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 .
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 (abstract idea) without significantly more.
Analysis
Claim 1: Ineligible.
STEP 1:
The broadest reasonable interpretation of the claim encompasses a computer system (e.g., hardware such as processors and memories) for updating machine learning models. The system is directed to at least a machine, which is a statutory category of invention (Step 1: YES). See MPEP 2106.03.
STEP 2A (PRONG 1):
The claim is analyzed to determine whether it is directed to a judicial exception. The claim recites:
at least one processor, at least one memory, and computer-readable media having computer-executable instructions stored thereon, the computer-executable instructions, when executed by the at least one processor, causing the system to perform operations comprising:
inputting, into a machine learning model, a dataset comprising a plurality of entries with each entry comprising a corresponding plurality of features to obtain a plurality of predictions, wherein the machine learning model is trained to generate predictions for entries based on corresponding features;
generating, for each entry of the plurality of entries, a plurality of feature impact parameters indicating a relative impact of each feature of the corresponding plurality of features on each prediction of the plurality of predictions;
determining a feature impact threshold for assessing which features of the corresponding plurality of features have contributed to each prediction;
generating, using the plurality of feature impact parameters and the feature impact threshold, a sparsity metric for each prediction, wherein the sparsity metric indicates which features of the corresponding plurality of features have relative impacts that meet the feature impact threshold for the prediction;
generating a global sparsity metric for the machine learning model, the global sparsity metric indicating, for the plurality of predictions, the features having relative impacts that meet the feature impact threshold; and
updating the machine learning model based on the global sparsity metric.
Examiner submits that the foregoing bolded limitation(s) constitute mental processes. In order words, the bolded claim limitations as drafted, refer to processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, and/or performance of mathematical concepts, but for the use of generic computer components.
Therefore, the bolded claim limitations fall under the abstract idea category of “Mental Processes” group in the form of “observation, evaluation, judgement, opinion” and/or “Mathematical concepts” group in the form of “mathematical relationships”. (Step 2A1-Yes). See MPEP 2106.04(a)-(c)
STEP 2A (PRONG 2):
Next, the claim is analyzed to determine if it is integrated into a practical application.
Examiner submits that the foregoing italicized limitation(s) constitute the additional elements.
The claim recites additional elements of at least one processor, memory and computer-readable media (CRM). The at least one processor, memory and computer-readable media in the steps are recited at a high level of generality, i.e., as generic processor, memory and CRM performing generic computer functions. These elements are no more than mere instructions to apply the exception using generic computer components (see MPEP 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea (Step 2A2-No). See MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2)
STEP 2B:
Next, the claim is analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claim is not patent eligible. See MPEP 2106.05
Claims 2 and 15 recite corresponding method and non-transitory CRM equivalent of claim 1. These claims are similarly rejected under the same rationale as claim 1, supra.
Claims 3 and 16 recite wherein determining the feature impact threshold comprises: modifying the dataset to include an additional feature for each entry of the plurality of entries, wherein values for the additional features are randomly generated; generating, for each entry of the additional plurality of entries, a corresponding additional feature impact parameter indicating a relative impact of a corresponding additional feature on each prediction of the plurality of predictions; and determining the feature impact threshold based on a highest additional feature impact parameter.
These limitations further narrow the abstract idea, but are nonetheless part of the abstract idea identified in claim 1 above. The additional elements, as similarly analyzed in claim 1 above, do not integrate the abstract idea into a practical application. The claimed invention as a whole also does not amount to significantly more than the abstract idea. The claim is similarly rejected under the same rationale as claim 1, supra.
Claims 4-5 and 17 recite wherein generating the sparsity metric for each prediction comprises: determining whether a feature impact parameter for each feature associated with the prediction meets the feature impact threshold; based on a first subset of the plurality of feature impact parameters for a first subset of features associated with the prediction meeting the feature impact threshold, determining that the first subset of features contributes to the prediction; based on a second subset of the plurality of feature impact parameters for a second subset of features associated with the prediction not meeting the feature impact threshold, determining that the second subset of features does not contribute to the prediction; and generating the sparsity metric for the prediction to include the first subset of features and exclude the second subset of features; wherein generating the global sparsity metric comprises applying a function to sparsity metrics for the plurality of features across the plurality of predictions
These limitations further narrow the abstract idea, but are nonetheless part of the abstract idea identified in claim 1 above. The additional elements, as similarly analyzed in claim 1 above, do not integrate the abstract idea into a practical application. The claimed invention as a whole also does not amount to significantly more than the abstract idea. The claim is similarly rejected under the same rationale as claim 1, supra.
Claims 6-10 recite generating, based on a feature of the plurality of features, a plurality of subsets of the dataset, wherein a first subset of the plurality of subsets is associated with a first category of the feature different from a second category of the feature associated with a second subset of the plurality of subsets; and generating a plurality of sparsity metrics for the plurality of subsets; determining that one or more subsets of the plurality of subsets of the dataset have similar sparsity metrics to one or more other subsets of the plurality of subsets of the dataset; and training a new machine learning model based on the one or more subsets and the one or more other subsets; determining that one or more subsets of the plurality of subsets of the dataset have different sparsity metrics from one or more other subsets of the plurality of subsets of the dataset; and training, based on the different sparsity metrics, one or more new machine learning models to generate new predictions based on the one or more subsets and the one or more other subsets of the plurality of subsets of the dataset; identifying one or more features most commonly meeting the feature impact threshold within each subset of the plurality of subsets of the dataset; and training a new machine learning model based on the one or more features; determining that a received entry is associated with the first category; and selecting a corresponding machine learning model for the received entry based on the first category.
These limitations further narrow the abstract idea, but are nonetheless part of the abstract idea identified in claim 1 above. The additional elements, as similarly analyzed in claim 1 above, do not integrate the abstract idea into a practical application. The claimed invention as a whole also does not amount to significantly more than the abstract idea. The claim is similarly rejected under the same rationale as claim 1, supra.
Claims 11-12 and 19-20 recite determining an initial accuracy metric for the machine learning model; training a new machine learning model using a modified dataset comprising entries for a subset of the plurality of features to obtain new predictions; determining a modified accuracy metric for the new machine learning model based on the modified dataset; determining whether an accuracy difference between the initial accuracy metric and the modified accuracy metric meets an accuracy threshold; and based on the accuracy difference meeting the accuracy threshold, replacing the machine learning model with the new machine learning model; determining an initial prediction speed for the machine learning model; determining a modified prediction speed for the new machine learning model based on the modified dataset; determining whether a prediction speed difference between the initial prediction speed and the modified prediction speed meets a prediction speed threshold; and based on the accuracy difference meeting the accuracy threshold and the prediction speed difference meeting the prediction speed threshold, replacing the machine learning model with the new machine learning model.
These limitations further narrow the abstract idea, but are nonetheless part of the abstract idea identified in claim 1 above. The additional elements, as similarly analyzed in claim 1 above, do not integrate the abstract idea into a practical application. The claimed invention as a whole also does not amount to significantly more than the abstract idea. The claim is similarly rejected under the same rationale as claim 1, supra.
Claims 13-14, 18 recite wherein updating the machine learning model comprises: selecting one or more hyperparameters associated with the machine learning model; and adjusting the one or more hyperparameters based on the global sparsity metric; wherein updating the machine learning model comprises: selecting, based on the global sparsity metric, one or more features of the plurality of features; and training a new machine learning model based on the one or more features.
These limitations further narrow the abstract idea, but are nonetheless part of the abstract idea identified in claim 1 above. The additional elements, as similarly analyzed in claim 1 above, do not integrate the abstract idea into a practical application. The claimed invention as a whole also does not amount to significantly more than the abstract idea. The claim is similarly rejected under the same rationale as claim 1, supra.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The references cited in PTO-892 are relevant to the claimed invention but they do not, individually, or in combination teach the claimed invention as a whole.
In particular, the cited references individually, or in combination, fail to teach:
generating, using the plurality of feature impact parameters and the feature impact threshold, a sparsity metric for each prediction, wherein the sparsity metric indicates which features of the corresponding plurality of features have relative impacts that meet the feature impact threshold for the prediction;
generating a global sparsity metric for the machine learning model, the global sparsity metric indicating, for the plurality of predictions, the features having relative impacts that meet the feature impact threshold; and
updating the machine learning model based on the global sparsity metric.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLABODE AKINTOLA whose telephone number is (571)272-3629. The examiner can normally be reached Mon-Fri 8:30a-6:00p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached at 571-270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLABODE AKINTOLA/Primary Examiner, Art Unit 3691