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 communication is responsive to the applicant’s amendments dated 5/4/2026. The applicant amended claims 1, 5-7, 10-12, and 15. Claims 4, 9, and 14 have been cancelled.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C. 101, see Remarks (pg. 13, line 12 – pg. 20, line 6), filed 5/4/2026, with respect to claims 1-15 have been fully considered but they are not persuasive.
The applicant contends that the claims are not directed to an abstract idea because certain steps cannot be practically performed in the mind. This argument is not persuasive. The claim limitations are broadly recited in functional terms and encompass mental processes and organizing activity, including: receiving information, preprocessing and scoring responses, generating metadata,
analyzing rules, converting text to numerical constants, calculating productivity metrics, mapping answer scripts to markers, performing classification using logistic regression, and updating status based on results. These operations are fundamentally information processing and evaluation steps. The mere recitation of generic computer components such as a processor, memory, communication interfaces, or non-transitory storage media does not remove the claims from the abstract idea category. Generic computer implementation of an abstract idea is not sufficient to make the claims eligible under 101.
The applicant’s assertion that the claims are not abstract because some operations involve OCR, transcription, or logistic regression is also unpersuasive. The presence of computer-implemented tools does not, by itself, transform an abstract idea into a patent-eligible application where those tools are invoked at a high level of generality and merely perform conventional functions.
The applicant also argues that the claims integrate the abstract idea into a practical application because the specification describes: retraining a logistic regression model based on deviations in correctness scores, rule analysis by substitution, converting textual values to numerical constants, and a scheduler that initiates preprocessing and segmentation. These arguments are not commensurate with the claims as currently pending. The claims do not recite a specific improvement to computer functionality, a specific improvement to machine learning architecture, or a particular technical solution to a technical problem. Instead, the claims recite results-oriented steps and general functional operations. Any alleged improvement is described at a high level and is not tied to a particular claimed algorithm, data structure, or computer architecture. The specification passages, such as paragraphs [0036], [0049], [0063], cited by applicant cannot save the claims where the claims themselves fail to recite the specific technical features purportedly providing the improvement. Eligibility is determined based on the claims as a whole, not on unclaimed details from the specification.
Further, the applicant’s reliance on paragraph [0063] is ineffective. Retraining a logistic regression model based on deviations in scores is itself merely a mathematical or statistical processing step applied to information. The claim does not recite a specific unconventional training architecture, parameter update rule, or technical constraint that improves computer operation. Rather, it recites the use of a generic machine-learning model in the ordinary course of processing data. Similarly, the rule-substitution and text-to-number conversion limitations are directed to ordinary data manipulation. These steps do not amount to a particular technological improvement; they merely describe how information is organized and processed.
The applicant’s references to grammatical correctness, pronunciation correctness, OCR, and transcription, (page 17 of the remarks), also do not establish a practical application. These are conventional preprocessing functions that may be performed with generic tools and do not, as claimed, improve the functioning of the computer itself. The applicant further asserts that the claims include “significantly more” because the logistic regression model is retrained to improve mapping efficiency. This argument is not persuasive. The additional claim elements amount to no more than generic computer implementation of the abstract idea. The recited processor, memory, communication interfaces, database, scheduler, and machine-readable medium are conventional computer components used to carry out the abstract workflow. The use of a logistic regression model does not add significantly more where it is merely used as a mathematical tool for scoring and updating data.
The claims do not recite: a new or improved computer component, a new data structure, a specific nonconventional machine-learning training mechanism, a technical improvement in memory usage, latency, throughput, or network operation, or any other inventive concept sufficient to transform the abstract idea into a patent-eligible application.
The applicant’s reliance on Ex parte Desjardins is also not persuasive. The claims here do not recite the kind of specific machine-learning training architecture, parameter preservation, or task-specific technical improvement that was relevant in that case. Instead, the claims merely recite retraining a logistic regression model based on deviation of correctness scores in the context of answer-script-to-marker mapping. That is an abstract analytical workflow, not a claimed technological improvement.
Therefore, the 35 U.S.C. 101 rejection is maintained.
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, 5-8, 10-13, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 6, and 11 recite “receiving, via one or more hardware processors, one or more answer scripts in at least one media format, and information associated with a plurality of markers”, “pre-processing, via the one or more hardware processors, the one or more answer scripts based on the at least one media format to obtain a score of response in the one or more answer scripts by a scheduler comprised in a memory”, “generating, via the one or more hardware processors, an answer script metadata based on the score of the response in the one or more answer scripts”, “analyzing, via the one or more hardware processors, a set of pre- defined rules comprised in a database, applicable for each of the one or more answer scripts associated with the answer script metadata based on one or more answer script attribute values in the set of pre-defined rules, wherein analyzing the set of pre-defined rules includes fetching the set of pre-defined rules configured by an entity for marking, and analyzing each pre- defined rule applicable for each of the answer script by substituting the answer script attribute value in the rule”, “generating, via the one or more hardware processors, one or more instances of the one or more answer scripts based on the one or more answer script attribute values”, “converting, via the one or more hardware processors, one or more textual values of the one or more instances of the one or more answer scripts to one or more numerical constants to obtain one or more format-based answer scripts attributes”, “calculating, via the one or more hardware processors, a productivity metric for a current day based on one or more observations during marking by the plurality of markers, and adjusting an overall ranking for each marker from the plurality of markers based on the productivity metric for the current day”, “merging, via the one or more hardware processors, the calculated productivity metric for the current day with a plurality of productivity metrices till date to obtain merged productivity metric”, “determining, via the one or more hardware processors, an availability and a marking limit of one or more markers from the plurality of markers based on the merged productivity metric”, “transforming, via the one or more hardware processors, the merged productivity metric based on the availability and the marking limit of one or more markers to obtain transformed productivity metric into a pre-defined format”, “analyzing, via the one or more hardware processors, the set of pre- defined rules comprised in the database, applicable for each marker comprised in the transformed productivity metric based on one or more marker attribute values in the set of pre-defined rules”, “generating, via the one or more hardware processors, one or more instances of one or more markers based on the one or more marker attribute values”, “converting, via the one or more hardware processors, one or more textual values of the one or more instances of the one or more markers to one or more numerical constants to obtain one or more format-based markers attributes”, “performing, via the one or more hardware processors, a comparison of (i) the answer script attribute values of the one or more format-based answer script attributes and (ii) the marker attribute value of the one or more format- based marker attributes to obtain a mapped data further comprising a mapping of a relevant marker from the one or more markers for each answer script from the one or more answer scripts based on the overall ranking”, “categorizing, via the one or more hardware processors, the mapped data having (i) a status attribute further comprising a value '1' as a first test data, and (ii) one or more remaining attributes as a second test data”, “performing, by using a logistic regression model via the one or more hardware processors, a sigmoid function on a value of the one or more remaining attributes of the second test data, using a pre-configured training mapped data to calculate a correctness score for the first test data for each format-based answer script”, “retraining the logistic regression model, via the one or more hardware processors, based on a deviation of the correctness scores, wherein the correctness scores generated during mapping of the one or more answer scripts and the one or more markers undergo regression testing using the pre-configured training mapped data to re-check correctness score and the deviations”, “updating, via the one or more hardware processors, the status attribute comprising the value ‘1’ of the first test data based on the correctness score and a marking limit of a corresponding marker”, “analyzing the one or more answer scripts to determine a response in the one or more answer scripts correspond to two or more domains and identifying a subset of the one or more answer scripts based on the two or more domains to obtain a set of segmented answer scripts”, “generating an answer script metadata for the set of segmented answer scripts”, “analyzing the set of pre-defined rules comprised in a database, applicable for each of the set of segmented answer scripts associated with the answer script metadata based on one or more answer script attribute values in the set of pre-defined rules”, “generating one or more instances of the set of segmented answer scripts based on the one or more answer script attribute values”, and “converting one or more textual values of the one or more instances of the set of segmented answer scripts to one or more numerical constants to obtain a set of segmented format-based answer script attributes”.
The limitation of receiving answer scripts, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor”, “a memory”, “one or more communication interfaces”, and “non-transitory machine-readable information storage mediums”, nothing in the claim precludes the step from practically being performed in the mind. For example, but of the components listed above, “receiving” in the context of this claim encompasses receiving text which a human can do in the mind or with a pen and paper. Next, the limitation of pre-processing the answer script to obtain a score, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “pre-processing” in the context of this claim encompasses scoring text which a human can do in the mind or with a pen and paper. Next, the limitation of generating answer script metadata based on a score, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “generating” in the context of this claim encompasses generating data based off score which a human can do in the mind or with a pen and paper. Next, the limitation of analyzing pre-defined rules, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “analyzing” in the context of this claim encompasses analyzing text which a human can do in the mind or with a pen and paper. Next, the limitation of generating instances, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “generating” in the context of this claim encompasses analyzing text which a human can do in the mind or with a pen and paper. Next, the limitation of converting textual values to numerical constants, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “converting” in the context of this claim encompasses number conversion which a human can do in the mind or with a pen and paper. Next, the limitation of calculating a productivity metric, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “calculating” in the context of this claim encompasses performing calculations which a human can do in the mind or with a pen and paper. Next, the limitation of merging productivity metrics, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “merging” in the context of this claim encompasses combining data which a human can do in the mind or with a pen and paper. Next, the limitation of determining availability, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “determining” in the context of this claim encompasses analyzing data, which a human can do in the mind or with a pen and paper. Next, the limitation of transforming the merged productivity metric into a pre-defined format, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “transforming” in the context of this claim encompasses restructuring data, which a human can do in the mind or with a pen and paper. Next, the limitation of analyzing the set of pre-defined rules, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “analyzing” in the context of this claim encompasses analyzing text, which a human can do in the mind or with a pen and paper. Next, the limitation of generating instances based off attribute values, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “generating” in the context of this claim encompasses analyzing text which a human can do in the mind or with a pen and paper. Next, the limitation of converting textual values to numerical constants, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “converting” in the context of this claim encompasses number conversion which a human can do in the mind or with a pen and paper. Next, the limitation of performing a comparison is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “performing” in the context of this claim encompasses comparing values which a human can do in the mind or with a pen and paper. Next, the limitation of categorizing data, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “categorizing” in the context of this claim encompasses labeling data which a human can do in the mind or with a pen and paper. Next, the limitation of performing a sigmoid function is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “performing” in the context of this claim encompasses performing mathematical calculations which a human can do in the mind or with a pen and paper. Next, the limitation of retraining a model based off correctness cores, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “retraining” in the context of this claim encompasses performing mathematical/statistical processing which a human can do in the mind or with a pen and paper. Next, the limitation of updating the status attribute and first test data is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “updating” in the context of this claim encompasses adjust data which a human can do in the mind or with a pen and paper. Next, the limitation of analyzing answer scripts, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “analyzing” in the context of this claim encompasses analyzing text which a human can do in the mind or with a pen and paper. Next, the limitation of generating answer script metadata, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “generating” in the context of this claim encompasses generating data which a human can do in the mind or with a pen and paper. Next, the limitation of analyzing rules, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “analyzing” in the context of this claim encompasses analyzing rules which a human can do in the mind or with a pen and paper. Next, the limitation of generating instances, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “generating” in the context of this claim encompasses classifying text which a human can do in the mind or with a pen and paper. Lastly, the limitation of converting textual values to numerical constants, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but of the components listed above, “converting” in the context of this claim encompasses numerical conversion which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “a processor”, “a memory”, “one or more communication interfaces”, and “non-transitory machine-readable information storage mediums” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a processor”, “a memory”, “one or more communication interfaces”, and “non-transitory machine-readable information storage mediums” to perform the recited limitations. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 2-3, 5, 7-8, 10, 12-13, and 15 are also rejected for the same reasons provided in independent claim 1, 6, and 11 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Allowable Subject Matter
Claims 1-3, 5-8, 10-13, and 15 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101, set forth in this Office action. There are no pending prior art rejections.
The following is an examiner’s statement of reasons for allowance in terms of the prior art:
The closest piece of prior the examiner found was Cuzzola et al. (US 20240005230 A1) which teaches features such as “receiving, via one or more hardware processors, one or more answer scripts in at least one media format, and information associated with a plurality of markers”, however upon further search and consideration the examiner deems the prior art of record whether taken alone or in combination fails to teach “calculating, via the one or more hardware processors, a productivity metric for a current day based on one or more observations during marking by the plurality of markers, and adjusting an overall ranking for each marker from the plurality of markers based on the productivity metric for the current day” in combination with the other claim features, therefore the claims as a whole are allowable in terms of the prior art.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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