indications. The claims also correspond to mental processes (observation, evaluation, judgment, opinion) considering that the claims involve the observation and evaluation of data and the response being generated based on the observed and evaluated data. The claims recite an abstract idea.
Note: the features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: an integrated control system, a payment system, and a first large language model. The additional elements are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the large language model also amounts to generally linking the judicial exception to particular field of use (generating responses to user queries, said responses involving pricing indications). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to particular field of use (generating responses to user queries, said responses involving pricing indications). Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claim 8 recites the limitation(s) that generating the pricing request comprises the steps of: using [a pricing model template]; and/or using a natural language processing mode; and/or using a feedback mechanism, the feedback mechanism comprising: receiving user feedback associated with the response and/or the response price; storing the user feedback in [a historian database]; analyzing the user feedback to identify most frequent user complaints; adapting the pricing request based on the analyzed user feedback. The claim limitations are further directed to the abstract idea analyzed above in Step 2A Prong One. The claim also recites the additional elements of a pricing model template and a historian database. The historian database amounts to “apply it” or merely using a computer [component] as a tool to implement the abstract idea. The pricing model template amounts to generally linking the judicial exception to a particular field of use (generating pricing requests). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 13 recites the limitation that the assigning and/or adjusting of weights is performed using a neural network. The claim limitation is further directed to the abstract idea analyzed above in Step 2A Prong One. The claim also recites the additional element of a neural network. The neural network amounts to “apply it” or merely using a computer [component] as a tool to implement the abstract idea, and generally linking the judicial exception to a particular field of use (adjusting weight in providing pricing requests/indications). Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 17 recites the limitations: sending a request to the user to watch [a video], determining whether the user has watched [the video], when the user has watched [the video]: reducing the response price by a specified amount and allocating the reduced price; and/or allocating a credit to the user. The claim limitations are further directed to the abstract idea analyzed above in Step 2A Prong One. The claim also recites the additional elements of the video which amounts to generally linking the judicial exception to a particular field of use (price reduction or incentives). Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 18 recites the limitation that [the payment system] comprises [a digital wallet] for allocating the credit. The claim limitation is further directed to the abstract idea analyzed above in Step 2A Prong One. The claim also recites the additional element of the payment system and digital wallet. The additional element of the payment system and digital wallet amounts to “apply it” or merely using a computer as a tool to implement the abstract idea, and generally linking the judicial exception to a particular field of use (allocating credit). Accordingly, in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 7, 9-12, 14-16, and 19-21 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements (large language model(s)) that have been analyzed in the rejected claims above. Thus, claims 7, 9-12, 14-16, and 19-21 are also rejected under 35 U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim(s) 1, 3-5, and 22-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Mukherjee (2024/0354436) further in view of Dakka (2012/0059732).
Claim 1: Maiman discloses: A system for providing response to a user query, the system comprising:
an integrated control system, said integrated control system including: a processor, a memory, a communication interface adapted to communicate with a network, and at least one database, wherein the memory, communication interface and at least one database are each in communication with the processor; and (Maiman ¶0022 disclosing computing device, and/or third party data server(s) in communication via a network; ¶0023 disclosing databases; ¶0025 disclosing one or more processor(s) for controlling overall operation of the search system and its associated components; a network interface, and memory; see also ¶0026 and ¶0027; see also Fig. 1)
wherein the integrated control system is adapted to: receive a user query from a user, the user query requesting information about a specific topic to be provided in the form of one or more of text, an image, audio or video data; (Maiman ¶0006 disclosing the computing device may, after training the machine learning model, receive a query from a user; ¶0044 the search system may receive a query from a client device; the query may include data in a media format, such as audio data, and the search system may convert the data into a textual form (e.g., using a text-to-speech algorithm to convert an audio query into textual search terms))
generate a pricing request comprising a natural-language prompt based on the user query and/or parameters attached to the user; (Maiman ¶0006 receive a query from a user, identifying one or more merchants matching the query, and generate inputs for the machine learning model based on the user and merchant data in order to generate a customized price indicator; ¶0063 in some cases (e.g., when the received query was a voice command (thus a natural language prompt)), the search system may format a search result to include textual information that may be used by a text to speech algorithm at the user device and/or may generate audio data at the search system; ¶0066 further disclosing a user may submit a voice query (e.g., by talking to a voice assistant) (natural language prompt), or may provide a query via any other type of application and/or input method)
Maiman in view of Mukherjee discloses:
transmit the pricing request to a first large language model as a request to the first large language model to generate a price indication;
Maiman discloses transmitting the pricing request as a request to the first large language model to generate a price indication, and discloses a machine-learning model that appears to process natural language: (Maiman discloses a machine learning model: (¶0020 information may be used to tailor the customized price rating (e.g., by providing the cost of predicted dish as input to the machine learning model) and any displayed search result (e.g., by displaying the cost of the predicted dish along with other information about the restaurant); ¶0031 the customized price rating machine learning model may be provided a training data set which trains the risk detection machine learning model to generate a customized price rating based on various inputs). Maiman does not explicitly refer to the machine learning model as a large language model. A large language model is a software tool capable of corpus-based linguistic analysis and prediction, particularly an artificial intelligence system that processes written instructions (prompts) and is capable of generating natural language text. Maiman teaches that the ML model can process natural language text as illustrated in Fig. 5 and as explained in ¶0066, and thus encompasses an LLM.). Maiman does not explicitly disclose or recite a large language model. Mukherjee suggests or discloses this limitation/concept: (Mukherjee ¶0065 user input or “Natural Language Input”) can be, for example, a term, phrase, question, and/or statement written in a human language (e.g., English, Chinese, Spanish, and/or the like), and/or other text string, that is provided by a user or on behalf of a user, such as via a keyboard, mouse, touchscreen, voice recognition, and/or other input device; user can include a request for data, such as data accessed and/or processed by one or more services, and can include one or more queries, one or more questions, one or more requests, or the like; ¶0072 devices to respond to a user input or a user query; computing environment includes the document search system, an LLM, an LLM; ¶0074 a user query from the user may be a natural language query; ¶0079 receiving a user input from the user, the document search system may generate and provide a prompt to a LLM, which may include one or more large language models trained to fulfill a modeling objective, such as question and answer, task completion, text generation, summarization, etc.). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the first large language model of the secondary reference(s) for the machine learning model of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Maiman, as modified above, discloses the following limitations:
receive the price indication generated by the first large language model; (Maiman ¶0006 disclosing the machine learning model outputting a customized price rating based on input data indicating at least one or more product costs for the particular merchant and a spending habit for a particular user; ¶0018 generating and displaying customized price ratings using machine learning techniques; see also ¶0019; ¶0020 displayed search result (e.g., by displaying the cost of the predicted dish along with other information about the restaurant); ¶0059) (Regarding the first language model, see above citations and rational to combine provided in the initial mentioning of the model)
determine based on the price indication a response price; (Maiman ¶0006 and Fig. 5 disclosing the price based on the price indication of “$$” or “$$$” rating of the particular user)
request a confirmation from the user to allocate the response price; and (Maiman ¶0065 disclosing the system generating a customized price rating of “$$” for a merchant, but the user may disagree with this rating and instead rate the merchant as “$$$”; search system may store the user rating and, in the future, use the user's personal rating when displaying information about the same merchant)
Maiman in view of Mukherjee further in view of Dakka discloses:
responsive to receiving the confirmation form the user, transmit a response associated with the user query to the user.
Maiman discloses display of the customized price indicator (response) associate with the user query, but does not appear to disclose responsive to receiving the confirmation form the user, transmit a response associated with the user query to the user. Dakka suggests or discloses this limitation/concept: (Dakka ¶0079 users are presented a user interface, the product request user interface includes a current purchase price, and the process optionally receives purchase data from at least one of the users; ¶0081 bids are received from users to whom the product request user interface was presented, each respective bid specifies a price that a respective user will pay (response price); ¶0083 using the bids and the inventory data, a final price is computed for the particular product (confirmation); ¶0067 product is allocates to users at the final price (response and confirmation); ¶0003 disclosing users may submit one or more search queries to a search engine in an effort to receive information about products that the user in interested in buying (thus the response in associated with the user query). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maiman in view of Mukherjee to include that responsive to receiving the confirmation form the user, transmit a response associated with the user query to the user as taught by Dakka 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 22: Claim 22 is directed to a system. Claim 22 recites limitations that are parallel in nature as those addressed above for claim 1, which is directed towards a system. Claim 22 is therefore rejected for the same reasons as set forth above for claim 1.
Claim 3: The system according to claim 1
wherein the integrated control system is adapted to receive the response from a second large language model adapted for generating the response indication.
Maiman discloses the use of a LLM to input and receive queries or prompts, but does not explicitly disclose that the integrated control system is adapted to receive the response from a second large language model adapted for generating the response indication. Mukherjee suggests or discloses this limitation/concept: (Mukherjee ¶0047 disclosing generating the prompt for the LLM; the system may summarize the conversation history using another LLM or using the LLM to which the prompt is to be transmitted). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maiman to include that the integrated control system is adapted to receive the response from a second large language model adapted for generating the response indication as taught by Mukherjee. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Maiman such that a size of the prompt generated by the system for the LLM does not exceed or overflow size limit on the prompt for the LLM (see ¶0047 of Mukherjee).
Claim 23: Claim 23 is directed to a system. Claim 23 recites limitations that are parallel in nature as those addressed above for claim 3, which is directed towards a system. Claim 23 is therefore rejected for the same reasons as set forth above for claim 3.
Claim 4: The system according to claim 3,
wherein the first and second large language models are different large language models.
Maiman discloses the use of a LLM to input and receive queries or prompts, but does not explicitly disclose that the first and second large language models are different large language models. Mukherjee suggests or discloses this limitation/concept: (Mukherjee ¶0047 disclosing generating the prompt for the LLM; the system may summarize the conversation history using another LLM (thus different) or using the LLM to which the prompt is to be transmitted). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maiman to include the first and second large language models are different large language models as taught by Mukherjee. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Maiman such that a size of the prompt generated by the system for the LLM does not exceed or overflow size limit on the prompt for the LLM (see ¶0047 of Mukherjee).
Claim 24: Claim 24 is directed to a system. Claim 24 recites limitations that are parallel in nature as those addressed above for claim 4, which is directed towards a system. Claim 24 is therefore rejected for the same reasons as set forth above for claim 4.
Claim 5: The system according to claim 3,
wherein the first and second large language models are the same large language models. (Maiman ¶0065 discloses a single (same) machine learning model)
Maiman discloses the use of the machine learning models that processes natural language being a single (same) machine learning model, but does not explicitly disclose the first and second large language models are the same large language models. Mukherjee suggests or discloses this limitation/concept: (Mukherjee ¶0107 disclosing employing a language model such as a LLM different from or the same as the LLM 130 to vectorize the natural language user query; ¶0072 devices to respond to a user input or a user query; computing environment includes the document search system, an LLM, an LLM; ¶0074 a user query from the user may be a natural language query; ¶0079 receiving a user input from the user, the document search system may generate and provide a prompt to a LLM, which may include one or more large language models trained to fulfill a modeling objective, such as question and answer, task completion, text generation, summarization, etc.). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the first large language model and second large language model of the secondary reference(s) for the machine learning model of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 25: Claim 25 is directed to a system. Claim 25 recites limitations that are parallel in nature as those addressed above for claim 5, which is directed towards a system. Claim 25 is therefore rejected for the same reasons as set forth above for claim 5.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Mukherjee (2024/0354436) further in view of Dakka (2012/0059732) further in view of Koppelman (20210019738 A1).
Claim 2: The system according to claim 1,
wherein the at least one database includes at least a historian database and a general database.
Maiman discloses a database, but does not explicitly disclose that the at least one database includes at least a historian database and a general database. Koppelman suggests or discloses this limitation/concept: (Koppelman ¶0023 disclosing the account database (historian) that includes proposal availability (e.g. the supply of offers in the market place), previous acceptance history, and the exchange database (general) current trends in the exchange network (e.g., a market place), etc.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maiman in view of Mukherjee further in view of Dakka to include that the at least one database includes at least a historian database and a general database as taught by Koppelman 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable Subject Matter
Claims 6-21 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference found that is relevant to the applicant’s invention includes Maiman (2023/0012164) which discloses a system that may allow for generating a customized price rating using a machine learning algorithm. The system improves the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users which is done by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating. The reference does not appear to explicitly disclose the detailed limitations of the applicant’s claim including the payment system. The claims appear to overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “NLSQL: Generating and Executing SQL Queries via Natural Language Using Large Language Models” (Attawar, et. al.; 2023) which discloses a system that makes use of LLMs like GPT-3 and shows how efficiently prompt engineering can be done in order to extract from LLMs the desired code for SQL queries. The NLSQL system shows that using pre-trained LLMs along with the suggested priming prompts is an accurate and reliable way to create and run SQL queries in natural language, even if the queries aren’t very well written or are missing important information. The reference does not appear to explicitly disclose the detailed limitations of the applicant’s claim including the payment system. The claims appear to overcome the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3629
Prosecution Insights