DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/11/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is 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 1-15 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”, “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”, “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”, and “updating, via the one or more hardware processors, the status attribute and the first test data based on the correctness score and a marking limit of a corresponding marker”.
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. Lastly, 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.
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 2-5, 7-9, and 12-15 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 1-15 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:
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.
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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DelBane (US 20200294410 A1) teaches a system for facilitating grading of handwritten sheets is disclosed. Accordingly, the system may include a communication device configured for receiving at least one digital answer sheet from at least one student device, receiving at least one digital master sheet from at least one evaluator device, and transmitting at least one grade to at least one of the at least one student device, and the at least one evaluator device, a processing device configured for determining at least one of a student hand-script and an evaluator hand-script based on analysis of the at least one digital answer sheet and digital master sheet respectively, comparing the at least one student hand-script with the at least one evaluator hand-script, and assigning the at least one grade to the at least one digital answer sheet based on the comparing, and a storage device configured for storing the at least one grade.
Shen et al. (US 20190138614 A1) teaches a method for recommending a teacher to a target student in a network teaching system. The method comprises: obtaining characteristic information of the target student; retrieving at least one candidate teacher from a teacher database according to the characteristic information of the target student, so as to obtain a candidate teacher list including the at least one candidate teacher; calculating, for the target student, a probability of reserving a course provided by each candidate teacher in the candidate teacher list; and ranking the at least one candidate teacher in the candidate teacher list based on the calculated probability and providing the target student with the ranked candidate teacher list.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658