DETAILED ACTION
1. 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
2. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 an abstract idea without significantly more.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 8 and 15 as the representative claim, the following claimed limitations recite an abstract idea:
obtain course material and test materials for a plurality of different courses offered by an academic institution, wherein the test materials comprises a written response test;
obtain a standardized scoring rubric for a plurality of tests and different courses, wherein the standardized scoring rubric is the same for the plurality of courses;
obtain grading guidelines comprising test scoring rules for the written response test or course;
analyze the course materials and test materials to [obtain] a customized scoring rubric for the written response test or course based on the standardized scoring rubric, wherein the customized scoring rubric comprises at least a list of different grading criteria, customized descriptions for each grading criteria based at least on subject matter of the written response test and/or course, and criteria score levels for each grading criteria;
obtain a written response from a learner for a test in a course taken by the learner;
analyze the written response using two or more prepared test grading [templates], wherein each test grading template is associated with a single grading criteria from the customized scoring rubric [for] analyzing the written response based on the single grading criteria from the customized scoring rubric for the written response test and/or course and to generate a criteria score for the single grading criteria;
combine, by a scoring [template], a plurality of criteria scores from the two or more prepared test grading [templates] according to the test scoring rules from the grading guidelines for the written response test and/or course to compute a test score for the written response test; and
[present] a grading report comprising at least: the test score for the written response test, the plurality of criteria scores, or descriptions for the grading criteria from the customized scoring rubric for the written response test.
Accordingly, the limitations identified above recite an abstract idea since the limitations correspond to at least to mental processes, which is part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)).
For instance, considering claim 1 as an example, a human—e.g., a teacher—can practically perform the following mentally and/or using a pen and paper:
the teacher gathers, for one or more courses that the school is proving, course and test materials; wherein the test material comprises a written response test;
the teacher also gathers one or more evaluation materials that the teacher uses for evaluating one or more constructed responses, wherein such evaluation materials include:
a standardized scoring rubric for a plurality of tests and different courses, wherein the standardized scoring rubric is the same for the plurality of courses;
grading guidelines comprising test scoring rules for the written response test or course;
the teacher then drafts, based on the evaluation materials above, two or more templates, including a customized scoring rubric for the written response test or course based on the standardized scoring rubric, wherein the customized scoring rubric comprises at least a list of different grading criteria, customized descriptions for each grading criteria based at least on subject matter of the written response test and/or course, and criteria score levels for each grading criteria;
the teacher then collects, from a learner, a written response for a test in a course that the learner is taking;
the teacher then analyzes, using each of the two or more templates, the learner’s written response; wherein each template is associated with a single grading criteria from the customized scoring rubric, so that the teacher analyzes the written response based on the single grading criteria from the customized scoring rubric for the written response test and/or course and to generate a criteria score for the single grading criteria;
the teacher then combines, using a scoring template/formula, the plurality of criteria scores above according to the test scoring rules from the grading guidelines for the written response test and/or course to compute a test score for the written response test;
the teacher finally presents, verbally and/or using a pen and paper, a grading report comprising at least: the test score for the written response test, the plurality of criteria scores, or descriptions for the grading criteria from the customized scoring rubric for the written response test.
The observation above demonstrates that the claims do recite an abstract idea; namely, a mental process (e.g., an evaluation, an observation, and/or a judgment process, etc.).
Step 2A [Wingdings font/0xE0] Prong Two:
The claim(s) recite additional element(s), wherein a computer that executes machine-learning models is utilized to facilitate the recited functions/steps with respect to: collecting/storing information regarding course materials, test materials and scoring materials (e.g., “obtaining course material and test materials for a plurality of different courses offered by an academic institution, wherein the test materials comprises a written response test; obtaining a standardized scoring rubric for a plurality of tests and different courses, wherein the standardized scoring rubric is the same for the plurality of courses; obtaining grading guidelines comprising test scoring rules for the written response test or course”); analyzing the collected information using ML algorithms (e.g., “analyzing the course materials and test materials using a prepared rubric customizer machine learning model (MLM) configured to generate a customized scoring rubric for the written response test or course based on the standardized scoring rubric, wherein the customized scoring rubric comprises at least a list of different grading criteria, customized descriptions for each grading criteria based at least on subject matter of the written response test and/or course, and criteria score levels for each grading criteria”); collecting user input provided in the form of a written response (e.g., “obtaining a written response from a learner for a test in a course taken by the learner”); analyzing the user’s input using the ML algorithms to determine one or more results (e.g., “analyzing the written response using two or more prepared test grading MLM agents executing in parallel with each other, wherein each test grading MLM agent is associated with a single grading criteria . . . combining, by a scoring engine, a plurality of criteria scores from the two or more prepared test grading MLM agents according to the test scoring rules from the grading guidelines for the written response test and/or course to compute a test score for the written response test”); generating/displaying one or more of the results (e.g., “generating, for display on a user interface (UI), a grading report comprising at least: the test score for the written response test, the plurality of criteria scores, or descriptions for the grading criteria from the customized scoring rubric for the written response test”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a patent-eligible practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Accordingly, when each of the claims is considered as a whole, the additional element(s) fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
Although the specification states that “. . . scoring module 102 to perform parallel processing of two or more MLM agents. The benefit of having separate MLM agents for each particular criteria is a decrease in hallucinations compared to using a general MLM agent for multiple grading criteria and an increase of speed” (see [0052], emphasis added).
However, the implementation of parallel processing, which executes two or more machine-learning models, is already part of the existing computer/network technology. Thus, the benefits that the specification is alleging (e.g., the alleged “decrease in hallucinations”, and the alleged “increase of speed”) appear to be inherent features of the existing computer/network technology. In this regard, except for the generic remark above, the specification fails to demonstrate any new or advanced feature(s) that supposedly provide a technological improvement over the relevant existing technology.
Thus, at least for the reasons above, the generic remark above appears to be a preemptive assertion in an attempt to avoid a potential challenge under section §101.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B:
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
In addition, the use of the conventional computer/network technology to facilitate the analysis of collected information, including the process of implementing one or more machine-learning models to generate one or more grade results, based on the analysis of one or more written/constructed responses collected from one or more users or students, etc., is already directed to a well-understood, routine, conventional activity in the art (e.g., US 2018/0349476; US 2015/0248608, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7, 9-14 and 16-20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
Prior Art
● Considering each of claims 1, 8 and 15 as a whole (including their respective deponent claims), the prior art does not teach or suggest the current claims.
(a) Higgins (US 2015/0248608) appears to be one of the closest references to the current claims. Higgins teaches a computer-based system for automatically analyzing and scoring a constructed response received from a user ([0021]; [0023]); and such automatic scoring is achieved using one or more machine-learning algorithms—such as convolutional neural networks ([0029]; [0030]), which are trained based on relevant training data ([0035]; [0045]; [0071]).
However, Higgins fails to address the claimed features regarding the standardized scoring rubric for a plurality of tests and different courses and the guidelines that comprise test scoring rules. Instead, Higgins appears to rely merely on a plurality of human-scored constructed responses for optimizing the machine-learning model ([0045]). Of course, given the lack of teaching regarding the standardized scoring rubric, Higgins also fails to teach the limitation regarding the prepared rubric customizer
machine learning model, which is configured to generate a customized scoring rubric for the written response test or course based on the standardized scoring rubric.
Similarly, Higgins also suggests a parallel processing scheme where one or more processors of a single computer (or that of multiple computers) execute one or more models (e.g., see [0089]). Although the above indicate the technology that the current claims are implementing, it still fails to teach or suggest the two or more prepared test grading MLM agents that are executed in parallel, wherein each test grading MLM agent is associated with a single grading criteria from the customized scoring rubric, etc.
Thus, at least for the reasons above, Higgins fails to teach or suggest the current claims.
(b) Carmeli (US 2018/0349476) is another reference relevant to the current claims. Carmeli teaches a computer-based system for automatically evaluating and scoring thesis ([0019]; [0023]), wherein the system implements one or more marine-learning models—such as, one or more neural networks that are trained based on a set of training examples ([0038]; [0044]; [0045]); and accordingly, once the system receives a thesis to be scored ([0056]; [0057]), the thesis is analyzed and a pertinent score is generated ([0064]; [0071]).
However, quite similar to the deficiencies noted per Higgins above, Carmeli also fails to teach the claimed features regarding the standardized scoring rubric for a plurality of tests and different courses, including the guidelines that comprise test scoring rules. The above lack of standardized scoring rubric confirms that Carmeli also lacks the claimed prepared rubric customizer machine learning model, which is configured to generate a customized scoring rubric for the written response test or course based on the standardized scoring rubric.
Of course, besides suggesting a distributed cloud computing environment where different tasks are performed using various computing devices over a communication network (see [0091]), Carmeli also suggests that two or more of the functions, which the system is executing during the scoring process, can be executed concurrently (e.g., see [0111]). The teaching above demonstrates the technology that the current claims are implementing. However, again quite similar to the case of Higgins, Carmeli also fails to teach or suggest the two or more prepared test grading MLM agents that are executed in parallel, wherein each test grading MLM agent is associated with a single grading criteria from the customized scoring rubric, etc.
In addition, given the common deficiencies noted per Higgins and Carmeli, it is worth noting that the combined teaching (if any) of Higgins and Carmeli also fails to render any of the claims obvious over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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