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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an integrated control system in claims 6 and 22.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 8, claim 8 recites “analyzing the user feedback, e.g. to identify most frequent user complaints”. The phrase "for example" (“e.g.”) renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For examination purposes, the identifying step is merely serving as an example and is not part of the claimed invention.
Claim 9 is also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to dependency on the rejected claim above.
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.
Independent claims 1 and 22 recite the limitations: receive a user query from a user; generate a pricing request 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 confirmation from the user to allocate the response price; and 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 gen computer, and generally linking the judicial exception to particular field of use eric (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; generating a pricing request 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]; 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 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 gen computer, and generally linking the judicial exception to particular field of use eric (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, e.g. 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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Maiman (2023/0012164).
Claim 1: A system for providing response to a user query, the system comprising:
an integrated control system, said integrated control system comprising: 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; (Maiman ¶0006 disclosing the computing device may, after training the machine learning model, receive a query from a user)
generate a pricing request 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)
transmit the pricing request to a first large language model and request the large first language model to generate a price indication; (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.)
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)
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 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)
transmit a response associated with the user query to the user. (Maiman ¶0006 disclosing computing device may then cause display of the customized price indicator)
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 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) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) 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 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.
Claim(s) 3-5 and 23-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Mukherjee (2024/0354436).
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)
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) 6-8, 10-16, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Armes (US 8,271,384).
Claim 6: A computer-implemented method for an integrated control system for a providing response to a user query, the method comprising the steps of:
receiving a user query from a user by an integrated control system; (Maiman ¶0006 disclosing the computing device may, after training the machine learning model, receive a query from a user)
generating a pricing request 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)
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; (Maiman ¶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.)
receiving by the integrated control system 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)
determining 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)
requesting confirmation from the user to allocate the response price; receiving by the integrated control system a confirmation to allocate the response price; (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)
transmitting a response associated with the user query to the user. (Maiman ¶0006 disclosing computing device may then cause display of the customized price indicator)
Maiman in view of Armes discloses:
allocating the response price using a payment system;
Maiman discloses allocating a response price, but does not explicitly disclose allocating the response price using a payment system. Armes suggests or discloses this limitation/concept: (Armes Col. 2, Ln. 45-63 disclosing the customer interacting with the supplier in the system; payment criteria being provided which includes prices, transaction fees, a payment instrument selected by the user, an amount of the purchase, an availability of the payment systems; an optimal price for the item, 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 to include allocating the response price using a payment system as taught by Armes. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Maiman in order to enable entities to dynamically locate and transact with payment systems to facilitate processing payments (see Col. 1, Ln. 30-32 of Armes).
Claim 7: The method of claim 6,
wherein the request for confirmation to allocate the response price comprises the response price. (Maiman ¶0006 and Fig. 5 disclosing the price based on the price indication of “$$” or “$$$” rating of the particular user)
Claim 8: The method of claim 6, wherein 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; (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)
storing the user feedback in a historian database; (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; the user rating may be stored in the data associated with the user in database)
analyzing the user feedback, e.g. to identify most frequent user complaints; adapting the pricing request based on the analyzed user feedback. (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)
Claim 10: The method according to claim 6,
wherein at least one response performance indicator is provided to the first large language model, wherein the pricing request causes the first large language model to generate the price indication based on the at least one response performance indicator. (Maiman ¶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)
Claim 11: The method according to claim 6, wherein at least one user profile data item is provided to the first large language model as part of the pricing request, wherein the pricing request causes the first large language model to generate the price indication based on the at least one user profile data item. (Maiman ¶0007 the user data may be stored in a user profile which the computing device may retrieve to determine a spending habit for the user; ¶0047 system may retrieve and/or process user data from a user profile that is associated with the user identifier to obtain user data inputs for the machine learning model; system may process the transaction data to determine an average price spent by the user at restaurants, a standard deviation of the average price, a highest amount spent by the user; ¶0050 search system may filter the transaction data obtained from the user profile to generate more specific indication(s) of a spending habit that may be used as input to the machine learning model; ¶0056 system may tailer data based on the user spending habit data; search system may then determine the cost of the predicted item, which may be used as a merchant input along with or instead of a more general input (e.g., instead of an input indicating an average cost of every item on the menu); the transaction history may indicate that the user has certain preferences which may enable the search system to generate more tailored product cost inputs; see also ¶0057 the search system may use the inputs to generate a custom price rating)
Claim 12: The method according to claim 6, further comprising the steps of:
assigning and/or adjusting a weight for at least one response performance indicator by the integrated control system based on at least one user profile data item, and forwarding the weight to the first large language model for use generating the price indication. (Maiman ¶0033 disclosing the machine learning model training process, machine learning software may adjust the weights of each connection and/or node (parameter); ¶0058 machine learning model may generally map the one or more inputs to a given output indicating a customized price rating, and thus may be able to predict a customized price rating for every user-merchant pairing based on the inputs corresponding to the user and the inputs corresponding to the merchant; customized price rating may be adjusted accordingly)
Claim 13: The method according to claim 12,
wherein the assigning and/or adjusting of weights is performed using a neural network. (Maiman ¶0033 disclosing the machine learning model training process, machine learning software may adjust the weights of each connection and/or node (parameter); ¶0031 a neural network in used to implement the price rating; ¶0058 machine learning model may generally map the one or more inputs to a given output indicating a customized price rating, and thus may be able to predict a customized price rating for every user-merchant pairing based on the inputs corresponding to the user and the inputs corresponding to the merchant; customized price rating may be adjusted accordingly)
Claim 14: The method according to claim 6,
wherein determining the response price comprises the steps of: calculating a total amount based on the price indication, establishing a response price range using the price indication. (Maiman ¶0006 and Fig. 5 disclosing the price based on the price indication of “$$” or “$$$” rating of the particular user)
Claim 15: The method according to claim 6, wherein the step of allocating the response price comprises the steps of:
transmitting a request for allocation by the integrated control system to the payment system; receiving by the integrated control system a confirmation for the allocation from the payment system and/or from the user.
Maiman discloses allocating a response price, but does not explicitly disclose transmitting a request for allocation by the integrated control system to the payment system; receiving by the integrated control system a confirmation for the allocation from the payment system and/or from the user. Armes suggests or discloses this limitation/concept: (Armes Col. 2, Ln. 45-63 disclosing the customer interacting with the supplier in the system; payment criteria being provided which includes prices, transaction fees, a payment instrument selected by the user, an amount of the purchase, an availability of the payment systems; an optimal price for the item, etc.; Col. 3, Ln. 15-24 disclosing a transaction request with payment criteria, and processing the payment/transaction; Col. 3, Ln. 45-48 disclosing the payment system providing a means to track and report transaction history (confirmation)). 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 transmitting a request for allocation by the integrated control system to the payment system; receiving by the integrated control system a confirmation for the allocation from the payment system and/or from the user as taught by Armes. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Maiman in order to enable entities to dynamically locate and transact with payment systems to facilitate processing payments (see Col. 1, Ln. 30-32 of Armes).
Claim 16: The method according to claim 6, wherein the step of allocating the response price comprises the steps of:
transmitting from the payment system to the user a request to allocate the response price; receiving by the payment system confirmation from the user to allocate the response price; allocating by the payment system the response price.
Maiman discloses allocating a response price, but does not explicitly disclose transmitting from the payment system to the user a request to allocate the response price; receiving by the payment system confirmation from the user to allocate the response price; allocating by the payment system the response price. Armes suggests or discloses this limitation/concept: (Armes Col. 2, Ln. 45-63 disclosing the customer interacting with the supplier in the system; payment criteria being provided which includes prices, transaction fees, a payment instrument selected by the user, an amount of the purchase, an availability of the payment systems; an optimal price for the item, etc.; Col. 3, Ln. 15-24 disclosing a transaction request with payment criteria, and processing the payment/transaction; Col. 3, Ln. 45-48 disclosing the payment system providing a means to track and report transaction history (confirmation)). 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 transmitting from the payment system to the user a request to allocate the response price; receiving by the payment system confirmation from the user to allocate the response price; allocating by the payment system the response price as taught by Armes. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Maiman in order to enable entities to dynamically locate and transact with payment systems to facilitate processing payments (see Col. 1, Ln. 30-32 of Armes).
Claim 19: The method according to claim 6, further comprising the steps of: transmitting a request to a second large language model to generate a response to the user query after the response price has been allocated, wherein the second large language model is adapted for generating the response; and receiving from the second large language model a response to the user query. (Maiman ¶0065 disclosing the user may disagree with this rating and instead rate the merchant as “$$$.” the search system may store the user rating and, in the future, use the user's personal rating when displaying information about the same merchant; the user rating may be stored in the data associated with the user in database; this data may be used at step 305 to generate user data inputs for the machine learning model)
Claim 21: The method according to claim 19, wherein the first and second large language models are the same large language models. (Maiman ¶0065 discloses a single (same) machine learning model)
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Armes (US 8,271,384) further in view of Sandhu (20200143476).
Claim 9: The method of claim 8,
wherein the pricing model template comprises at least one placeholder, wherein the at least one placeholder is assigned at least one response performance indicator or at least one user profile data item.
Maiman discloses generating pricing responses, but does not explicitly disclose that the pricing model template comprises at least one placeholder, wherein the at least one placeholder is assigned at least one response performance indicator or at least one user profile data item. Sandhu suggests or discloses this limitation/concept: (Sandhu ¶1580 disclosing providers can execute custom templates and members can use system-defined buying pattern templates (or a mix of user-defined and system-defined templates) that will automatically respond to price quotes by modifying them and submitting such modifications to the quoting providers, according to the parameters detected). 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 Ames to include that the pricing model template comprises at least one placeholder, wherein the at least one placeholder is assigned at least one response performance indicator or at least one user profile data item as taught by Sandhu 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(s) 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maiman (2023/0012164) in view of Armes (US 8,271,384) further in view of Kalaboukis (US 10,853,775).
Claim 17: The method of according to claim 6, further comprising the steps of:
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.
Maiman discloses generating pricing responses, but does not explicitly disclose 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. Kalaboukis suggests or discloses this limitation/concept: (Kalaboukis Col. 18, Ln. 31-38 disclosing that in a case where product is to be dispensed via vending machine, the machine prompting the customer to view a video in order to receive the product at a discounted price; if it is determined that the customer views the video the vending machine applies the discount to the product; fee for the product may be charged directly to the customer's mobile wallet). 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 Armes to include sending a request to the user t