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 .
DETAILED ACTION
RESPONSE TO ARGUMENTS
112f INTERPRETATION
The examiner acknowledges the amendment of claims 1 & 19-20 & addition of claims 21-22 filed 07/13/2026. After carefully reviewing applicant amendments, 35 USC 112f guidance and applicant arguments, 112f interpretation is respectfully maintained 112(f) interpretation.
TERMINAL DISCLAIMER
The terminal disclaimer filed on 07/13/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent 11,068,703 has been reviewed and is accepted. The terminal disclaimer has been recorded.
In view of terminal disclaimer approval, double patenting rejection is overcome.
PRIOR ART REJECTION
The examiner acknowledges the amendment of claims 1 & 19-20 & addition of claims 21-22 filed 07/13/2026. Applicants arguments filed on (07/13/2026) have been fully considered but are deemed moot in view of new grounds of rejection. Due to the variation in claim scope via amendments a new ground of rejection is proper.
ALLOWABLE SUBJECT MATTER
Claims 5, 9, 16-17 & 20 are 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.
CLAIM REJECTIONS - 35 USC § 103
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 of this title, 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 1, 10-15 & 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Boregowda et al. (U.S. Publication 2007/0206865) in view of Abramov et al. (U.S. Publication 2003/0063082)
As to claims 1 & 19, Boregowda discloses a data processing device for detecting motion in a sequence of frames each comprising one or more blocks of pixels ([0001, 0022-0025] discloses video motion detection and describes using neighborhood blocks of pixels in successive frames. [0024] discloses building a Gaussian mixture for a block of pixels. [0025] discloses processes that block using data accumulated over N frames.), the data processing device comprising: a sampling unit configured to determine image characteristics at a set of of a block in a current frame of said sequence ([0025, 0029] disclose supplying the block based pixel image data and processing of a block from the current incoming frame); a feature generation unit configured to: form a current feature for the block in dependence on the determined image characteristics ([0029] discloses computing from the incoming current frame, the block mean and block variance of the intensity values for the RGB channels.), the current feature having a plurality of values derived from the sample points ([0025, 0029] discloses 3x3 block produces 9N data points per color component, while [0029] computes at least the block mean and block variance from the block intensity values.); and motion detection logic configured to generate a motion output for the block by comparing the current feature for the block to the learned feature for the block having a plurality of values representing historical feature values for the block in frames of said sequence previous to said current frame ([0022-0029] discloses initialization/adaptation across a plurality of frames produces historical motion data.); wherein the feature generation unit is further configured to maintain the learned feature for the block by: decaying the plurality of values of the learned feature; and allocating the values of the current feature to the corresponding values of the learned feature. ([0012] discloses the background subtraction method of video motion detection (VMD) maintains a background reference and classifies pixels in the current frame as either background or foreground by comparing them against the background reference. The background can be either an image or a set of statistical parameters (e.g. mean, variance, median of pixel intensities. [0013] discloses an exponential forgetting scheme is often followed – See B(x, y, T). [0024] discloses building a gaussian mixture for a block of pixels, e.g. a 3 by 3 neighborhood of pixels, rather than for a single pixel. [0025] discloses a certain number of frames N are stored for initialization purposes and there is then a total 9N data points per color component to be clustered into three normal (Gaussian) distributions). [0019] discloses the gaussian mixture uses two parameters Alpha, which is the learning constant, and T, which is the proportion of data to be accounted for by the background. [0029] discloses from the incoming frame, and in particular, a block of pixels in the incoming frame, the block mean and block variance of the intensity values are computed. The Lp distance between the new distribution and the existing distributions are computed. Based on the minimum distance, the new distribution is classified or labeled as background. Otherwise, the unmatched distribution is replaced with the new distribution. The weight for the new distribution is then computed. The parameter update for mean and variance are done. )
Boregowda is silent to randomly or pseudo-randomly selected sample points.
However, Abramov discloses randomly or pseudo-randomly selected sample points. ([0045-0047, 0054-0056])
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda’s disclosure to include the above limitations in order to a set of sample locations that is more evenly distributed across the block while controlling the number of image locations that must be processed.
As to claim 10, Boregowda in view of Abramov discloses everything as disclosed in claim 1 but is silent to wherein the sampling unit is configured to randomly generate the set of sample points of the block, the set of sample points being fixed for the block over the sequence of frames.
However, Abramov discloses wherein the sampling unit is configured to randomly generate the set of sample points of the block, the set of sample points being fixed for the block over the sequence of frames. ([0045] discloses sample points are generated using a Halton sequence. [0046] discloses s-dimensional coarse Halton sequence may be used to define sample points)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov’s disclosure to include the above limitations in order to support repeatable temporal comparison of feature values for that block over successive frames.
As to claim 11, Boregowda in view of Abramov discloses everything as disclosed in claim 1 but is silent to wherein the sampling unit is configured to randomly generate the set of sample points of the block according to a pseudo-random Halton sequence.
However, wherein the sampling unit is configured to randomly generate the set of sample points of the block according to a pseudo-random Halton sequence. ([0045] discloses sample points are generated using a Halton sequence. [0046] discloses s-dimensional coarse Halton sequence may be used to define sample points)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov’s disclosure to include the above limitations in order to produce a well distributed deterministic/pseudo random set of sample points for block analysis.
As to claim 12, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses wherein the feature generation unit is configured to decay the plurality of values of the learned feature according to a predefined decay factor. ([0013] discloses an exponential forgetting scheme is often followed. See Equation.)
As to claim 13, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses wherein the feature generation unit is configured to weight the values of the current feature according to a predefined weighting factor prior to allocating the values of the current feature to the values of the learned feature. ([0014] discloses each distribution is parameterized by a weight factor. [0019] discloses a learning constant. [0029] discloses the weight for the new distribution is then computed and that parameter updates are then performed.)
As to claim 14, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses wherein the comparison performed by the motion detection logic comprises forming an estimate as to whether, based on a measure of differences between the values of the current feature and the corresponding values of the learned feature, and to within a predefined or adaptive threshold, the current feature is expected based on the historical values of the learned feature of the block. ([0012] discloses segmentation can accomplished through simple distance measures. [0019] discloses T is the proportion of data to be accounted for by the background (i.e. the background threshold). [0029] discloses The Lp Distance between new distribution and the existing distributions are computed and that classification is then performed based on that result.)
As to claim 15, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses wherein the motion detection logic is configured to generate the motion output so as to indicate motion at the block if the estimate exceeds the predefined or adaptive threshold. ([0029] discloses based on the minimum distance, the new distribution is classified or labeled as background. Otherwise, the unmatched distribution is replaced with the new distribution.)
As to claim 18, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses the data processing device further comprising decision logic configured to generate an indication of motion for a frame based on the one or more motion outputs generated by the motion detection logic in respect of the blocks of that frame. ([0029] discloses after block segmentation is completed, morphological operations (median filter, dilation and erosion) are applied across the whole frame.)
Claims 2-4 & 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over
Boregowda et al. (U.S. Publication 2007/0206865) in view of Abramov et al. (U.S. Publication 2003/0063082) as applied in claim 1 above further in view of in view of DEMOULIN et al. (U.S. Publication 2013/0343654)
As to claim 2, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses block based motion detection using image statistics for a block. ([0024] discloses building a gaussian mixture for a block of pixels 3x3. [0029] discloses from the incoming frame a block of pixels in the incoming frame, the block mean and variance are computed)
Boregowda in view of Abramov is silent to forming for each current feature value from a comparison of a pair of sample points, with each value representing the comparison result for the respective pair.
However, DEMOULIN discloses forming each current feature value from a pairwise comparison result. ([0019] discloses a first specified number of features may be identified within the image patch 200. The features may be groups of pixels. [0020] discloses mean intensity difference tests may then be performed for the features within the image patch 200. A mean intensity difference test t performed for pixel groups, or features X and Y in patch P may be defined. [0021] discloses according to Eq. 1 PX and PY represent the mean intensities for the pixel groups X and Y, respectively. [0022] discloses the results of the mean intensity differences test T for the first specified number L0 of features may be used to generate an L0-dimensional binary string.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov’s disclosure to include the above limitations in order to form a compact current feature for each block while still comparing that block feature over time for motion detection.
As to claim 3, Boregowda in view of Abramov & DEMOULIN discloses everything as disclosed in claim 2. In addition, Boregowda discloses a learned historical feature/model over prior frames. ([0012] discloses that the background can be a set of statistical parameters (e.g. mean, variance, median of pixel intensities. [0019] discloses pixel/block values are modeled over time and that gaussian mixture model uses two parameters; Alpha, learning constant and T. Also see [0022 & 0029]])
Boregowda in view of Abramov & DEMOULIN is silent to learned historical feature values are pair specific or that each current value formed for a specific pair of sample points has a corresponding historical value for that same pair.
However, DEMOULIN discloses learned historical feature values are pair specific or that each current value formed for a specific pair of sample points has a corresponding historical value for that same pair. ([0020] discloses a comparison test is performed for pixel groups or features x and y. [0022] discloses the results of the mean intensity differences test may be used to generate an Lo dimensional binary string. [0023-0024] discloses repeated binary string generation for corresponding sub-patches from repeated feature tests.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov & DEMOULIN’s disclosure to include the above limitations in order to compare like with like over time same pairwise feature test.
As to claim 4, Boregowda in view of Abramov & DEMOULIN discloses everything as disclosed in claim 2 but is silent to wherein each value of the current feature comprises a bit as a binary representation of the result of the comparison by the motion detection logic for the respective pair of sample points.
However, DEWMOULIN discloses wherein each value of the current feature comprises a bit as a binary representation of the result of the comparison by the motion detection logic for the respective pair of sample points. ([0016] discloses the binary descriptor may be generated using binary strings or bit strings. [0022] discloses the results of the mean intensity differences tests may be used to generate an L0-dimensional binary string. [0030] discloses performing mean intensity difference test and generating a binary string based on those tests.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov & DEMOULIN’s disclosure to include the above limitations in order to reduce feature storage and comparison cost.
As to claim 6, Boregowda in view of Abramov & DEMOULIN discloses everything as disclosed in claim 2 but is silent to wherein the feature generation unit is configured to, for each value of the current feature, represent the result of the comparison with a binary value indicating which of the image characteristics of the sample points of the pair is greater in value.
However, DEWMOULIN discloses wherein the feature generation unit is configured to, for each value of the current feature, represent the result of the comparison with a binary value indicating which of the image characteristics of the sample points of the pair is greater in value. ([0020] discloses a mean intensity difference test performed for pixel groups or features, X and Y in Patch P may be defined. [0021] discloses PX and PY represent the mean intensities fro the pixel groups X and Y respectively)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov & DEMOULIN’s disclosure to include the above limitations in order to represent each pairwise comparison in a one bit compact form.
As to claim 7, Boregowda in view of Abramov & DEMOULIN discloses everything as disclosed in claim 2 but is silent to wherein the historical value at each of the values of the learned feature is a weighted average of the results of comparisons by the feature generation unit for the respective pair of sample points of the block over a plurality of frames.
However, DEMOULIN discloses wherein the historical value at each of the values of the learned feature is a weighted average of the results of comparisons by the feature generation unit for the respective pair of sample points of the block over a plurality of frames. ([0020-0022] discloses the pairwise comparison outputs used as feature values in the binary string.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov & DEMOULIN’s disclosure to include the above limitations in order to maintain a historical weighted average for each pairwise comparison position over a plurality of frames.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Boregowda et al. (U.S. Publication 2007/0206865) in view of Abramov et al. (U.S. Publication 2003/0063082) as applied in claim 1 above further in view of in view of U S et al. (U.S. Publication 2014/0098999)
As to claim 8, Boregowda in view of Abramov discloses everything as disclosed in claim 1. In addition, Boregowda discloses block based motion detection using sample data from a block over multiple frames. ([0024-0025] discloses block based motion detection using sample data from a block over multiple frames.)
Boregowda is silent to the current feature and learned feature are histograms with bins/counts.
However, U S discloses the current feature and learned feature are histograms with bins/counts. ([0016] discloses using a histogram based motion detection method. [0017] discloses building a separate histogram for each of the blocks. [0018] discloses histograms of the same blocks in each of the training image frames can be used to build the initial histogram model for that block.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Boregowda in view of Abramov’s disclosure to include the above limitations in order to represent block image characteristics in a bin based count form while still performing temporal motion detection over blocks.
CONCLUSION
No prior art has been found for claims 5, 9, 16-17 & 20 in their current form.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen P Coleman whose telephone number is (571)270-5931. The examiner can normally be reached Monday-Thursday 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675
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