DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the correspondences filed on 11/19/2025.
Claims 1 and 11 have been amended and are hereby entered.
Claims 10 and 20 have been canceled.
Claims 1-9 and 11-19 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Objections
Claims 1, 9, 11, and 19 are objected to because of the following informalities:
Claim 1: line 20 and Claim 11: line 19 recite the limitation “generate/generating an optimization model.” “An optimization model” is previously recited in Claim 1: line 1 and Claim 11: line 1. Is the optimization model recited in Claim 1: line 20 and Claim 11: line 19 different than an optimization model recited in Claim 1: line 1 and Claim 11: line 1? Furthermore, in claims 1 and 11, the preamble recites “an apparatus for generating an optimization model” and “a method for generating an optimization model,” respectively. In Claim 1: line 22 and Claim 11: line 22, the “display” and “displaying” step, respectively, and dependent claims 7-9 and 17-19 refers to “the optimization model”. Here, the antecedence basis is unclear because there are two instances in both independent Claim 1: lines 1 and 20 and Claim 11: lines 1 and 19 that recite “an optimization model.” It appears there is a typographical mistake since the specification only points to a single optimization model. For examination purposes, Examiner interpreted the instances recited in Claim 1: line 20 and Claim 11: line 19 as “the optimization model.” Appropriate correction is required.
Claim 1: line 10 and Claim 11: lines 7-8 recite the limitation “receive user metrics related to a user” and “receiving, using at least a processor, user metrics related to a user,” respectively. “User metric(s)” is previously recited in Claim 1: line 6 and Claim 11: lines 3-4. Is the user metric(s) recited in Claim 1: line 10 and Claim 11: lines 7-8 different than a user metric/user metrics recited in Claim 1: line 6 and Claim 11: lines 3-4? It appears there is a typographical mistake since the specification only points to a single instance of user metric(s). For compact examination purposes, Examiner interpreted the instances recited in Claim 1: line 10 and Claim 11: lines 7-8 as “receive the user metrics related to a user” and “receiving, using at least a processor, the user metrics related to a user,” respectively. Appropriate correction is required.
Claim 1: lines 26-27, Claim 9: line 8, Claim 11: lines 24-25, and Claim 19: line 8 recite the limitation “a function of the user metrics.” “A function of the user metric” is previously recited in Independent Claim 1: lines 7-8 and Independent Claim 11: lines 4-5. Is the function of the user metrics recited in Claim 1: lines 26-27, Claim 9: line 8, Claim 11: lines 24-25, and Claim 19: line 8 different than a function of the user metric recited in Independent Claim 1: lines 7-8 and Independent Claim 11: lines 4-5? It appears there is a typographical mistake since the specification only points to a single instance of function of the user metric(s). For compact examination purposes, Examiner interpreted the instances recited in Claim 1: lines 26-27, Claim 9: line 8, Claim 11: lines 24-25, and Claim 19: line 8 as “the function of the user metrics.” Appropriate correction is required.
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 1-9 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing user metrics and transaction data to determine efficiency scores of contingent transactions without significantly more.
Examiner has identified claim 1 as the claim that represents the claimed invention presented in independent claims 1 and 11.
Claim 1 is directed to an apparatus, which is one of the statutory categories of invention; and Claim 11 is directed to a method, which is one of the statutory categories of invention (Step 1: YES).
Claim 1 is directed to an apparatus for generating an optimization model, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query; classify the user metrics to a plurality of sets of protocol parameters related to a plurality of contingent transactions using a classifier trained with training data, wherein: the training data comprises correlations between exemplary user metrics and exemplary protocol parameters; and a plurality of data elements of the training data is classified to a plurality of types of the user metrics using a training data classifier, wherein the types of the user metrics comprise transaction types; determine an efficiency score of each of the contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion; select a first contingent transaction of the plurality of the contingent transactions as a function of the efficiency score, wherein the first contingent transaction comprises a first set of protocol parameters of the plurality of parameters; generate an optimization model of the first contingent transaction as a function of the user metrics and the first set of protocol parameters; display the optimization model on the graphical user interface using an executable data structure; and generate a report for the user as a function of the executable data structure; and display the report on the graphical user interface. These series of steps describe the abstract idea of processing user metrics and transaction data to determine efficiency scores of contingent transactions, (with the exception of the italicized and bolded terms above), which is mitigating risk by processing an efficiency score for contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion by determining the eligibility and level of risk associated with a potential client before assigning a client to an advisor; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Similar arguments can be extended to the other independent claim, claim 11; and hence claim 11 is rejected on similar grounds as claim 1.
Dependent claims 2-9 and 12-19 are directed to an apparatus and a method, respectively, which recites a series of steps that describe the abstract idea of processing user metrics and transaction data to determine efficiency scores of contingent transactions. Furthermore, dependent claims 5, 8-9, 15, and 18-19 are directed to an apparatus and a method, which recites the steps: “wherein determining the efficiency score comprises: generating scoring training data, wherein the scoring training data comprises correlations between protocol parameters inputs and efficiency score outputs; training a scoring machine-learning model using the scoring training data; and determining the efficiency score using the trained scoring machine-learning model; wherein the optimization model comprises a pecuniary data structure; an optimization machine-learning model; and wherein generating the optimization model comprises: receiving optimization training data comprising a plurality of user metrics inputs and first set of protocol parameter inputs correlated to a plurality of optimization model outputs; training an optimization machine-learning model using the optimization training data; and generating the optimization model as a function of the user metrics and the first set of protocol parameters.” These series of steps describe the abstract idea of processing user metrics and transaction data to determine efficiency scores of contingent transactions (with the exception of the italicized and bolded terms above), which is mitigating risk by processing an efficiency score for contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion by determining the eligibility and level of risk associated with a potential client before assigning a client to an advisor; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-9 and 12-19 are directed to an abstract idea. The additional elements of an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, executable data structure, scoring machine-learning model, trained scoring machine-learning model, pecuniary data structure, and optimization machine-learning model are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, executable data structure, scoring machine-learning model, trained scoring machine-learning model, pecuniary data structure, and optimization machine-learning model, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-9 and 12-19 have further defined the abstract idea that is present in their respective independent claims: Claims 1 and 11; and thus correspond to Certain Methods of Organizing Human Activity, and are abstract in nature for the reason presented above. The dependent claims 2-9 and 12-19 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 2-9 and 12-19 are directed to an abstract idea without significantly more.
Thus, claims 1-9 and 11-19 are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Palakodety (U.S. Patent Application Publication No. US 2023/0269090 A1 hereinafter “Palakodety”), in view of Bireley (U.S. Patent Application Publication No. US 2023/0360121 A1; hereinafter “Bireley”), and further in view of Sharma (U.S. Patent Application Publication No. US 2022/0100740 A1; hereinafter “Sharma”).
Claims 1 and 11:
Palakodety teaches:
An apparatus for generating an optimization model, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: (Palakodety, An apparatus for secure multiparty computations for machine-learning is presented. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to submit a secure multiparty computation request onto an immutable sequential listing (See, Para. Abstract); Joint training protocol 156 may incorporate stacking multiple machine-learning models to produce joint model 168. A “joint model,” as used in this disclosure, is an optimized predictive model trained by each party member of secure multiparty computation request 104. (See, Para. 17 and 43));
receive user metrics related to a user using a graphical user interface and [the generated query]; (Palakodety, the secure multiparty computation request includes a contingent payment and an authenticity commitment of a first private dataset, receive at least a participant commitment from each participating device of a quorum of participating devices, generate a first localized model as a function of the first private dataset (See, Abstract; Para. 39); input device 932 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface (See, Para. 52, 107-109; Fig. 1); An example of a blockchain is the BITCOIN blockchain used to record BITCOIN transactions and values. (See, Para. 68));
classify the user metrics to a plurality of sets of protocol parameters related to a plurality of contingent transactions using a classifier trained with training data, wherein: the training data comprises correlations between exemplary user metrics and exemplary protocol parameters; and (Palakodety, a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 804 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 804 used by machine-learning module 800 may correlate any input data as described in this disclosure to any output data (See, Para. 45, 86-96; Fig. 1 and 8); At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804. (See, Para. 94-96; Fig. 8); An example of a blockchain is the BITCOIN blockchain used to record BITCOIN transactions and values. (See, Para. 68); verify if the final result represented by training protocol datum is not biased and/or unfairly skewed by a participating device's data; classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data….determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples. (See, Para.85-93));
a plurality of data elements of the training data is classified to a plurality of types of the user metrics using a training data classifier, wherein the types of the user metrics comprise transaction types; (Palakodety, verify if the final result represented by training protocol datum is not biased and/or unfairly skewed by a participating device's data; classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data….determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples. (See, Para. 45, 86-96; Fig. 1 and 8); An example of a blockchain is the BITCOIN blockchain used to record BITCOIN transactions and values. (See, Para. 4, 18, 55, 68));
determine an efficiency score of each of the contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion; (Palakodety, At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804.(See, Abstract; Para.94-96; Fig. 8));
select a first contingent transaction of the plurality of the contingent transactions as a function of the efficiency score, wherein the first contingent transaction comprises a first set of protocol parameters of the plurality of parameters; (Palakodety, A “confirmation commitment,” as used in this disclosure, is a cryptographic commitment to be posted onto immutable sequential listing 144 that proves that the private dataset and/or localized model of a participating device achieves a minimum level of accuracy as denoted by accuracy threshold 172; For instance, even if a successful multiparty computation is achieved, if one or more participating devices behaved inappropriately by not participating with integrity and/or providing manipulated data, the multiparty transaction of contingent payment 108 may be revoked in which each participating device must forfeit any outstanding contingent payment 108 and surety bond 136. This process may be governed by a smart contract embedded in secure multiparty computation request 104. In another non-limiting example, in the event each participating device and computing device 104 behaved appropriately, each participating device submits confirmation commitment 176 and deploys a proof of performance onto immutable sequential listing 144, enabling a multiparty transaction of contingent payment 108. Thus, contingent payment 108 may be fulfilled and distributed among each participating device. This process may also be governed by a smart contract embedded in secure multiparty computation request 104 (See, Abstract; Para. 33, 34, 44, 45, 54, 55, 75, and 76; Fig. 1, 8); verification may include comparing a product, such as without limitation, a private dataset, joint training datum 164, and/or joint model 168 against one or more acceptance criteria. For example, in some cases, joint model 168 may be required to fall within accuracy threshold 172. Ensuring that joint model 168 is in compliance with acceptance criteria may, in some cases, constitute verification. In some cases, verification may include ensuring that the private dataset used by the issuer of secure multiparty computation 100 achieves a minimum accuracy level comparable to the private datasets provided by the quorum of participating devices. At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs (See, Para. 45, 94-96; Fig. 1 and 8); In another non-limiting example, contingent payment 108 may include an up-front initial payment of 50% of the payment amount to be given to the participating parties wherein the remaining payment amount is given as a result of the completion of secure multiparty computation request 104 (See, Para. 31-36; Fig. 1));
generate an optimization model of the first contingent transaction as a function of the user metrics and the first set of protocol parameters. (Palakodety, the apparatus submits a cryptographic commitment to an immutable sequential listing with a smart contract associated with a contingent payment. The immutable sequential listing may be public, at least in part, for any institution to view and participate in. The smart contract governs the incentive and the contingent payment to be provided to participating party members. Aspects of the present disclosure can also include submitting a smart contract governing the MPC protocol based on receiving a cryptographic commitment of a quorum of party members. In an embodiment, the apparatus incorporates a surety bond attached to the smart contract to ensure each party member of the quorum is liable for their individual participation in the MPC protocol and that upon the success of the MPC protocol, the contingent payment will be fulfilled. (See, Para. 18, 30, 33, 45, and 55; Fig. 1, 8; Abstract)).
Palakodety does not specifically teach displaying the optimization model on the graphical user interface using an executable data structure; and generating a report for the user as a function of the executable data structure; and display the report on the graphical user interface.
However, Bireley teaches the following limitations:
display the optimization model on the graphical user interface using an executable data structure; and (Bireley, The systems and methods can include an interactive graphical user interface (GUI) that provides one or more automated recommendations (e.g., in a report) based on the analysis scoring model. A user can approve or decline one or more recommendations presented in the GUI by entering instructions through the GUI; The model training can also create and adjust the weighting applied to various model variables, which represent respective category tags, such that the resulting scoring model 140 includes a machine learning scoring model optimized to achieve (e.g., predict) the desired loan outcome. (See, Abstract; Para. 25-27, 40-41, 67, 72, 81-82, 91));
generate a report for the user as a function of the executable data structure; and display the report on the graphical user interface. (Bireley, The systems and methods can include an interactive graphical user interface (GUI) that provides one or more automated recommendations (e.g., in a report) based on the analysis scoring model. A user can approve or decline one or more recommendations presented in the GUI by entering instructions through the GUI; The model training can also create and adjust the weighting applied to various model variables, which represent respective category tags, such that the resulting scoring model 140 includes a machine learning scoring model optimized to achieve (e.g., predict) the desired loan outcome. (See, Abstract; Para. 25-27, 40-41, 67, 72, 81-82, 91)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Palakodety with the features of Bireley’s system because this system “include[s] instructions programmed to apply rules to the feature data and provide a first hierarchical decision. The first hierarchical decision can be based on execution of a rule set, which may be customized to align with underwriting criteria used by the lender. For instance, the first hierarchical decision can represent an automatic denial for the borrower or enable further consideration and processing based on the scoring model. The scoring model can be applied to process the transaction and feature data and compute a score having a value representing the credit worthiness and/or likelihood of loan default by the borrower. The score can provide a second hierarchical decision, which can be a recommendation to deny or approve the borrower or flag the borrower for further review by the user (e.g., an agent). The systems and methods can include an interactive graphical user interface (GUI) that provides one or more automated recommendations (e.g., in a report) based on the analysis scoring model. A user can approve or decline one or more recommendations presented in the GUI by entering instructions through the GUI.” (Bireley, Para. 25).
Palakodety and Bireley do not specifically teach generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; and the generated query.
However, Sharma teaches the following limitations:
generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; (Sharma, receive a metric request descriptor from a client device, the metric request descriptor including at least information for creating a metric query and an identifier of an electronic table. …. automatically generating a custom metric query based on the information for creating the metric query and one or more predefined metric templates …..the instructions cause the processor to automatically generate a custom metric query based on the information for creating the metric query and one or more predefined metric templates and create an electronic data table based on the identifier of the electronic (See, Abstract; Para. 5-6, 53, 77-78, 84; Fig. 1, 5));
and the generated query. (Sharma, receive a metric request descriptor from a client device, the metric request descriptor including at least information for creating a metric query and an identifier of an electronic table. …. automatically generating a custom metric query based on the information for creating the metric query and one or more predefined metric templates ….. the instructions cause the processor to automatically generate a custom metric query based on the information for creating the metric query and one or more predefined metric templates and create an electronic data table based on the identifier of the electronic (See, Abstract; Para. 5-6, 53, 77-78, 84; Fig. 1, 5)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Palakodety and Bireley with the features of Sharma’s system because “these data metrics and/or experiments are created using complex database queries, which extract relevant data from event logs and transform the extracted data for further analysis. Further, there is usually no central location to manage or view the already created metrics. Because of this, developers may create duplicate metrics, which initiate duplicate scheduled data extractions and data transformation, unnecessarily wasting precious network and storage resources.” (Sharma, Para. 4).
Claims 2 and 12:
Palakodety teaches:
classifying the user metrics comprises determining causative and predictive links between protocol parameters of the plurality of sets of protocol parameters and a transaction history using a machine learning process, wherein the user metrics comprises the transaction history. (Palakodety, a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 804 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 804 used by machine-learning module 800 may correlate any input data as described in this disclosure to any output data (See, Para. 45, 86-96; Fig. 1 and 8); At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804. (See, Para. 94-96; Fig. 8); An example of a blockchain is the BITCOIN blockchain used to record BITCOIN transactions and values. (See, Para. 68); verify if the final result represented by training protocol datum is not biased and/or unfairly skewed by a participating device's data; classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data….determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples. (See, Para.85-93)).
Claims 3 and 13:
Palakodety teaches:
wherein selecting the first contingent transaction comprises comparing the efficiency score to a predetermined threshold to accept or eliminate the first set of protocol parameters. (Palakodety, A “confirmation commitment,” as used in this disclosure, is a cryptographic commitment to be posted onto immutable sequential listing 144 that proves that the private dataset and/or localized model of a participating device achieves a minimum level of accuracy as denoted by accuracy threshold 172; For instance, even if a successful multiparty computation is achieved, if one or more participating devices behaved inappropriately by not participating with integrity and/or providing manipulated data, the multiparty transaction of contingent payment 108 may be revoked in which each participating device must forfeit any outstanding contingent payment 108 and surety bond 136. This process may be governed by a smart contract embedded in secure multiparty computation request 104. In another non-limiting example, in the event each participating device and computing device 104 behaved appropriately, each participating device submits confirmation commitment 176 and deploys a proof of performance onto immutable sequential listing 144, enabling a multiparty transaction of contingent payment 108. Thus, contingent payment 108 may be fulfilled and distributed among each participating device. This process may also be governed by a smart contract embedded in secure multiparty computation request 104 (See, Abstract; Para. 33, 34, 44, 45, 54, 55, 75, and 76; Fig. 1, 8); verification may include comparing a product, such as without limitation, a private dataset, joint training datum 164, and/or joint model 168 against one or more acceptance criteria. For example, in some cases, joint model 168 may be required to fall within accuracy threshold 172. Ensuring that joint model 168 is in compliance with acceptance criteria may, in some cases, constitute verification. In some cases, verification may include ensuring that the private dataset used by the issuer of secure multiparty computation 100 achieves a minimum accuracy level comparable to the private datasets provided by the quorum of participating devices. At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs (See, Para. 45, 94-96; Fig. 1 and 8); In another non-limiting example, contingent payment 108 may include an up-front initial payment of 50% of the payment amount to be given to the participating parties wherein the remaining payment amount is given as a result of the completion of secure multiparty computation request 104 (See, Para. 31-36; Fig. 1)).
Claims 4 and 14:
Palakodety teaches:
wherein the memory contains instructions further configuring the at least a processor to display a color coded efficiency score to indicate a relationship between the predetermined threshold and the efficiency score. (Palakodety, A “confirmation commitment,” as used in this disclosure, is a cryptographic commitment to be posted onto immutable sequential listing 144 that proves that the private dataset and/or localized model of a participating device achieves a minimum level of accuracy as denoted by accuracy threshold 172; For instance, even if a successful multiparty computation is achieved, if one or more participating devices behaved inappropriately by not participating with integrity and/or providing manipulated data, the multiparty transaction of contingent payment 108 may be revoked in which each participating device must forfeit any outstanding contingent payment 108 and surety bond 136. This process may be governed by a smart contract embedded in secure multiparty computation request 104. In another non-limiting example, in the event each participating device and computing device 104 behaved appropriately, each participating device submits confirmation commitment 176 and deploys a proof of performance onto immutable sequential listing 144, enabling a multiparty transaction of contingent payment 108. Thus, contingent payment 108 may be fulfilled and distributed among each participating device. This process may also be governed by a smart contract embedded in secure multiparty computation request 104 (See, Para.33, 34, 44, 45, 54, 55, 75, and 76; Fig. 1, 8)).
Claims 5 and 15:
Palakodety teaches:
wherein determining the efficiency score comprises: generating scoring training data, wherein the scoring training data comprises correlations between protocol parameters inputs and efficiency score outputs; (Palakodety, At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804.(See, Para.94-96; Fig. 8));
training a scoring machine-learning model using the scoring training data; and (Palakodety, At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804.(See, Para.94-96; Fig. 8));
determining the efficiency score using the trained scoring machine-learning model. (Palakodety, verification may include comparing a product, such as without limitation, a private dataset, joint training datum 164, and/or joint model 168 against one or more acceptance criteria. For example, in some cases, joint model 168 may be required to fall within accuracy threshold 172. Ensuring that joint model 168 is in compliance with acceptance criteria may, in some cases, constitute verification. In some cases, verification may include ensuring that the private dataset used by the issuer of secure multiparty computation 100 achieves a minimum accuracy level comparable to the private datasets provided by the quorum of participating devices. At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs (See, Para.45, 94-96; Fig. 1 and 8)).
Claims 6 and 16:
Palakodety teaches:
wherein determining the efficiency score further comprises: generating a weight for each of the plurality of sets of protocol parameters of the plurality of contingent transactions based on significance relative to the efficiency score; and iteratively training the scoring machine-learning model using the scoring training data and the weight. (Palakodety, a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 804 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 804 used by machine-learning module 800 may correlate any input data as described in this disclosure to any output data (See, Para. 45, 86-93; Fig. 1 and 8); At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804.(See, Para. 94-96; Fig. 8)).
Claims 7 and 17:
Palakodety teaches:
wherein the optimization model comprises one or more regulatory elements. (Palakodety, An apparatus for secure multiparty computations for machine-learning is presented……wherein the secure multiparty computation request includes a contingent payment and an authenticity commitment of a first private dataset, receive at least a participant commitment from each participating device of a quorum of participating devices, generate a first localized model as a function of the first private dataset, and perform a joint training protocol as a function of the first localized model and a second localized model from the quorum of participating devices, wherein the joint training protocol includes generating a joint training datum; A “joint model,” as used in this disclosure, is an optimized predictive model trained by each party member of secure multiparty computation request 104. (See, Abstract; Para.17, 43); the apparatus submits a cryptographic commitment to an immutable sequential listing with a smart contract associated with a contingent payment. The immutable sequential listing may be public, at least in part, for any institution to view and participate in. The smart contract governs the incentive and the contingent payment to be provided to participating party members. Aspects of the present disclosure can also include submitting a smart contract governing the MPC protocol based on receiving a cryptographic commitment of a quorum of party members. In an embodiment, the apparatus incorporates a surety bond attached to the smart contract to ensure each party member of the quorum is liable for their individual participation in the MPC protocol and that upon the success of the MPC protocol, the contingent payment will be fulfilled. (See, Para. 18, 30, 33, and 55; Fig. 1)).
Claims 8 and 18:
wherein the optimization model comprises a pecuniary data structure. (Palakodety, An apparatus for secure multiparty computations for machine-learning is presented……wherein the secure multiparty computation request includes a contingent payment and an authenticity commitment of a first private dataset, receive at least a participant commitment from each participating device of a quorum of participating devices, generate a first localized model as a function of the first private dataset, and perform a joint training protocol as a function of the first localized model and a second localized model from the quorum of participating devices, wherein the joint training protocol includes generating a joint training datum; A “joint model,” as used in this disclosure, is an optimized predictive model trained by each party member of secure multiparty computation request 104. (See, Abstract; Para.17, 43)).
Claims 9 and 19:
Palakodety teaches:
wherein generating the optimization model comprises: receiving optimization training data comprising a plurality of user metrics inputs and first set of protocol parameter inputs correlated to a plurality of optimization model outputs; training an optimization machine-learning model using the optimization training data; and generating the optimization model as a function of the user metrics and the first set of protocol parameters. (Palakodety, a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 804 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 804 used by machine-learning module 800 may correlate any input data as described in this disclosure to any output data (See, Para. 45, 86-96; Fig. 1 and 8); At least a supervised machine-learning process 828, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include any inputs as described above as inputs, any outputs as described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 804.(See, Para.94-96; Fig. 8)).
Response to Arguments
With respect to the objection of claims 10 and 20, the objections are withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 11/19/2025. However, the objection of claims 1 and 11 are not withdrawn because the noted typographical mistake was not appropriately corrected; and new claim objections have been given with regards to Claims 1, 9, 11, and 19. In view of the grounds for the claim objections presented above in this office action, appropriate correction is required.
With respect to the nonstatutory double patenting rejection of claims 1-20 over claims 1, 4-6, 11, 14-16 of U.S. Patent No. 12,073,461, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 11/19/2025. Specifically, the claims of the '461 Patent does not disclose the following amended limitations, as recited in independent claims 1 and 11: "generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query.” Hence, the nonstatutory double patenting rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 11/19/2025.
With respect to the 35 U.S.C. 112(d) rejection of claims 10 and 20, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 11/19/2025.
Applicant's arguments filed on 11/19/2025 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-20 under 35 U.S.C. 103, Applicant arguments are moot in view of new grounds of rejections presented above in this office action. Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C.103 rejection of claims 1-9 and 11-19.
With respect to the rejection of claims 1-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “Claim 1, as amended, includes limitations such as "generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query." Applicant respectfully submits that the amended claim language is not directed to a method of organizing human activity under Step 2A, Prong 2 of the 2019 Revised Patent Subject Matter Eligibility Guidance. The claim does not recite concepts such as fundamental economic practices, commercial interactions, or interpersonal relationships that typically fall within the enumerated "method of organizing human activity" category. Instead, the claim is directed to a technical improvement in the way computer systems generate and process queries to handle user metrics….Accordingly, the claim does not recite a method of organizing human activity, but instead provides a practical application of query generation and weighting within a computing environment that improves how user metrics are processed and searched…..As such, Applicant's claims do not recite a method of organizing human activity nor a mental process. Accordingly, Applicant submits that at least the limitations described in amended claim 1 are not directed to an abstract idea of a certain method of organizing human activity or mental process. "
Examiner respectfully disagrees.
Under Step 2A: Prong 1, Examiner respectfully notes that claim 1, as amended, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions; without significantly more. The series of steps recited in claim 1, as amended, describe the abstract idea of processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions, which is mitigating risk by processing an efficiency score for contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion by determining the eligibility and level of risk associated with a potential client before assigning a client to an advisor; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the system limitations, e.g., an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure, do not necessarily restrict the claim from reciting an abstract idea.
Furthermore, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, and as discussed in the Guidance on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations: “generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query; classify the user metrics to a plurality of sets of protocol parameters related to a plurality of contingent transactions using a classifier trained with training data, wherein: the training data comprises correlations between exemplary user metrics and exemplary protocol parameters; and a plurality of data elements of the training data is classified to a plurality of types of the user metrics using a training data classifier, wherein the types of the user metrics comprise transaction types; determine an efficiency score of each of the contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion; select a first contingent transaction of the plurality of the contingent transactions as a function of the efficiency score, wherein the first contingent transaction comprises a first set of protocol parameters of the plurality of parameters; generate an optimization model of the first contingent transaction as a function of the user metrics and the first set of protocol parameters; display the optimization model on the graphical user interface using an executable data structure; and generate a report for the user as a function of the executable data structure; and display the report on the graphical user interface” are making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong I and concludes that these arguments are not persuasive. For example, under Step 2A-Prong I, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Applicant argues that “this direct application of machine-based query generation and weighting to solve a technical problem in information retrieval aligns with the principles established in Example 47….. the present claims achieve improved data processing and retrieval by embedding weighting logic and keyword generation directly into the query generation process. This is a clear technical improvement over traditional methods that rely on static or manually constructed queries without weighting mechanisms tied to user metrics….. claim 1 as amended is not directed to an abstract idea. Therefore, pursuant to Step 2A of the § 101 analysis prescribed in Alice, claim 1 as amended recites patentable subject matter. Applicant respectfully requests that this rejection be withdrawn.”
Examiner respectfully disagrees.
Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply generating, searching, assigning, receiving, classifying, training ( processing), inputting, determining, selecting, displaying, reporting, and outputting data (i.e., user metrics, weighted values, protocol parameters, contingent transactions data, data elements, transaction types data, efficiency scores, report data, and etc.). The disclosed invention cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query; classify the user metrics to a plurality of sets of protocol parameters related to a plurality of contingent transactions using a classifier trained with training data, wherein: the training data comprises correlations between exemplary user metrics and exemplary protocol parameters; and a plurality of data elements of the training data is classified to a plurality of types of the user metrics using a training data classifier, wherein the types of the user metrics comprise transaction types; determine an efficiency score of each of the contingent transactions as a function of the plurality of protocol parameters and an efficiency criterion; select a first contingent transaction of the plurality of the contingent transactions as a function of the efficiency score, wherein the first contingent transaction comprises a first set of protocol parameters of the plurality of parameters; generate an optimization model of the first contingent transaction as a function of the user metrics and the first set of protocol parameters; display the optimization model on the graphical user interface using an executable data structure; and generate a report for the user as a function of the executable data structure; and display the report on the graphical user interface.” The recited features in the limitations do not result in computer functionality or technical improvement. Unlike the eligible claims of Examples 47 and 48 of the 2024 Updated Guidance on Subject Matter Eligibility, Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. Unlike the eligible claims of Examples 47 and 48 of the 2024 Updated Guidance on Subject Matter Eligibility, the recited features in the limitations, as amended, does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and do not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (user metrics, weighted values, protocol parameters, contingent transactions data, data elements, transaction types data, efficiency scores, report data, and etc.), and no technical solution or improvement has been disclosed.
Moreover, there is no technology/technical improvement as a result of implementing the abstract idea. Unlike the eligible claims of Examples 47 and 48 of the 2024 Updated Guidance on Subject Matter Eligibility, the recited limitations in the pending claims simply amount to the abstract idea of processing of transaction data of contingent transaction payment data to determine an efficiency score of each of the contingent transactions. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, unlike the eligible claims of Examples 47 and 48 of the 2024 Updated Guidance on Subject Matter Eligibility, Claims 1 and 11, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, processing, and outputting. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, these arguments are not persuasive. The recited steps in claim 1, as amended, are recited as being performed by an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure. The additional elements: an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Additionally, the claims, as amended, recites the additional elements: an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure, which are used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure” in the limitations of the claim 1 and 11, as amended, merely indicates a field of use or technological environment in which the judicial exception is performed. Specifically, the additional elements, as listed above, are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking the above listed additional elements is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. The claim, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Hence, claims 1-9 and 11-19, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Applicant argues that “claim 1 as amended recites: "generate a query configured to search for data related to a user metric, wherein the query is further configured to generate at least a keyword as a function of the user metric, wherein the query is further configured to assign a weighted value to at least an attribute of the user metric; receive user metrics related to a user using a graphical user interface and the generated query." …..The claimed invention instead leverages weighting logic and automated keyword generation tied directly to user metrics, thereby improving the accuracy and efficiency of information retrieval….. claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above….. Claims 2-10 and 12-20 depend, directly or indirectly, on claims 1 or 11 and thus recite all of the same elements as claim 1 and claim 11. Claims 10 and 20 have been canceled, thus the rejections are now moot. Applicant, therefore, submits that claims 2-9 and 12-19 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11.”
Examiner respectfully disagrees.
Under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment. Furthermore, as explained above with respect to Step 2A, Prong II, the additional elements: an optimization model, apparatus, processor, memory, graphical user interface, classifier, training data classifier, and executable data structure, are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-9 and 11-19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Orr (U.S. Patent Publication No. US-2007/0156555-A1) “Systems, methods and programs for determining optimal financial structures and risk exposures”
Mhlanga (U.S. Patent Publication No. US-2021/0398210-A1) “Systems and methods of transaction tracking and analysis for near real-time individualized credit scoring”
Hubard (U.S. Patent Publication No. US-2022/0122171-A1) “Client server system for financial scoring with cash transactions”
Patel (U.S. Patent Application Publication No. US-2021/0406740-A1) “Method and system for estimating relocation costs”
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693