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
Claims 1-20 are pending of which claims 1, 10 and 13 are in independent form.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) generating embedding vectors using ML models.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claims 1 and 10, is directed to a method, which is a process.
Independent claims 13, is directed to a system, which comprises, one or more computers and one or more memory devices.
Independent All other claims depend on claims 1, 10 and 13. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 10, and 13:
With respect to step 2A, prong one (Judicial Exception), it is noted that the independent claims recite an abstract idea falling within the Mental Health grouping of abstract ideas. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claims recite the following limitations directed to an abstract idea:
“processing a set of training samples through a plurality of first machine learning models to generate embedded vectors” as described recites mathematical algorithm/processing for transforming input data into mathematical representations (embedded vector);
“wherein each of the plurality of first machine learning models generates an embedded vector for each training sample in the set of training samples”, as drafted recites generating mathematical optimization performed using mathematical algorithm/operations;
“training a second machine learning model by using the embedded vectors”, as drafted recites mathematical algorithm/optimization performed using the embedded vector representation;
“training the plurality of first machine learning models by using the second machine learning model”, as drafted recites mathematical algorithm/optimization and parameter adjustment of ML models based on the output of another ML model.
The claims are directed to: mathematical algorithm/concept because the involve mathematical calculations and mathematical relationships performed on numerical representations of data to generate and optimize ML models. Such mathematical operations fall within the mathematical concepts grouping of abstract ideas. Se MPEP 2016.04(a)(2).
With respect to step 2A, Prong Two (Particular Application), the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite the use of:
“processing a set of training samples” as drafted recites insignificant extra solution activity, including data gathering and supplying data to the recited mathematical operations. See MPEP 2016.05(g) (mere data gathering).
“a plurality of first machine learning models” as drafted recites generic computer implemented mathematical models that perform their ordinary function of generating mathematical models that perform their ordinary functions of generating mathematical representations of input data.
“a second machine learning model” as drafted recites another ML model performing its ordinary function of mathematical training and optimization. The claim does not recite any particular architecture, training mechanism, or technological implementation that improves computer functionality.
“embedded vector” as drafted recite mathematical representations of data that are used during execution of the abstract idea. The embedded vectors themselves form part of the recited mathematical algorithms/concepts and therefore do not integrate the judicial exception into a practical application.
“training the plurality of first machine learning models by using the second machine learning model” as drafted simply recites applying the mathematical concept to additional mathematical models and therefore recites the judicial exception itself. The limitation is result-oriented and does not specify any particular technical implementation or technological improvement.
The processor merely executes the abstract steps. The database is used in a routine storage and retrieval. The hashing is a conventional identifier technique. The list of hash values is a standard lookup/cache structure.
The claims do not:
Improve database architecture and memory organization;
Improve hashing algorithm;
Improve query execution at a technical level;
Introduce a new data structure/cache architecture/parser/ hash algorithm/database engine.
Reduce computational complexity in a technical way.
The additional elements identified above fail to integrate the abstract idea into a practical application because the additional elements, individually and in combination, amount to no more than: generic ML models performing conventional mathematical operations; insignificant extra solution activity such as supplying training samples for mathematical process; applying mathematical calculation to generate embedded vectors and update ML model parameters.
The claims do not:
Improve the functioning of a computer of processor;
Improve memory utilization, networking, storage, or another computer technology;
Improve the operation of ML hardware or distributed computing system;
Recite a particular technical implementation for generating embedded vector or training ML models; or
Provide a specific technological solution to technological problem.
Instead, the recited computer implementation merely serves as a tool for performing the mathematical concepts of generating embedded vectors and training ML models.
Therefore, judicial exception is not integrated into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims are merely generic processor, generic storage, conventional ML models. Courts have consistently helped such high-level information management operations are conventional.
The claims recite only functional, result oriented language (“detecting”, “propagating”, “transferring”,…), without specifying any technical mechanism for performing these operations in a non-conventional manner.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2 and 14 (Iterative ML Training),
The claim recites:
Repeatedly training the second ML model
Repeatedly training the plurality of first ML models
This merely refines: repeating the mathematical training process; performing additional optimization iteration. There are no changes to: how the iterative training improves computer functionality; how the repeated training is technically implemented. These fall under: Mathematical Algorithm (iterative mathematical optimization).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 3 and 15 (Mathematical Loss Function),
The claim recites:
Training second ML model using a loss function;
Determining whether embedded vectors correspond to the same training sample.
This merely refines: evaluating mathematical relationships between embedded vectors. There are no changes to: how the loss function improves computer functionality; how a particular technical solution has been implemented to improve the computer functionality. These fall under: Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 4 and 16 (Mathematical Aggregation),
The claim recites:
Generating outputs from multiple first ML models;
Aggregating the outputs using an aggregation function;
Producing an aggregate output.
This merely refines: combining mathematical outputs; mathematically processing multiple model results. There are no changes to: how the aggregation function improves computer technology; how aggregation in computationally implemented beyond generic processing. These fall under: Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 5-6 and 17-18 (Mathematical Evolution Parameters),
The claim recites:
Training based on accuracy parameters and a differentiation parameter (claims 5 and 17);
Calculating the accuracy parameter using an additional parameter (claims 6 and 18);
Evaluating individual model outputs and aggregated outputs (claims 6 and 18).
This merely refines: evaluating mathematical performance metrics; additional optimization calculation; selecting model based on mathematical criteria. There are no changes to: how the evaluation parameters improve computer technology; how the parameters improve ML architecture; how the calculation improve processor performance. These fall under: Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 7 and 19 (Types of Training Data),
The claim recites:
Training samples comprise images, software code, or text.
This merely refines: the type of data supplied to the training models. No technical mechanism is provided for: improving processing of images, software code, or tech; improving computer technology based on the selected data type. These fall under: Mathematical Algorithm; Field of Use, insignificant extra solution activity.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 8, 9, 11, 12, and 20 (Mathematical Prediction Type),
The claim recites:
Each first ML model performs classification (claims 8, 11 and 20);
Each first ML model performs regression (claims 9 and 12).
This merely refines: the mathematical prediction performed by ML models; mathematical output generated. No technical mechanism is provided for: how classification or regression improves computer functionality; how either prediction technique improves ML implementation. These fall under: Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3-5, 7 are rejected under 35 U.S.C. 103 as being unpatentable over HILL; Brian Lawrence et al. (US 20250200427 A1) [Hill] in view of SCHREIBER; Andre Maurice et al. (US 20240220856 A1) [Schreiber].
Regarding claims 1, and 13, Hill discloses, a method, comprising: processing a set of training samples through a plurality of first machine learning models to generate embedded vectors (first model processes first data and generates embeddings ¶ [0104], second model processes the second data and generates embeddings ¶ [0110], multiple embedding models process different data types into a shared embedding space ¶ [0004], [0042], [0097], first and second models are separate embedding models operating within the same embedding space ¶ [0163]), wherein each of the plurality of first machine learning models generates an embedded vector for each training sample in the set of training samples (first model generates the first embedding ¶ [0104], second model generates the second embedding ¶ [0110], first and second embedding generated for the corresponding data ¶ [0187], first and second models are separate embedding models operating within the same embedding space ¶ [0163]);
training a second machine learning model by using the embedded vectors (second model trained using contrastive learning based on first and second embeddings ¶ [0114], contrastive learning compares embeddings ¶ [0120], loss function applied using first and second embeddings ¶ [0121]. Also see ¶ [0158], [0165]); and
training the [plurality of] first machine learning models by using the second machine learning model (first model and second model share the same embedding space ¶ [0163], second models is trained using contrastive learning involving first and second embeddings [0165]).
However, Hill does not explicitly facilitate plurality of … machine learning models.
Schreiber discloses, plurality of first machine learning models (training multiple ML models based on computed similarities [Abstract], operations based on the first plurality of similarities and the second plurality of similarities to generate a first trained machine learning model corresponding to the first machine learning model and a second trained machine learning model corresponding to the second machine learning model ¶ [0006], operations based on the first plurality of similarities and the second plurality of similarities to generate a first trained machine learning model corresponding to the first machine learning model and a second trained machine learning model corresponding to the second machine learning model ¶ [0093], updating parameters of the first machine learning model and the second machine learning model based on a loss that increases the first plurality of similarities ¶ [0100], [0101]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Schreiber’s system would have allowed Hill to facilitate plurality of … machine learning models. The motivation to combine is apparent in the Hill’s reference, because there is a need to improve techniques for processing CAD data in different formats.
Regarding claims 3 and 15, the combination of Hill and Schreiber discloses, wherein the second machine learning model is trained by using a loss function that predicts whether different embedded vectors are generated from a same training sample (Hill: a loss function that (i) decreases as the second embedding is closer to the first embedding in a circumstance where the first embedding is associated with a shared identifier matching the first identifier, and (ii) increases as the second embedding is closer to the first embedding in a circumstance where the first embedding is associated with a second identifier that differs from the first identifier ¶ [0014]. Also see ¶ [0114], [0120], [0121], [0158], [0165]).
Regarding claims 4 and 16, the combination of Hill and Schreiber discloses, wherein training the plurality of first machine learning models comprises training a plurality of first machine learning models with an aggregation function, wherein each of the plurality of first machine learning models generates a first output from an input sample and the aggregation function aggregates the first outputs from the plurality of first machine learning models to generate an aggregated output for the input sample (Hill: aggregated dimension algorithm … a mean aggregation algorithm, a sum aggregation algorithm, a max value aggregation algorithm ¶ [0133], attention masks are combined to generate the final values for each dimension or may generate multiple data values that are aggregated to combine into a single value for a given dimension ¶ [0136], embeddings are averaged and/or otherwise combined ¶ [0122]).
Regarding claims 5, and 17, the combination of Hill and Schreiber discloses, wherein the plurality of first machine learning models are trained based on a first parameter that evaluates an accuracy of the plurality of the first machine learning models (Hill: improve efficiency and accuracy of the models that generate such embeddings, thus reducing the error associated with such models compared to the amount of training data entries needed to train such models, as well as the accuracy of downstream processes performed based on such embeddings. Additionally or alternatively, the techniques described herein improve the computational efficiency, storage-wise efficiency, and/or speed of configuring accurate machine learning models ¶ [0042], accurate embedding ensures that the downstream determinations and/or other processes are similarly accurate ¶ [0097], Such contrastive learning implementations leverage multiple data types to improve the accuracy of the learned embeddings for an entity corresponding to a particular identifier ¶ [0099], Additionally still, use of particular contrastive learning enables more accurate learnings during configuration of an embedding model to account for a plurality of data types. In this regard, embodiments of the present disclosure improve the accuracy of the learned representations based on the generated attention masks as well as providing improved explainability of such learned representations ¶ [0100]. Also see ¶ [0123], [0159]) and a second parameter that evaluates a differentiation result. (Hill: the contrastive learning based on: (i) a set of positive queries based on a first set of signal data corresponding to a shared identifier, or (ii) a set of negative queries associated with a first identifier based on a second set of signal data corresponding to a second identifier ¶ [0013], [0135], [0201]. a plurality of different embeddings, where the embedding space 400 is utilized to perform contrastive learning during training of at least one model ¶ [0115]. Loss functions ¶ [0114], [0120], [0158]. Examiner specifies that the first parameter is associated with accuracy and second parameter is associated with differentiation between embeddings).
Regarding claims 7, and 19, the combination of Hill and Schreiber discloses, wherein the plurality of first machine learning models are trained based on a first parameter that evaluates an accuracy of the plurality of the first machine learning models (Schreiber: training engine 122, mapping engine 124, and evaluation engine 126 could be used to determine similarities in designs that can be represented using hand-drawn sketches, two-dimensional (2D) drawings, images, three-dimensional (3D) models, meshes, boundary representations, point clouds, text-based descriptions, or other formats ¶ [0025]. Also see ¶ [0033], [0036]-[0037], [0049], [0077]).
Regarding claim 10, the combination of Hill and Schreiber discloses, a method, comprising: receiving an input sample (Hill: training data entries needed to train such models ¶ [0042], Models are configured to accurately determine portions of one or more input data that contribute to embeddings (e.g., learned representations) based on generated attention masks ¶ [0100], also see ¶ [0111], [0130], [0134]); and
processing the input sample by a plurality of first machine learning models to generate embedded vectors, wherein each of the plurality of first machine learning models generates a respective embedded vector (Hill: first model generates the first embedding ¶ [0104], second model generates the second embedding ¶ [0110], first and second embedding generated for the corresponding data ¶ [0187], first and second models are separate embedding models operating within the same embedding space ¶ [0163]), and
wherein [the plurality of first machine learning models] are trained based on a first parameter that evaluates an accuracy of the plurality of the first machine learning models (Hill: improve efficiency and accuracy of the models that generate such embeddings, thus reducing the error associated with such models compared to the amount of training data entries needed to train such models, as well as the accuracy of downstream processes performed based on such embeddings. Additionally or alternatively, the techniques described herein improve the computational efficiency, storage-wise efficiency, and/or speed of configuring accurate machine learning models ¶ [0042], accurate embedding ensures that the downstream determinations and/or other processes are similarly accurate ¶ [0097], Such contrastive learning implementations leverage multiple data types to improve the accuracy of the learned embeddings for an entity corresponding to a particular identifier ¶ [0099], Additionally still, use of particular contrastive learning enables more accurate learnings during configuration of an embedding model to account for a plurality of data types. In this regard, embodiments of the present disclosure improve the accuracy of the learned representations based on the generated attention masks as well as providing improved explainability of such learned representations ¶ [0100]. Also see ¶ [0123], [0159]) and a second parameter that evaluates a differentiation result. (Hill: the contrastive learning based on: (i) a set of positive queries based on a first set of signal data corresponding to a shared identifier, or (ii) a set of negative queries associated with a first identifier based on a second set of signal data corresponding to a second identifier ¶ [0013], [0135], [0201]. a plurality of different embeddings, where the embedding space 400 is utilized to perform contrastive learning during training of at least one model ¶ [0115]. Loss functions ¶ [0114], [0120], [0158]. Examiner specifies that the first parameter is associated with accuracy and second parameter is associated with differentiation between embeddings).
Schreiber discloses, plurality of first machine learning models (training multiple ML models based on computed similarities [Abstract], operations based on the first plurality of similarities and the second plurality of similarities to generate a first trained machine learning model corresponding to the first machine learning model and a second trained machine learning model corresponding to the second machine learning model ¶ [0006], operations based on the first plurality of similarities and the second plurality of similarities to generate a first trained machine learning model corresponding to the first machine learning model and a second trained machine learning model corresponding to the second machine learning model ¶ [0093], updating parameters of the first machine learning model and the second machine learning model based on a loss that increases the first plurality of similarities ¶ [0100], [0101]).
Claim(s) 2, 8, 9, 11, 12, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hill in view of Schreiber in view of Browder; Blake et al. (US 12314305 B1) [Browder].
Regarding claims 2 and 14, the combination of Hill and Schreiber teaches all the limitation of claims 1 and 13.
However, neither Hill nor Schreiber explicitly facilitates, repeating the processing, the training of the second machine learning model, and the training of the plurality of first machine learning models in an iterative operation.
Browder discloses, repeating the processing, the training of the second machine learning model, and the training of the plurality of first machine learning models in an iterative operation (retraining a second ML model; generating updated outputs from the retrained model [col. 1, ll. 24-col. 2, ll. 12], also see [col. 17, ll. 47-col. 19, ll. 13]. Continuing to refer to FIG. 3, any process of training, retraining, deployment, and/or instantiation of any machine-learning model and/or algorithm may be performed and/or repeated [col. 31, ll. 15-62], configured to iteratively retrain the second machine-learning model as a function of new embeddings [col. 34, ll. 38-56]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Browder’s system would have allowed Hill and Schreiber to facilitate repeating the processing, the training of the second machine learning model, and the training of the plurality of first machine learning models in an iterative operation. The motivation to combine is apparent in the Hill and Schreiber’s reference, because there is a need to improve efficiently processing and interpreting unstructured data
Regarding claims 8, 11 and 20, the combination of Hill, Schreiber and Browder discloses, wherein each of the plurality of first machine learning models is configured to classify each training sample (Browder: machine-learning process like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image features [col. 13, ll. 33-57], NLP model 128 may utilize at least a machine-learning algorithm to classify text from the plurality of datasets 112 into categories [col. 14, ll. 57-67], such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in 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 [col. 21, ll. 1-33]. Training data classifier 316 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 300 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 304. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers [col. 21, ll. 34-67], also see [col. 24, ll. 37-41]).
Regarding claims 9, and 12, the combination of Hill, Schreiber and Browder discloses, wherein each of the plurality of first machine learning models is configurated to generate a regression value based on each training sample (Browder: a linear regression model, generated using a linear regression algorithm [col. 27, ll. 41-67]. Still referring to FIG. 3, machine-learning module 300 may be designed and configured to create a machine-learning model 324 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization [col. 29, ll. 43-col. 30, ll. 40]).
Claim(s) 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hill in view of Schreiber in view of Nguyen; Christopher et al. (US 20230316105 A1) [Nguyen].
Regarding claim 6 and 18, the combination of Hill and Schreiber teaches all the limitation of claims 5 and 17.
However, neither Hill nor Schreiber discloses, wherein the first parameter is calculated based on a third parameter that evaluates a first accuracy of each of the plurality of first machine learning models and a second accuracy of an aggregated output of the plurality of first machine learning models.
Nguyen discloses, wherein the first parameter is calculated based on a third parameter (generalized ML model and to provide predictive output for a functional system even in absence of a well trained ML model. The system also contains an ensemble model which aggregates the outputs of both the expert-made knowledge model and the generalized (ML) model and outputs a final decision. This ensemble model can combine these outputs in a number of ways. According to an embodiment, the ensemble model combines the outputs using a logical AND or OR between the prior model outputs. According to other embodiments, the ensemble model inspects the model accuracy of the ML model and prioritizes the knowledge model output if ML model accuracy is low. According to an embodiment, the ensemble model is implemented as an ML model, learning to optimally use both ML and knowledge outputs to generate a final decision for system operation ¶ [0031], In an embodiment, the ensembled oracle 230 determines a result by combining the results of the generalized model 220 and the knowledge model 210. For example, if the output of each of the knowledge model 210 and the generalized model 220 is boolean, the ensembled oracle 230 performs an AND operation on the outputs of the knowledge model 210 and the generalized model 220 and returns the result of the AND operation as the overall prediction. In an embodiment, the ensembled oracle 230 determines the final result by taking a weighted aggregate of the outputs of the knowledge model 210 and the generalized model 220. The weights assigned to each output may be determined based on a measure of accuracy of the corresponding models executed for determining the output ¶ [0047], The system provides the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model ¶ [0181], also see claim 4) that evaluates a first accuracy of each of the plurality of first machine learning models (determine 1540 a measure of accuracy of prediction for each of the knowledge model and the ML model. The system provides the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model ¶ [0181], also see claim 4) and a second accuracy of an aggregated output of the plurality of first machine learning models (The system also contains an ensemble model which aggregates the outputs of both the expert-made knowledge model and the generalized (ML) model and outputs a final decision. This ensemble model can combine these outputs in a number of ways. According to an embodiment, the ensemble model combines the outputs using a logical AND or OR between the prior model outputs. According to other embodiments, the ensemble model inspects the model accuracy of the ML model and prioritizes the knowledge model output ¶ [0031], determines the final result by taking a weighted aggregate of the outputs of the knowledge model 210 and the generalized model 220. The weights assigned to each output may be determined based on a measure of accuracy of the corresponding models executed for determining the output ¶ [0047], if the outputs are numeric values the system uses the accuracy of each output to weight and average the outputs ¶ [0062], also see claims 4 and 5).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Nguyen’s system would have allowed Hill and Schreiber to facilitate wherein the first parameter is calculated based on a third parameter that evaluates a first accuracy of each of the plurality of first machine learning models and a second accuracy of an aggregated output of the plurality of first machine learning models. The motivation to combine is apparent in the Hill and Schreiber’s reference, because there is a need to improve use of machine learning based models combined with knowledge models for accurate predictions.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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7/17/2026
/MOHAMMAD S ROSTAMI/ Primary Examiner, Art Unit 2154
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