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 .
Allowable Subject Matter
In re claims 1–10, Examiner determined that these claims would be in condition for allowance. While thoroughly investigating the claimed language, Examiner could not find the related prior art: e.g., the second limitation recites interconnection of input and output layers of two datasets in machine learning model; and the third limitation recites two functions, i.e., a training loss function and an accuracy function in parallel datasets. (emphasis added) Referring to Applicant’s Spec. ¶40 and Fig. 5, Applicant describes a connection between a training dataset and a validation dataset after processing an accuracy function in the machine learning; Examiner was not able to find whether prior art teaches two distinct functions to correlate two corresponding datasets, however.1
Therefore, claims 1–10 are allowed.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 11–12, 14–15 and 19–20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Anderson et al. (U.S. 10,769,571 B2).
Regarding claim 11, Anderson discloses an examination proctor computer system comprising:
one or more processors; and (Fig. 2, 204 processing units)
a memory storing instructions executable in the one or more processors, the instructions encoding a trained artificial intelligence (AI) neural network that is instantiated in the one or more processors, the instructions further causing the one or more processors to implement operations comprising: (Fig. 2, 218 memory)
receiving, from a candidate computing device (a candidate computing device construed as a client device 106) that is interconnected with the examination proctor computing system (the examination proctor computing system construed as a server 102) within a distributed network computing system, a set of timestamped images transmitted from the candidate computing device, the set of timestamped images associated with performance of a skills based examination performed by an examination candidate; and (Per Fig. 1A, Anderson’s client devices 106—which are used by exam proctors to supervise test-takers—acquire images. Anderson col. 3 lines 39–57. [c]lient devices 106 used at least by test-takers, and in some embodiments also those used by proctors and other administrators, may require a webcam or other image capture device that captures images and/or video of the client device 106 user and his immediate surroundings.)
generating, in association with the trained AI neural network,2 a candidate performance profile (a candidate performance profile construed environment validation process) based at least in part on the set of timestamped images. (Per Fig. 4A, Anderson’s environment processor 480 analyzes a sequence of images to evaluate a testing environment 480. Ibid. col. 18 lines 38–62. [t]he environment processor 480 may obtain one or more frames of the video feed, which may consist of a sequence of images, and compare these frames to stored thresholds or to each other to evaluate the testing environment 450.)
Regarding claim 19, Anderson discloses a computer-readable non-transitory memory having instructions stored thereon, the instructions when executed in one or more processors causing the one or more processors to implement operations comprising:
receiving, from a candidate computing device that is interconnected with the examination proctor computing system within a distributed network computing system, a set of timestamped images transmitted from the candidate computing device, the set of timestamped images associated with performance of a skills based examination performed by an examination candidate; and (Per Fig. 1A, Anderson’s client devices 106—which are used by exam proctors to supervise test-takers—acquire images. Anderson col. 3 lines 39–57. [c]lient devices 106 used at least by test-takers, and in some embodiments also those used by proctors and other administrators, may require a webcam or other image capture device that captures images and/or video of the client device 106 user and his immediate surroundings.)
generating, in association with the trained AI neural network, a candidate performance profile based at least in part on the set of timestamped images. (Per Fig. 4A, Anderson’s environment processor 480 analyzes a sequence of images to evaluate a testing environment 480. Ibid. col. 18 lines 38–62. [t]he environment processor 480 may obtain one or more frames of the video feed, which may consist of a sequence of images, and compare these frames to stored thresholds or to each other to evaluate the testing environment 450.)
Regarding claim 12, Anderson discloses the examination proctor computing system further comprising assigning, to the examination candidate, an examination performance result based at least in part upon the candidate performance profile. (Per Fig. 4A, Anderson’s environment processor 480 discloses a machine learning engine where results of testing environments 450 are assessed. Anderson col. 22 lines 13–36. The machine learning engine may report results of its analysis to the environment processor 480,)
Regarding claim 14, Anderson discloses the examination proctor computing system wherein the training dataset is provided based on pre-processing of a plurality of human action videos. (Per Fig. 4A, Anderson’s environment processor 480 discloses a sequence of images in video data. Anderson col. 19 lines 9–20. The determination of a contrast or contrast ratio of one or more frames of a sequence of images in video data may be determined using well-established video processing and analysis technologies.)
Regarding claim 15, Anderson discloses the examination proctor computing system wherein the pre-processing includes extraction of video frames in accordance with at least one of: (i) predetermined time intervals (Per Fig. 6B, Anderson’s proctor manager 620 discloses historical data 612 over various time periods. Anderson col. 30 lines 47–57. [t]he historical data 612 may include traffic trends over various time periods;) and (ii) human action motion detection, wherein temporal dynamics of the human action motions constituted in the human action videos are captured. (Anderson discloses human actions by incorporating images into geometrical shapes. Ibid. col. 37 lines 15–41. [t]he generated images incorporating the geometrical shapes may be incorporated into a gaming environment, in which the proctor user or another user looks for simple patterns or actions that may, in turn, be indicative of a cheating or IP theft event.)
Regarding claim 20, it has been rejected in the same manner as claim 12.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 16–18 are rejected under 35 U.S.C. § 103 as being unpatentable over Anderson in view of Lu (CN 112995150 A).
Regarding claim 16, Anderson fails to specifically disclose the examination proctor computing system wherein the AI neural network comprises a fusion of a convolutional neural network (CNN) and a long short term memory (LSTM) neural network.
In related art, Lu discloses the examination proctor computing system wherein the AI neural network comprises a fusion of a convolutional neural network (CNN) and a long short term memory (LSTM) neural network. (Lu’s detection model comprises a CNN and a LSTM. Lu Spec. ¶46. A detection model is constructed, which includes a convolutional neural network (CNN), a long short-term memory (LSTM) network, a feature fusion module, and a fully connected layer.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Lu into the teachings of Anderson to improve accuracy of object detection. Ibid. ¶6.
Regarding claim 17, Anderson as modified by Lu, discloses the examination proctor computing system wherein the CNN performs spatial feature extraction and the LSTM neural network captures temporal dependencies. (Lu discloses that his CNN is used for spatial feature extraction and the LSTM for temporal feature extraction. Lu Spec. ¶46. The CNN is used for spatial feature extraction, and the LSTM is used for temporal feature extraction.)
Regarding claim 18, Anderson as modified by Lu, discloses the examination proctor computing system wherein the fusion comprises sequentially feeding the output of the CNN as input into the LSTM network, enabling the AI neural network to contemporaneously learn spatial and temporal features of the human action motions. (Lu’s detection model comprises a CNN and a LSTM. Lu Spec. ¶46. A detection model is constructed, which includes a convolutional neural network (CNN), a long short-term memory (LSTM) network, a feature fusion module, and a fully connected layer.)
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chu (U.S. 11,989,940 B2) discloses a movie detection system and a method.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-17:00 (EST).
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 For example, Sivakumar et al. (U.S. 12,561,398 B2) discloses training datasets, whereas he is silent on whether these datasets are related to human action images.
2 Anderson’s platform discloses machine learning engine where environment validation process is rendered. See his col. 22 lines 13–36. [t]he platform 400 may include a machine learning engine implemented on any of the servers 402, 412 and executing one or more machine learning algorithms to evaluate the data pertaining to a testing environment validation process.
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