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 .
Response to Amendment
This Office Action is in response to applicant’s communication filed 11 June 2026, in response to the Office Action mailed 25 March 2026. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-7 recite a method, claims 8-14 recite a manufacture, and claims 15-20 recite a device. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
analyzing one or more external datasets to identify a set of similar features – a data scientist analyzes external datasets to identify sets of similar features. Alternatively/additionally – comparing feature values of different datasets to determine similarity is a mathematical calculation.
wherein analyzing one or more external datasets to identify a set of similar features includes converting feature labels into encoded labels and creating a correlation matrix between the encoded feature labels indicating levels of similarity between the feature labels – the data scientist converts feature labels into encoded labels and creates a matrix of the levels of similarity between them. Alternatively/additionally – converting features to numerical vectors is a mathematical calculation, as is determining the similarity between the encoded values.
whose similarity exceeds a threshold amount determined using the correlation matrix – comparing similarity values to a threshold is a mathematical calculation.
and assessing performance of the updated machine learning model – the data scientist assesses the performance of the updated machine learning model (speed, accuracy, error, etc.). Alternatively/additionally – assessing performance of the updated machine learning model includes calculating an assessment value (e.g., via a loss/cost/error function), which is a mathematical calculation/formula.
responsive to training the updated machine learning model, comparing a performance of the updated machine learning model to a previous performance that occurred prior to the training – comparing performance metric values is a mathematical calculation. Alternatively/additionally – the data scientist compares the performance of the updated machine learning model with previous performance (of the model before training).
identifying a set of actions, wherein the set of actions are utilized to determine whether to optimize the modified dataset by merging or modifying the existing features based on the comparison of performances and the monitoring – the data scientist determines whether to merge or modify the existing features and identifies a set of actions to take, based upon the comparison.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a computer implemented method – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
comprising: receiving a dataset for use with respect to a current machine learning model – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage. See MPEP § 2106.05(g).
wherein the dataset comprises one or more features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
appending the set of similar features to the received dataset to generate an updated dataset – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein appending the set of similar features to the received dataset includes adding features whose similarity exceeds a threshold amount determined using the correlation matrix – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating an updated dataset by modifying a received dataset to reflect added feature and updating additional instances of the received dataset to reflect the appended set of similar features – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
applying the updated dataset to the current machine learning model to generate an updated machine learning model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein assessing performance of the updated machine learning model comprises: training the updated machine learning model according to the modified dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
monitoring one or more performance metrics of interest associated with the updated machine learning model – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
outputting, to a user interface, recommendations for additional actions to improve the performance – this is recited at a high level of generality and amounts to insignificant extra-solution activity as insignificant application (display) of the abstract idea that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a computer implemented method – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
comprising: receiving a dataset for use with respect to a current machine learning model – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage. The courts have found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
wherein the dataset comprises one or more features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
appending the set of similar features to the received dataset to generate an updated dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
wherein appending the set of similar features to the received dataset includes adding features whose similarity exceeds a threshold amount determined using the correlation matrix – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating an updated dataset by modifying a received dataset to reflect added feature and updating additional instances of the received dataset to reflect the appended set of similar features – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
applying the updated dataset to the current machine learning model to generate an updated machine learning model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein assessing performance of the updated machine learning model comprises: training the updated machine learning model according to the modified dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
monitoring one or more performance metrics of interest associated with the updated machine learning model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
outputting, to a user interface, recommendations for additional actions to improve the performance – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining, storing, and displaying information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "presenting offers and gathering statistics.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
The claim recites the following additional mental process and/or mathematical concept elements:
recommending one or more actions based on the performance assessment of the updated machine learning model – the data scientist recommends one or more actions based on the performance assessment of the updated machine learning model (e.g., perform a recommended action provided by the model, retrain the model, create a new model, etc.).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein analyzing one or more external datasets to identify a set of similar features includes: converting the one or more features into numerical feature vectors – converting features into a numerical feature vector is a mathematical function. Alternatively/additionally – the data scientist could create the numerical feature vector from the one or more features.
identifying a set of similar features in the one or more external datasets – the data scientist analyzes external datasets to identify sets of similar features. Alternatively/additionally – comparing feature values of different datasets to determine similarity is a mathematical calculation.
using word embedding on the set of similar features – word embedding is a mathematical function. Alternatively/additionally – the data scientist performs the word embedding.
and identifying a vectoral distance between the one or more features and the set of similar features – identifying the vectoral distance between features is a mathematical function. Alternatively/additionally – the data scientist calculates the vectoral distance.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the current machine learning model includes a reinforcement learning model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the current machine learning model includes a reinforcement learning model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein analyzing one or more external datasets to identify a set of similar features includes using a bag of words technique to find similar features – the bag of words technique is a mathematical function to convert words/counts into a vector/numerical values. Alternatively/additionally – the data scientist could perform the bag of words technique.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
The claim recites the following additional mental process and/or mathematical concept elements:
using Pearson correlation to create a correlation between the one or more features and the set of similar features indicating a level of similarity – the Pearson correlation is a mathematical formula which creates the level of similarity value via mathematical calculations. Alternatively/additionally – the data scientist could determine the Pearson correlation value(s).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
The claim recites the following additional mental process and/or mathematical concept elements:
categorizing the features of the dataset into categorical features and unstructured text features, wherein categorical features are features with corresponding identifying metadata, and unstructured text features are features which lack such metadata – the data scientist looks at the features of the dataset and categorizes them into categorical features and unstructured text features, based on whether the features correspond to metadata or unstructured text without metadata.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
See the rejection of claim 1 above wherein, under step 2A, prong 1, the claim also includes the following mental process and/or mathematical concept elements:
features whose similarity falls below a threshold – this is a mathematical calculation (comparison). Alternatively/additionally – the data scientist can compare the similarity values to a threshold to determine which features should be added.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a computer program product comprising: one or more computer readable storage media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and program instructions stored on the one or more computer readable storage media, the program instructions comprising instructions to [perform the method] – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
adding features whose similarity falls below a threshold amount to the received dataset – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a computer program product comprising: one or more computer readable storage media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and program instructions stored on the one or more computer readable storage media, the program instructions comprising instructions to [perform the method] – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
adding features whose similarity falls below a threshold amount to the received dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9, see the rejection of claim 2, above.
As per claim 10, see the rejection of claim 3, above.
As per claim 11, see the rejection of claim 4, above.
As per claim 12, see the rejection of claim 5, above.
As per claim 13, see the rejection of claim 6, above.
As per claim 14, see the rejection of claim 7, above.
As per claim 15:
See the rejection of claim 8 above wherein, under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer system comprising: one or more processors – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
one or more computer-readable storage media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising instructions to [perform the method] – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A computer system comprising: one or more processors – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
one or more computer-readable storage media – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising instructions to [perform the method] – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 16, see the rejection of claim 2, above.
As per claim 17, see the rejection of claim 3, above.
As per claim 18, see the rejection of claim 4, above.
As per claim 19, see the rejection of claim 5, above.
As per claim 20, see the rejection of claim 6, above.
Response to Arguments
Applicant's arguments filed 11 June 2026, with respect to the rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the identified abstract idea does not fall within the subject matter groupings of abstract ideas.
However, the abstract ideas have been identified as mental process and/or mathematical concepts, as described above. If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Applicant also argues that the abstract idea is “integrated into the practical application of a performance metric improvement system” by “providing improvements to existing advisory agent system technology.”
While the examiner has identified (above) what constitutes the abstract idea and what is drawn to conventional components, the Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process, where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer functionality. FairWarning IP, LLC v. latric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). The Federal Circuit has also indicated that a claim must include more than conventional implementation on generic components or machinery to qualify as an improvement to an existing technology. Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264-65, 120 USPQ2d 1201, 1208-09 (Fed. Cir. 2016); TLI Communications LLC v. AVAuto, LLC, 823 F.3d 607, 612-613, 118 USPQ2d 1744, 1747-48 (Fed. Cir. 2016). Claims must also include more than just instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP § 2106.05(a)).
Applicant further argues that the improvement is provided in how “embodiments of the present invention dynamically improve performance through reinforcement learning to append new features to existing dataset and reassess the model for its performance” and that “recited limitations improve the system by introducing a structured and controlled feature-engineering pipeline that enhances both the quality and relevance of training data used by the machine learning model.”
However, applicant has described an improvement to determining whether/when to add new features to the existing dataset. As described above, this is a mental process/mathematical calculations. Therefore, (assuming that the invention provides these advantages) this amounts to an improvement to an abstract idea rather than to a computer or technology. See MPEP 2106.05(a). It appears that any benefits to the computer itself are based solely on the use of an improvement to the abstract idea(s), using generic computer components to apply the abstract idea(s). Additionally, to find a valid improvement to a computer or technology the specification must disclose the improvement and the claim must include the necessary components to realize the improvement. MPEP 2106.05(d)(1).
Applicant’s arguments, see the remarks, filed 11 June 2026, with respect to the rejections under 35 U.S.C. 103 have been fully considered and are persuasive in view of the amendments made to the independent claims. While additional cited art (see below) teaches various elements of the claimed invention, none of the cited art appears to provide motivation for combining the feature encoding and use of the correlation matrix with the other recited limitations taught by the prior cited art. Therefore, the rejections of claims 1-20 have been withdrawn.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu (US 11,443,553) – discloses merging subsets of facial images based on similarity scores of feature vectors.
Seifert (US 2021/0141897) – discloses combining features into a similarity training set with pairing of similar files/documents, used for multiple training instance datasets, including for negative sampling based on similarity.
Yagnik (US 7,827,123) – discloses determining feature proximity values, combined feature proximity values, and adding a lowest proximity value feature to a training set.
Chang (US 11,636,161) – discloses adding new features, provided by a user, to a training dataset.
Zhang (US 12,165,056) – discloses using a trained model to transform an observed data subset and features to a predicted version of a new feature, and use the new feature and dataset to train an auxiliary model.
Gupta (US 2019/0130304) – discloses a system identifying a new electronic communication (or new features and interactions for a prior communication), then generating an additional training instance based on the new data.
Uchide (US 2021/0312333) – discloses selecting samples for a negative example data set based on similarity level rankings.
Yadav (US 2019/0279618) – discloses identifying a cluster below a threshold level of similarity between latent features of users and, in response, excluding a specific model in generating a new personalized model.
Liu (US 11,443,554) – discloses merging subsets of images based on feature centroid similarity scores, including merging them with the centroid.
Saad (US 2023/0115855) – discloses monitoring performance metrics for particular arrangements of features (or combination of features) and updating the arrangements/combinations of features in response to the performance metric(s).
Gehler et al. (On Feature Combination for Multiclass Object Classification, Oct 2009, pgs. 221-228) – discloses a system to update weightings of features during training, and combining multiple complementary features.
Zhang et al. (A feature selection and multi-model fusion-based approach of predicting air quality, Dec 2019, pgs. 210-220) – discloses a system/method of fusing models (and associated features) for air quality prediction, including feature selection/extraction.
Korycki (US 2017/0068906) – discloses adding received messages and their feature vectors to a training dataset(s) used to train general and user specific models.
Sokolov (US 10,924,513) – discloses a network security service that trains a machine learning model on previously collected data and, for each device in a plurality of devices, receives activity data collected by a security agent executed at the additional device, wherein the activity data specifies an action type of the previous action and a time at which the previous action was performed.
Grady (US 2023/0045347) – discloses modified feature data generated by a function generator used as features for training a machine learning model and/or used by the model during runtime, which can also include determining whether retraining on the new set of features decreases or increases the accuracy of the model based on the gathered performance metrics and dynamic generation of new features.
Herman-Saffar (US 10,721,266) – discloses utilizing expert/team feedback and reinforcement learning to improve a recommendation model.
Moore (US 10,402,726) – discloses feature selection including selecting highly correlated features using a Pearson correlation matrix.
Mao (US 2023/0298307) – discloses feature transformation including positional encodings of coarse features and embedding vector generation for a correlation matrix.
The examiner requests, in response to this Office action, that 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 number(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).
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).
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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