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 .
2. 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.
Claim Rejections - 35 USC § 101
3. 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 14 as the representative claims, the following claimed limitations recite an abstract idea:
receive [a] notification to evaluate a participant test including video with video frames, audio, timestamped screenshots, and initial test results of the participant answering multiple test questions, the initial test results indicating which test questions were answered correctly, incorrectly, or not answered;
transcribe the audio;
generate a set of text for each screenshot;
compare the set of text from each timestamped screenshot to determine which screenshots are associated with each test question;
determine a set of time periods for each question based on the timestamps of the screenshots associated with each test question;
segment the [transcript], the screenshots, and the video frames by question based on the determined set of time periods;
select a screenshot and a video frame for each test question from the segmented screenshots and video frames;
generate an ideal answer for each test question;
utilize the ideal answer, the segmented [transcript], the selected screenshot and the selected video frame, for determining whether the participant demonstrated mastery of each correctly answered test question thereby indicating subject matter performance;
utilize the ideal answer, the segmented transcribed audio, the selected screenshot and the selected video frame, for determining whether the participant indicated knowledge gaps for each incorrectly answered test question;
utilize the segmented [transcript], the selected screenshot and the selected video frame, for determining whether the participant indicated antipatterns for each incorrectly answered test question;
generate a coaching report including the mastery determinations, the knowledge gap determinations and the antipattern determinations; and
provide the coaching report to the participant.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to mental processes and/or certain methods of organizing human activity, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). In particular, given the limitations identified above, the current claims correspond at least to a mental process since the limitations can practically be performed in the human mind. For instance, considering claim 1 as a representative claim, a human—such as a teacher—can perform the following limitations mentally and/or using a pen and paper as follows:
the teacher receives a verbal and/or a written notification to evaluate a test document that relates to a participant; wherein the test document includes plurality of content items (i.e., video, audio, timestamped screenshots) and various pieces of information, including: initial test results of the participant answering multiple test questions, the initial test results indicating which test questions were answered correctly, incorrectly, or not answered;
the teacher, while listening to the audio, transcribes the audio—i.e., writes on a paper the words and/or phrases that the teacher recognizes, etc.;
the teacher further writes, while observing/reading each of the images or screenshots, a corresponding set of text for each of the images/screenshots;
the teacher then compares the set of text from each timestamped screenshot in order to determine the screenshots that are associated with each test question;
the teacher also determines, based on the timestamps of the screenshots associated with each test question, a set of time periods for each question;
the teacher then segments, based on the set of time periods determined above, the transcription, the screenshots and the video frames by question;
the teacher selects screenshot and a video frame for each test question from the segmented screenshots and video frames;
the teacher drafts, using a model/template (e.g., a textbook, a a solution manual, etc.) an ideal answer for each test question;
the teacher then analyzes, using the ideal answer above, each of various items above (i.e., the segmented transcription, the selected screenshot and the selected video frame) in order to determine whether:
the participant demonstrated mastery of each correctly answered questions—thereby, indicating subject matter performance;
the participant indicated knowledge gap for each incorrectly answered test question;
the participant indicated antipatterns for each incorrectly answered questions;
the teacher then drafts—on a paper—a coaching report that includes information regarding the determinations of: the mastery, the knowledge gap and the antipattern;
the teacher then provides the coaching report above to the participant, etc.
Thus, it is evident from the observation above that the current claims do recite an abstract idea—namely, a mental process (e.g., an observation, an evaluation and/or a judgement process).
Note also that depending on the scenario, such as the teacher being in the same room where the participant is taking the test, the teacher may perform one or more of the features above concurrently; such as, while observing the actions (and paying attention to the verbal expressions) that the participant is making, the teacher can make a judgment about the accuracy of the participant’s answer (e.g., by comparing the participant’s answer to a model answer that the teacher readily recognizes, or a model answer that the prepared), etc..
Step 2A [Wingdings font/0xE0] Prong Two:
The claim(s) recite additional element(s), wherein a computer-based system, which executes one or more algorithms—including AI models—is utilized to facilitate the recited functions/steps regarding: collecting information that is in the form of video, audio, and/or text format (e.g., “receiving notification to automatically evaluate a participant test including video with video frames, audio, timestamped screenshots, and initial test results of the 6 participant answering multiple test questions, the initial test results indicating which test questions were answered correctly, incorrectly, or not answered”); analyzing the collected information using one or more algorithms (e.g., “transcribing the audio; utilizing optical character recognition (OCR) on the screenshots to generate a set of text for each screenshot; comparing the set of text from each timestamped screenshot to determine which screenshots are associated with each test question . . . utilizing a second AI tool, with the ideal answer, the segmented transcribed audio, the selected screenshot and the selected video frame, for determining whether the participant demonstrated mastery of each correctly answered test question thereby indicating subject matter performance . . . utilizing the second AI tool, with the segmented transcribed audio, the selected screenshot and the selected video frame, for determining whether the participant indicated antipatterns for each incorrectly answered test question”; generating one or more pertinent results based on the analysis above (e.g., “generating a coaching report including the mastery determinations, the knowledge gap determinations and the antipattern determinations; and providing the coaching report to the participant”), 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.
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 process of evaluating content items, including executing one or more machine-learning and/or AI models to extract and/or analyze images/screenshots, videos, audio/speech data, etc., is already directed to a well-understood, routine, conventional activity in the art (e.g., see US 2016/0358632; US 2015/0058012, 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, 9-13 and 15-20). 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
4. Considering each of claims 1, 8 and 14 (including their respective dependent claims), the prior art does not teach of suggest the current claims.
(a) Venezky (US 11,523,759) appears to be one of the closest references to the current claims. Venezky teaches a system/method for monitoring a student, as the student is interacting with a lesson material—such as, answering a question on a test (col.4, lines 58-65). The system incorporates a camera to capture video of the student as the student is interacting with the lesson material; wherein the system also executes an algorithm—such as an AI—to analyze the captured data; and thereby: (i) extracts biometric and gaze data, including matching the extracted data to what was displayed on the screen (col.5, lines 54-67), (ii) determines one or more attributes (e.g., cues and reactions) regarding the student, and further (iii) provides the student with pertinent feedback—such as, informing the student regarding time spent on each question, how the student did on each topic, etc. (col.6, lines 9-51).
Venezky also suggests the process of analyzing a screenshot since the biometric and gaze data of the student is synchronized with data that was on the screen at the time of capture (col.5, lines 60-67); and furthermore, the above is utilized to provide the student with relevant feedback—such as, encouraging the student to reread the passage when answering the questions (col.6, lines 1-8). However, Venezky does not positively indicate whether an OCR is performed on the image/screenshot in order generate a set of text, including determining which screenshots are associated with each test question (i.e., by comparing the set of text from each timestamped screenshot). Venezky also fails to teach the process of transcribing audio/speech even though the student could be speaking during the video (col.5, lines 54-59). Of course, as a result of the above deficiency, Venezky does not segment a transcribed audio by question based on a predetermined set of time periods, etc.
Thus, at least for the reasons above, Venezky fails to teach or suggest the claims as currently presented.
(b) Khadka (US 2025/0078513) is also a relevant reference to the current claims. Khadka teaches an AI based system/method for proctoring online exams ([0017]), wherein the system captures and analyzes, during a time a participant (e.g., student) is taking an exam, video and audio data in order to determine one or more issues, including: determining, based on the analysis of the captured video, whether the student is looking at—or looking away from—the screen; determining, based on the captured audio/speech, whether the audio/speech is that of the student, etc. ([0020] to [0022]). Of course, one or more of the analysis above is based on: (i) extracting video frames from the captured video and associating one or more of the frames with a corresponding timestamp ([0031]; [0032]), (ii) segmenting the captured audio and performing a speaker verification by comparing the audio to a reference audio ([0044]), etc.
However, despite the implementation above to capture video and audio data relating to a participant who is taking a test, Khadka fails capture screenshots, which can be analyzed—via an OCR—to extract a set of text. Of course, due to the above deficiency, Khadka also fails to teach the process of determining which screenshots are associated with each test question, etc. Moreover, since Khadka is directed to monitoring the participant, as opposed to providing feedback to the participant, Khadka also fails to teach: (i) segmenting the transcribed audio by question; (ii) generating an ideal answer to each question; (iii) evaluating whether the participant being monitored is demonstrating—or fails to demonstrate—mastery of each question, etc.
Thus, due to the deficiencies pointed out above, Khadka fails to teach or suggest the current claims as currently presented.
Note also that given the missing features discussed above regarding each of the references, the combined teaching of the references (if any) also fails to render the current 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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