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 AIA .
Status of Claims
This communication is a Final Office action in response to communications received on 01/23/2026. Claims 1 and 6 have been amended. Claims 4-5 have been canceled. Therefore, claims 1-3 and 6-17 are currently pending and have been addressed below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/23/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-3 and 6-17 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-3 and 6-16 are directed to a method (i.e. a process). Claim 17 is directed to a computer readable storage device (i.e. an article of manufacture). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea. Examiner note: Applicant’s specification, para 0036, recites: “The storage device may include a machine readable storage device including any type of tangible, non-transitory storage device.” Therefore, Examiner is interpreting claim 17 storage device as non-transitory.
Step 2A: Prong One: Abstract Ideas
Claims 1-3 and 6-17 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claim 17 recites: A method of predicting the retention rates of students attending an educational institution, the method comprising: receiving an initial dataset related to a cohort of students attending the educational institution; dividing the initial dataset into a training dataset and a testing dataset; training a predictive algorithm via the training dataset to generate a prediction model; and processing the testing dataset via the prediction model to output a prediction results dataset. The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). As the claims discuss predicting the retention rates of students attending an educational institution which is one of certain methods of organizing human activity. Mathematical concepts (mathematical relationships, mathematical formulas or equations and mathematical calculations (as independent claim 1, analogous to independent claim 17, recites “predicting the retention rates”; “dividing the initial dataset into a training dataset and a testing dataset; training a predictive algorithm via the training dataset to generate a prediction model”; “the predictive algorithm comprises: processing the training dataset with the predictive algorithm to generate a training model; validating the training model; and generating the prediction model based on the validated training model”; “dividing the training dataset into a plurality of subsets; randomly selecting a first of the plurality of subsets as a first test subset and selecting the remaining ones of the plurality of subsets as a first training subset; training the training model via the first training subsets to generate a first training sub- model; validating the first training sub-model via the first test subset; and repeating the randomly selecting, training, and validating steps for each of the remaining ones of the plurality of subsets to generate a plurality of validated training sub-models”; claim 2 recites “filtering a percentage of the prediction results”; claim 6 recites: “the prediction model comprises a weighted combination of the plurality of validated training sub-models”; claim 7 recites: “the training dataset and the testing dataset are resampled from the initial dataset”; claims 9 and 12 recites: “filter and transform the initial dataset to a format compatible with the predictive algorithm”; claim 10 recites: “dividing the updated dataset into an updated training dataset and an updated testing dataset; retraining the predictive algorithm via the updated training dataset to generate an updated prediction model”; claim 15 recites: “wherein the training dataset comprises 80% of the initial dataset and the testing dataset comprises 20% of the initial dataset”: claim 16 recites: “the updated training dataset comprises 80% of the updated dataset and the updated testing dataset comprises 20% of the updated dataset.”)
Further, dependent claims add additional limitations, for example: (claim 3) wherein the watchlist comprises the top 15% of the cohort of students most likely to leave the educational institution; (claim 8) wherein the initial dataset comprises admission data, academic data, and financial data for each student of the cohort of students; (claim 11) wherein the updated dataset comprises updated admission data, updated academic data, and updated financial data for each student of the cohort of students still attending the educational institution after the predetermined period of time; (claim 13) the updated testing dataset are resampled from the updated dataset; (claim 14) learning management system (LMS). receiving data and early warning system (EWS) data for each student of the cohort of students; analyzing the LMS data and the EWS data; and adjusting the prediction results dataset based on the analyzed LMS data and EWS data, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of organizing human activity and mathematical concepts, but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 14) real-time (claim 17) computer readable storage device. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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 generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-3 and 6-17 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-3 and 6-17 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 2020/0302296 A1), hereinafter “Miller”, over Sardonis et al. (US 2012/0233084 A1), hereinafter “Sardonis”.
Regarding Claim 1, Miller teaches A method of predicting the... attending an educational institution, the method comprising: (See Miller throughout teaches prediction models of students attending an educational institution, See at least Miller para 0014, 0110, 0119, 0121);
receiving an initial dataset related to a cohort of students attending the educational institution; (Miller, para 0199, securing information on >4,000 students (i.e., demographics, admission, and enrollment criteria, competencies, surveys, course evaluations, testing results, etc.));
dividing the initial dataset into a training dataset and a testing dataset; (Miller, para 0210, teaches partitioning a training dataset and testing dataset);
training a predictive algorithm via the training dataset to generate a prediction model; and (Miller, para 0119, a machine learning (ML) analysis to predict individual learner's academic performance, career aptitudes, and personal resilience... the evaluation module configures the evaluation server to access datasets of students ... In one example, 2000 discrete data elements measuring>80 learner attributes from the point of school application to graduation including demographics, task performance data, opinions and standardized testing outcomes are provided to the configured evaluation server as test or training data to build a predictive model for student performance); processing the testing dataset via the prediction model to output a prediction results dataset, wherein; (Miller, para 0119, output the one or more predictive models generated by the evaluation module.) training the predictive algorithm comprises: processing the training dataset with the predictive algorithm to generate a training model; (See at least Miller, para 0195, datasets that are to be used to train and validate the predictive model);
validating the training model; and (See at least Miller, para 0195, datasets that are to be used to train and validate the predictive model);
generating the prediction model based on the validated training model; and (See at least Miller, para 0195, datasets that are to be used to train and validate the predictive model; para 0210, After training the predictive model on raw data, we then tested its robustness with new data in order to validate the model; para 0119, output the one or more predictive models generated); validating the training model comprises:
dividing the training dataset into a plurality of subsets; (Miller, para 0204, A cluster is a collection of similar (to each other) items (Examiner notes subsets) that are mathematically dissimilar from those in other data clusters);
randomly selecting a first of the plurality of subsets as a first test subset and selecting the remaining ones of the plurality of subsets as a first training subset; (Miller, para 0210, dataset was partitioned randomly into 80% training dataset and 20% testing dataset);
training the training model via the first training subsets to generate a first training sub- model; validating the first training sub-model via the first test subset; and (See at least Miller, para 0195, datasets that are to be used to train and validate the predictive model; Miller, para 0210, teaches training the predictive model, then validating the model).
repeating the randomly selecting, training, and validating steps for each of the remaining ones of the plurality of subsets to generate a plurality of validated training sub-models (Miller, para 0182, AI programmers train computers to solve problems by asking well-informed questions, adding ever-expanding fact arrays, ranking multiple algorithm performance, then repeating in order to build confidence in the answers—this is machine learning. Further, see at least Miller, para 0010, teaches AI iterative algorithms and Miller, para 0133, teaching the iterative process in the validations steps). Yet, Miller does not appear to explicitly teach and in the same field of endeavor Sardonis teaches retention rates of students (See at least Sardonis, Abstract, A retention management system identifies, analyzes, and evaluates student information collected by the enterprise resource planning systems and learning management systems. The retention management system applies an algorithm to collected information and locates students that are struggling before they are lost to attrition; Figure 5 and para 0077-0078, teaches student's total score including risk factor values.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller with retention rates of students as taught by Sardonis with the motivation to evaluate students based on academic, financial, and social risk factors to determine which students are most in danger of attrition (Sardonis, para 0008). The Miller invention now incorporating the Sardonis invention, has all the limitations of claim 1.
Regarding Claim 2, Miller, now incorporating Sardonis, teaches The method of claim 1,
Yet, Miller does not appear to explicitly teach and in the same field of endeavor Sardonis teaches wherein the prediction results dataset comprises a listing of the cohort of students organized from most likely to leave the educational institution to least likely to leave the educational institution; and (See at least Sardonis, Figure 6, para 0081-0084 and Table 1, teaches the names of student by risk including “high risk”, “moderate risk”, and “low risk” students. Examiner notes “high risk” students are students most likely to leave the educational institution and “low risk” students are least likely to leave the educational institution);
wherein the method further comprises filtering a percentage of the prediction results dataset to identify a watchlist of students likely to leave the educational institution (See at least Sardonis, Figure 5, teaches high risk students is below 40%; Figure 6, para 0081-0084 and Table 1, teaches a retention screen that allows a user to filter the names of student by risk including “high risk”. Examiner notes high risk students are a watchlist of students likely to leave the educational institution.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein the prediction results dataset comprises a listing of the cohort of students organized from most likely to leave the educational institution to least likely to leave the educational institution; and wherein the method further comprises filtering a percentage of the prediction results dataset to identify a watchlist of students likely to leave the educational institution as taught by Sardonis with the motivation to evaluate students based on academic, financial, and social risk factors to determine which students are most in danger of attrition (Sardonis, para 0008).
Regarding Claim 3, Miller, now incorporating Sardonis, teaches The method of claim 2.
Yet, Miller does not appear to explicitly teach and in the same field of endeavor Sardonis teaches wherein the watchlist comprises the top 15% of the cohort of students most likely to leave the educational institution (See at least Sardonis, Figure 5, teaches high risk students is below 40%; Figure 6, para 0081-0084 and Table 1, teaches a retention screen that allows a user to filter the names of student by risk including “high risk”, “moderate risk”, and “low risk” students. Examiner notes high risk students are the number of students most likely to leave the educational institution. The specific number of 15% adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein the watchlist comprises the top 15% of the cohort of students most likely to leave the educational institution as taught by Sardonis with the motivation to evaluate students based on academic, financial, and social risk factors to determine which students are most in danger of attrition (Sardonis, para 0008).
Regarding Claim 6, Miller, now incorporating Sardonis, teaches The method of claim 1, wherein the prediction model comprises a weighted combination of the plurality of validated training sub-models (Miller, para 0204, The k-means clustering algorithm was used to classify unlabeled data items from the student population into different groups... Whereas an initial set of weak base classifier predictions are combined and have their updated mathematical weighting (W) parameters adjusted through iteration to create a single stronger classifier, initial clusters can exert great influence on final clusters... the cluster analyses were validated in multiple data runs.)
Regarding Claim 7, Miller, now incorporating Sardonis, teaches The method of claim 1, wherein before the training step the training dataset and the testing dataset are resampled from the initial dataset (Miller, para 0210, teaches training dataset and testing dataset; para 0219, used an initial sample of ˜1,288 and a final sample of 929 students with ˜200 unique data elements per student).
Regarding Claim 8, Miller, now incorporating Sardonis, teaches The method of claim 1, wherein the initial dataset comprises admission data, academic data, and financial data for each student of the cohort of students (Miller, para 0124, teaches data points GPA, test score (Examiner notes academic data); Miller, para 0090, financial status (student debt); para 0102, relevant data including Admissions data).
Regarding Claim 9, Miller, now incorporating Sardonis, teaches The method of claim 1, further comprising preprocessing the initial dataset to filter and transform the initial dataset to a format compatible with the predictive algorithm (See at least Miller, para 0106, transform the structured or unstructured data received ... one or more data transformations are used to transform such subjective data into numerical or vector data. ... By converting subjective data into structured vector or numerical data, a wider array of data can be accessed and used ... by converting or transforming unstructured data into structured data using a consistent method, non-identical pieces of unstructured data can be compared to one another using the systems and methods provided herein, thereby increasing the predictive accuracy of the overall system).
Regarding Claim 10, Miller, now incorporating Sardonis, teaches The method of claim 1, further comprising:
receiving, after a predetermined period of time, an updated dataset related to the cohort of students; (See at least Miller, para 0130-0132, discussing updating data; Miller, para 0199, securing data from internal source systems on a periodic basis... information on >4,000 students (i.e., demographics, admission and enrollment criteria, competencies, surveys, course evaluations, testing results, etc.)); dividing the updated dataset into an updated training dataset and an updated testing dataset; (Miller, para 0210, the dataset was partitioned into 80% training dataset and 20% testing dataset.)
retraining the predictive algorithm via the updated training dataset to generate an updated prediction model; and (See at least Miller, para 0124, generate new content based on an initial or initiating request or instruction. Here, the content generation module configures the one or more processors of the evaluation server to generate a need of rectification for an individual learner. Where the model predicts a certain score for an individual learner, the content module configures the evaluation server to modify or augment one or more data values associated with the learner. For example, the content module configures the evaluation server change one or more data values in that learner's structured and unstructured data set. Upon augmenting the learner specific data set, the data set is evaluated again against the model. This process can proceed iteratively);
processing the updated testing dataset via the updated prediction model to output an updated prediction results dataset (Miller, para 0124, This process can proceed iteratively until the desired score is achieved. Once the desired score is achieved, the content module generates one or more data values indicating necessary data points needed to achieve the desired outcome).
Regarding Claim 11, Miller, now incorporating Sardonis, teaches The method of claim 10, wherein the updated dataset comprises updated admission data, updated academic data, and updated financial data for each student of the cohort of students still attending the educational institution after the predetermined period of time (See at least Miller, para 0130-0132, discussing updating data; Miller, para 0124, teaches data points GPA, test score (Examiner notes academic data); Miller, para 0090, financial status (student debt); para 0102, relevant data including Admissions data).
Regarding Claim 12, Miller, now incorporating Sardonis, teaches The method of claim 10, further comprising preprocessing the updated dataset to filter and transform the updated dataset to a format compatible with the predictive algorithm (See at least Miller, para 0130-0132, discussing updating data; See at least Miller, para 0106, transform the structured or unstructured data received ... one or more data transformations are used to transform such subjective data into numerical or vector data. ... By converting subjective data into structured vector or numerical data, a wider array of data can be accessed and used ... by converting or transforming unstructured data into structured data using a consistent method, non-identical pieces of unstructured data can be compared to one another using the systems and methods provided herein, thereby increasing the predictive accuracy of the overall system.)
Regarding Claim 13, Miller, now incorporating Sardonis, teaches The method of claim 10,wherein before the retraining step the updated training dataset and the updated testing dataset are resampled from the updated dataset (See at least Miller, para 0130-0132, discussing updating data; Miller, para 0210, teaches training dataset and testing dataset; para 0219, used an initial sample of ˜1,288 and a final sample of 929 students with ˜200 unique data elements per student).
Regarding Claim 14, Miller, now incorporating Sardonis, teaches The method of claim 1, further comprising: receiving, in real-time, ... data for each student of the cohort of students; (See at least Miller, Figures 11-15; para 0223, real-time insights on the academic positioning and performance trajectories of learners related to the in-cluster and near-cluster peers... the analytics platform may tag key personal success icons (i.e., empathy, manual dexterity, grit) and feed these data features into the analytic model. In turn the analytic platform is configured to evaluate the learner's information in real time);
analyzing the ... data and the ... data; and adjusting the prediction results dataset based on the analyzed ... data and ... data (Miller, para 0068, para 0124, the model predicts a certain score for an individual learner, the content module configures the evaluation server to modify or augment one or more data values associated with the learner.)
Yet, Miller does not appear to explicitly teach and in the same field of endeavor Sardonis teaches learning management system (LMS) data and early warning system (EWS) ... LMS... EWS (See at least Sardonis, Abstract, A retention management system identifies, analyzes, and evaluates student information collected by the enterprise resource planning systems and learning management systems. The retention management system applies an algorithm to collected information and locates students that are struggling before they are lost to attrition; See at least Sardonis Figure 1, teaching Learning Management System; Sardonis throughout teaches risk factors, see at least Figure 5, para 0008, para 0052-0053, para 0061-0062, discussing risk factors/retention factors; Further, Sardonis, Figure 19 and para 0127, teaches early warning). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Miller with learning management system (LMS) data and early warning system (EWS) ... LMS... EWS as taught by Sardonis with the motivation to evaluate students based on academic, financial, and social risk factors to determine which students are most in danger of attrition (Sardonis, para 0008).
Regarding Claim 15, Miller, now incorporating Sardonis, teaches The method of claim 1, wherein the training dataset comprises 80% of the initial dataset and the testing dataset comprises 20% of the initial dataset (Miller, para 0210, discussing ML algorithms, the dataset was partitioned randomly into 80% training dataset and 20% testing dataset.)
Regarding Claim 16, Miller, now incorporating Sardonis, teaches The method of claim 10,wherein the updated training dataset comprises 80% of the updated dataset and the updated testing dataset comprises 20% of the updated dataset (See at least Miller, para 0130-0132, discussing updating data; Miller, para 0210, discussing ML algorithms, the dataset was partitioned randomly into 80% training dataset and 20% testing dataset.)
Regarding Claim 17, the claim is an obvious variant to claim 1 above, and is therefore rejected on the same premise. Miller teaches a computer readable storage device having stored instructions that when executed by one or more processors result in operations (See at least Miller, para 0026, a computer program product having a computer-usable medium having a sequence of instructions which, when executed by a processor, causes said processor to execute an electronic process).
Response to Arguments
Applicants arguments filed on 01/23/2026 have been fully considered but they are not persuasive. Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejection in light of the most recent claim amendments and maintains the 101 rejection. Applicant’s arguments have been fully considered but are found unpersuasive.
With respect to Applicants remarks (page 6) “However, the Office failed to look at what the claims as a whole are directed to...” Examiner respectfully disagrees. Examiner asserts the claims have been properly considered both individually and as a whole. With respect to the abstract idea, the claimed invention falls within at least the abstract groupings of certain methods of organizing human activity and mathematical concepts as explained in the above 101 analysis.
With respect to Applicant’s remarks on Enfish, in Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. It was the specification' s discussion of how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. The claim was not simply using general purpose computers to perform an abstract idea, but a specific implementation of a solution to a problem in the software arts. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts.
With respect to Applicant’s remarks on Desjardins and an alleged improvement of ML algorithms, Examiner respectfully does not find these remarks persuasive. Desjardins is particular for describing techniques for training machine learning models and emphasizes the importance of explaining improvements to technology from the claimed processes within the specification. Applicant’s specification only mentions known techniques, see Applicant’s spec, para 0032, recites: “In some embodiments, hyperparameter tunning and cross validation are performed on the predictive algorithm to improve the prediction model's performance.” Examiner notes hyperparameter tuning is not only well-known, but is a foundational part of machine learning. Further, Applicant’s specification does not describe any improvements to ML algorithms, rather is using them in their known capacity for their known benefits.
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Each step does no more than require a generic
computer to perform generic computer functions. The claims do not, for example, purport to improve the functioning of the computer itself. In addition, the claims do not affect an improvement in any
other technology or technical field. The specification spells out different generic equipment and parameters that might be applied using the concept and the particular steps such conventional processing would entail based on the concept of information access. Thus, the claims at issue amount to nothing significantly more than instructions to apply the abstract idea using some unspecified, generic computer(s). Therefore, Applicants remarks are found unpersuasive and Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. With respect to the prior art rejections, Applicants arguments have been fully considered but are found unpersuasive. Examiner has updated the rejections in light of the most recent claim amendments.
With respect to Applicant’s remarks (page 9): "AI programmers train computers to solve problems by asking well-informed questions, adding ever-expanding fact arrays, ranking multiple algorithm performance, then repeating in order to build confidence in the candidate answers-this is machine learning." Furthermore, Paragraph 182 is a description of general steps that AI programmers perform, and does not appear to be a description of the explicit process being carried out in Miller. Therefore Miller does not fairly teach or render obvious the process of "repeating the randomly selecting, training, and validating steps for each of the remaining ones of the plurality of subsets to generate a plurality of validated training sub- models", as claimed.”
Examiner respectfully does not find these arguments persuasive.
As an initial matter, Applicant’s own specification briefly mentions the limitation “repeating the randomly selecting, training, and validating steps”. Further, Applicant’s specification describes machine learning in its known and ordinary capacity. Even Further, Miller, para 0182, teaches AI programmers train computers to solve problems by asking well-informed questions, adding ever-expanding fact arrays, ranking multiple algorithm performance, then repeating in order to build confidence in the answers—this is machine learning. Examiner respectfully notes “repeating” or “iterating” is a core part of the process of machine learning – this is how machine learning works. See Even further, Miller teaches throughout iterative algorithms see at least Miller, para 0010. Even further, see Miller, para 0133, teaching the iterative process in the validations steps.
Therefore, Applicants remarks are found unpersuasive and Examiner has updated maintains the 103 rejections for all claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.R.N./ Examiner, Art Unit 3629
/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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