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 .
Election/Restrictions
Applicant’s election without traverse of species II pertaining to the figure 3, claims 16-30 in the reply filed on 8/14/2025 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 16-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites the limitation "the 1st test" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the said learner specific trainable block" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the 2nd test" in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the period" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites 1st video data in lines 7 and 11, that’s make it unclear if these video data are same or different, furthermore it’s unclear the antecedent basis of the recitation “the said 1st video data” in line 12.
Also, claim 16 recites 2nd video data in lines 8 and 13, that’s make it unclear if these video data are same or different, furthermore it’s unclear the antecedent basis of the recitation “the 2nd video data” in line 15.
Claim 17 recites the limitation "the 1st and 2nd tests" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation “the 1st test” in lines 3 and 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the 2nd test" in line 3 and 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation “the 1st test” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation “the 1st test” and “the monitor screen” in line 2 and 3 respectively. There is insufficient antecedent basis for this limitation in the claim.
Claim 22 recites the limitation “the rear view” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 27 recites the limitation “the said body parts” and “the eye pupil” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 28 recites the limitation “the said body parts” and “the head” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 29 recites the limitation “the said body parts” and “the upper body” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 30 recites the limitation “the said body parts” and “the shoulder” in line 2. There is insufficient antecedent basis for this limitation in the claim.
All dependent claims are rejected as well as they depend on the rejected independent claim 16.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 16, 20-21, 23-25 and 27-30 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al (US Pub. 2023/0261894) in view of Liao et al (CN 115424166 A).
With respect to claim 16, Srivastava discloses A method for detecting online test cheating, the method comprising:
a trainable machine comprising:
learner agnostic block; learner specific block that is cascaded to the said learner agnostic block, (see figure 3, numerical 210, one or more neural network models);
Pre-training the said trainable machine using a plurality of datasets, each of the dataset (see paragraph 0015, wherein … the system may be configured to train a machine learning (ML) model for an attendee (such as a student or an employee) based on attention levels of the attendee determined in different meeting sessions “dataset” (such as classrooms or meetings either in an offline or in online mode)…) comprising:
time varying label data that indicates cheating for the period of time cheating behavior is occurring, (see figure 3, numerical 302A data acquisition, this is read as obtaining the 1st and 2nd video using camera during examination [see paragraph 0130]; see paragraph 0015, wherein … Hence, the disclosed system may propose a distinctive learning model for each student “time varying label data that indicates” (i.e., an attendee) by processing all historical data (related to the attention scores) generated from series of classroom activities over a period of time…; and see paragraph 0017, wherein … Moreover, the disclosed system may be also capable to detect malpractices (like cheating during an examination “cheating behavior is occurring”) that may be done by the attendees…);
capturing test taker’s 1st video data during the 1st test; fine tuning the said learner specific trainable block using the said 1st video data, (see paragraph 0097, wherein … In an embodiment, the disclosed system 102 may fine tune “fine tuning” the attention score (i.e. calculated based on the focus and interaction scores as per equation (4)) using different influential factors for the attentiveness of the attendee …);
capturing test taker’s 2nd video data during the 2nd test; extracting movement information of at least one body part of the test taker from the 2nd video data; feeding the said extracted movement information into the said trainable machine; making the cheating decision output from the said trainable machine, (see paragraph 0130, wherein … In another embodiment, the disclosed system 102 may be capable to identify one or more malpractices that may be done by the first attendee 406 in the first meeting session. The one or more malpractices may include, but are not limited to, cheating, …(for example during examination), use of an unauthorized material in the examination hall, a usage of illegal and abusing gestures “body part” by an attendee, …In such scenarios, the circuitry 202 may be configured to determine a pattern in the detected first set of activities performed by as attendee (such as the first attendee 406). …the circuitry 202 may be configured to apply at least one of the plurality of ML models or one or more NN models 210 on the detected first set of activities to detect the malpractice. Based on the detected malpractice, the system 102 may be configured to generate a first notification for the first attendee 406 or for the educator of the first meeting session…), as claimed.
However, Srivastava fails to disclose Pre-training the said trainable machine using a plurality of datasets, each of the dataset comprising: 1st video data that doesn’t include cheating behavior; 2nd video data that may or may not include cheating behavior, (emphasis added), as claimed.
Liao teaches training Pre-training the said trainable machine using a plurality of datasets, each of the dataset comprising: 1st video data that doesn’t include cheating behavior; 2nd video data that may or may not include cheating behavior, (emphasis added; see Contents of the invention second paragraph, wherein … the present invention provides an exam cheat identification detection method, comprising: according to the positive sample and the negative sample, performing model training and model improvement to the YOLOV4 model to obtain the target model; wherein the positive sample is the video of the examinee making the normal action; the negative sample is the video of the examinee doing cheating action; through the camera fixed in the examination room, obtaining the video recording of the examination room…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of cheating detection using image analysis. Teaching of Liao to train neural networks using positive and negative i.e. cheating behavior and not include cleating behavior, can be incorporated in to Srivastava’s system as suggested (see Srivastava figure 3, numerical 210), for suggestion, and modifying the system yields test cheat judgement accurately (see Liao Content of the invention first paragraph), for motivation.
With respect to claim 20, combination of Srivastava and Liao further discloses the said trainable machine is a deep neural network, (see Srivastava figure 3, numerical 210), as claimed.
With respect to claim 21, combination of Srivastava and Liao further discloses more than one camera is used to capture test taker’s video data, (see Srivastava paragraph 0121, wherein … system 102 may receive the plurality of images 502 of the plurality of attendees 504A-504N from the plurality of image capture devices “cameras” 104…), as claimed.
With respect to claim 23, combination of Srivastava and Liao further discloses the said trainable machine is located in a central test server, (see Srivastava figure 2, system 102 linked with communication network i.e. the system is in central test server), as claimed.
With respect to claim 24 and 25, combination of Srivastava and Liao discloses all the elements as claimed in and rejected in claim 16 above. However, they fails to explicitly disclose wherein: the said trainable machine is located in each test taker’s local computer; and wherein: the said trainable machine is split between a central test server and each test taker’s local computer, as claimed in claims 24 and 25.
But, it is well known “Official Notice” in the art to have the processors or to process the data locally or distribute the processing (see US 2023/0222934 paragraph 0036-0037).
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the well-known conventional knowledge of processing data locally or distribute the process among the processors, to yield more convenient system to the user as required.
With respect to claims 27-30, combination of Srivastava and Liao further discloses wherein: the said body parts include the eye pupil; the said body parts include the head; the said body parts include the upper body; and the said body parts include the shoulder, (see Srivastava paragraph 0146, wherein …The detected one or more activities performed by each of the plurality of attendees may be associated with at least one of an action performed by an attendee, a gesture performed by the attendee, a head pose of the attendee, a body posture of the attendee, a lip movement of the attendee, a gaze “pupil” of the attendee, or a facial emotion of the attendee), as claimed in claims 28-30.
Claims 17-19, 22 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al (US Pub. 2023/0261894) in view of Liao et al (CN 115424166 A) as applied to claim 16 above, and further in view of Khadka et al (US Pub. 2025/0078513).
With respect to claim 17, combination of Srivastava and Liao discloses all the elements as claimed and rejected in claim 16 as above. However, they fail to specifically disclose wherein: the 1st and 2nd tests have the same format of test sheets; the 1st test is conducted earlier than the 2nd test; the score of the 1st test is not used to assess the test taker’s online learning achievement; the score of the 2nd test is used to assess the test taker’s online learning achievement, as claimed.
Khadka teaches wherein: the 1st and 2nd tests have the same format of test sheets; the 1st test is conducted earlier than the 2nd test; the score of the 1st test is not used to assess the test taker’s online learning achievement; the score of the 2nd test is used to assess the test taker’s online learning achievement, (see paragraph 0025, wherein student may provide sample images …used by the face matching …the gaze estimation …as reference to compare , this is read as the 1st test which is not assess; and paragraph 0029 wherein the launching the exam is read as the 2nd test which is assess as the learning achievement; also both are of same format as they are taken online), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of cheating detection using image analysis. Teaching of Khadka to attain samples of the individuals prior to taking exam can be incorporated in to Srivastava and Liao system as suggested (see Srivastava paragraph 0033 …trained in historical data…), for suggestion, and modifying the system yields an online proctoring system (see Khadka paragraph 0002), for motivation.
With respect to claim 18, combination of Srivastava, Liao and Khadka discloses all the elements as claimed in and rejected in claim 17 above. However, they fail to explicitly disclose discloses wherein: the 1st test includes at least one question that forces test takers to read sentences to move their eyes horizontally, as claimed.
But, it is well known “Official Notice” in the art to calibrate a system for before use in test cheating by pretesting for eye gazing (see WO 2023/041940 figure 17, numerical 1222).
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the well-known conventional knowledge of calibration of the system prior to the use to yield more accurate test cheating detection system.
With respect to claim 19, combination of Srivastava, Liao and Khadka discloses all the elements as claimed in and rejected in claim 17 above. However, they fail to explicitly disclose the 1st test includes at least one question that forces test takers to move their eyes to four corners of the monitor screen, as claimed.
But, it is well known “Official Notice” in the art to calibrate a system for before use in test cheating by pretesting for the four corner test (see US 2017/0039869 paragraph 0008).
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the well-known conventional knowledge of calibration of the system prior to the use to yield more accurate test cheating detection system.
With respect to claim 22, combination of Srivastava, Liao and Khadka further discloses at least one camera captures the rear view of the test taker, (see Khadka figure 1, numerical 132, a laptop with a camera captures the rear view of the test taker), as claimed.
With respect to claim 26, combination of Srivastava, Liao and Khadka further discloses each test taker’s computer includes a video data pre-processor, (see Khadka figure 1, numerical 132), as claimed.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663