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 .
Status of the Claims
Claims 1-14 and 16-21 are pending. Claim 15 has been cancelled.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1, 5-7, 9-10, 13-14 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Reyzin (US 20170161905 A1) in view of Fan et al. (US 20160063344 A1, hereinafter Fan).
Concerning claim 1, Reyzin teaches a method for left-behind object detection, comprising:
acquiring a first to-be-detected image of a target region at a first moment (¶0066 & ¶¶0098-0099: video capture device capturing at least one image of a scene);
determining whether a foreground image exists in the first to-be-detected image according to a foreground image determination model (fig. 2: step 300; ¶0098), the foreground image being an image corresponding to a foreground object in the target region (fig. 2: step 302; ¶0099);
in a case where the foreground image exists in the first to-be-detected image and the foreground image satisfies a first preset condition (¶0099: “only contiguous foreground areas greater than a certain size (ex: by number of pixels) are identified as an object in the scene”), and the first preset condition includes at least one of: a ratio of an area of the foreground image to an area of the first to-be-detected image being greater than a first threshold, and a number of pixels of the foreground image being greater than a second threshold (¶0099: “only contiguous foreground areas greater than a certain size (ex: by number of pixels) are identified as an object in the scene”); and
detecting whether the foreground object is an object left behind in the target region (¶0104). Not explicitly taught is the method, comprising: inputting the foreground image and at least one comparison image into a preset tracking model to obtain at least one tracking result, wherein the at least one comparison image is an image of the target region within a second time period, the second time period is a time period after the first moment, and detecting whether the foreground object is an object left behind in the target region according to the at least one tracking result.
Fan, in the same field of endeavor, teaches long-term static object detection, wherein the foreground image and at least one comparison image into a preset tracking model to obtain at least one tracking result (fig. 2: steps S265 & S270; ¶¶0046-0049: a template for the detected object is used to track the object’s presence throughout a set of images that represent a time period), wherein the at least one comparison image is an image of the target region within a second time period, the second time period is a time period after the first moment (¶0043; ¶¶0046-0049: the set of images represent a time period, therefore the object detected in at least one of the images is compared to the remaining images in the set to determine if the object is still present), and detecting whether the foreground object is an object left behind in the target region according to the at least one tracking result (fig. 2: S275 & ¶0052: an alert is generated when the object is present in the set of images for an amount of time greater than a predefined threshold). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate long-term static object detection features of Fan into the Reyzin invention in order to determine the amount of time corresponding to an object’s presence in a set of images.
Concerning claim 5, Fan further teaches the method according to claim 1, wherein the at least one tracking result includes at least one of a first parameter value (¶¶0046-0049: weighting values), a range of a tracking image, or a tracking position; wherein the tracking image is a sub-image with a highest similarity to the foreground image in the comparison image, the first parameter value is used to indicate a similarity between the foreground image and the tracking image (¶¶0046-0049: the weighting values indicate how similar the template is throughout the image set), and the range of the tracking image is a region occupied by the tracking image in the comparison image, and the tracking position is a position of the tracking image in the comparison image.
Concerning claim 6, Fan further teaches the method according to claim 5, wherein the detecting whether the foreground object is an object left behind in the target region according to the at least one tracking result includes:
in a case where the at least one tracking result satisfies a second preset condition, determining that the foreground object is the object left behind in the target region, the second preset condition including at least one restrictive condition that corresponds to the at least one tracking result (¶¶0050-0051: detecting false positive such as a lighting change condition); and
in a case where any one of the at least one tracking result does not satisfy the second preset condition, determining that the foreground object is not the object left behind in the target region (¶¶0050-0051: ignoring the match when an abnormality is detected).
Concerning claim 7, Fan further teaches the method according to claim 6, wherein the determining that the foreground object is the object left behind in the target region in a case where the at least one tracking result satisfies a second preset condition includes:
in a case where the first parameter value is greater than a third threshold (¶0062: comparing the weight of a pixel to a threshold), and/or the range of the tracking image is greater than a fourth threshold, and/or the tracking position is within at least one preset range in the comparison image, determining that the foreground object is the object left behind in the target region (¶0062: Classifying the pixel as static. A static region (or object) is detected if the majority of pixels in the region become static.).
Concerning claim 9, Reyzin further teaches the method according to claim 1, comprising:
in a case where the foreground image does not exist in the first to-be-detected image, or the foreground image does not satisfy the first preset condition, or the foreground object is not the object left behind in the target region, updating the foreground image determination model (¶¶0142-0143).
Concerning claim 10, Reyzin further teaches the method according to claim 9, wherein the updating the foreground image determination model in a case where the foreground image does not exist in the first to-be-detected image, or the foreground image does not satisfy the first preset condition, or the foreground object is not the object left behind in the target region includes:
determining an updated image, the updated image being the first to-be-detected image; and updating the foreground image determination model according to the updated image to obtain an updated foreground image determination model (¶¶0142-0144).
Concerning claim 13, Reyzin further teaches the method according to claim 1, further comprising:
acquiring a second to-be-detected image of a target region at a second moment (¶0066 & ¶¶0098-0099: video capture device capturing at least one image of a scene), the second moment being a moment after the first moment (¶0066 & ¶¶0098-0099: video capture indicates capturing a sequence of images therefore the second to-be-detected image may be the next image in the sequence);
determining whether a foreground image exists in the second to-be-detected image according to a foreground image determination model (fig. 2: step 300; ¶¶0098-0099);
in a case where the foreground image exists in the second to-be-detected image and the foreground image satisfies a first preset condition (¶0099: “only contiguous foreground areas greater than a certain size (ex: by number of pixels) are identified as an object in the scene”), and the first preset condition includes at least one of: a ratio of an area of the foreground image to an area of the first to-be-detected image being greater than a first threshold, and a number of pixels of the foreground image being greater than a second threshold (¶0099: “only contiguous foreground areas greater than a certain size (ex: by number of pixels) are identified as an object in the scene”); and
detecting whether the foreground object is an object left behind in the target region (¶0104). Not explicitly taught is the method, comprising: inputting the foreground image and at least one second comparison image into a preset tracking model to obtain at least one tracking result, wherein the at least second comparison image is an image of the target region within a third time period, the third time period is a time period after the second moment, and detecting whether the foreground object is an object left behind in the target region according to the at least one tracking result.
Fan, in the same field of endeavor, teaches long-term static object detection, wherein the foreground image and at least one second comparison image into a preset tracking model to obtain at least one tracking result (fig. 2: steps S265 & S270; ¶¶0046-0049: a template for the detected object is used to track the object’s presence throughout a set of images that represent a time period), wherein the at least one second comparison image is an image of the target region within a third time period, the third time period is a time period after the first moment (¶0043; ¶¶0046-0049: the set of images represent a time period, therefore the object detected in at least one of the images is compared to the remaining images in the set to determine if the object is still present), and detecting whether the foreground object is an object left behind in the target region according to the at least one tracking result (fig. 2: S275 & ¶0052: an alert is generated when the object is present in the set of images for an amount of time greater than a predefined threshold). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate long-term static object detection features of Fan into the Reyzin invention in order to determine the amount of time corresponding to an object’s presence in a set of images.
Concerning claim 14, Fan teaches the method according to claim 1, further comprising: in a case where the foreground object is the object left behind in the target region, outputting prompt information (fig. 2: S275 & ¶0052).
Concerning claim 16, Reyzin teaches a left-behind object detection apparatus, comprising a processor and a communication interface, the communication interface being coupled to the processor, and the processor being used to run a computer program or instructions to implement the method for left-behind object detection according to claim 1 (¶¶0009-0011).
Concerning claim 17, Reyzin teaches a left-behind object detection system, comprising a left-behind object detection apparatus and at least one camera device, the left-behind object detection apparatus being used to perform the method for left-behind object detection according to claim 1 (figs. 1A-1B: systems 100 and/or 200).
Concerning claim 18, Reyzin teaches a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium has stored thereon instructions that, when executed by a computer, cause the computer to perform the method for left-behind object detection according to claim 1 (¶¶0009-0011).
Concerning claim 19, Reyzin teaches a computer program product being stored on anon-transitory computer-readable storage medium and comprising computer program instructions that, when executed by a computer, cause the computer to perform the method for left-behind object detection according to claim 1 (¶¶0009-0011).
Concerning claim 20, teaches the apparatus according to claim 16, further comprising a memory for storing the computer program or the instructions (¶0062).
Concerning claim 21, teaches the apparatus according to claim 16, wherein the left-behind object detection apparatus is a chip (¶¶0071-0073).
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Reyzin (US 20170161905 A1) in view of Fan et al. (US 20160063344 A1, hereinafter Fan) and Porikli et al. (US 20080247599 A1, hereinafter Porikli).
Concerning claim 2, Fan further teaches the method according to claim 1, wherein the foreground image determination model includes one or more sub-models corresponding to a position of each pixel in a background image of the target region, and the background image does not include the foreground image (¶0051: the background of an image is determined through background subtraction; ¶0062: Gaussian model with three distributions representing background pixels, static pixels, and moving pixels). Not explicitly taught is determining whether a foreground image exists in the first to-be-detected image according to a foreground image determination model includes: detecting whether each first pixel of the first to-be-detected image matches one or more sub-models of a corresponding second pixel, the second pixel being a pixel corresponding to a position of the first pixel in the background image; in a case where at least one first pixel does not match one or more sub-models of a corresponding second pixel, determining that the foreground image exists in the first to-be- detected image; and in a case where all first pixels match sub-models of corresponding second pixels, determining that the foreground image does not exist in the first to-be-detected image.
Porikli, in the same field of endeavor, teaches a method for detecting objects left-behind in a scene, wherein determining whether a foreground image exists in the first to-be-detected image according to a foreground image determination model includes:
detecting whether each first pixel of the first to-be-detected image matches one or more sub-models of a corresponding second pixel, the second pixel being a pixel corresponding to a position of the first pixel in the background image (fig. 2 & ¶¶0031-0034: long-term foreground and short-term foreground models);
in a case where at least one first pixel does not match one or more sub-models of a corresponding second pixel, determining that the foreground image exists in the first to-be- detected image (fig. 2 & ¶0034); and
in a case where all first pixels match sub-models of corresponding second pixels, determining that the foreground image does not exist in the first to-be-detected image (fig. 2 & ¶0034). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Porikli into the Reyzin in view of Fan invention in order to accurately detect left-behind objects against erratic motion of people in the scene, illumination change, camera vibration, and other artifacts (Porikli, ¶0083).
Concerning claim 3, Porikli further teaches the method according to claim 2, wherein the detecting whether each first pixel of the first to-be-detected image matches one or more sub-models of a corresponding second pixel includes:
determining a parameter value of any first pixel of the first to-be-detected image and a parameter interval of each sub-model of one or more sub-models of a second pixel corresponding to the first pixel (¶¶0051-0052: statistical information regarding the mean and the variance from the probability distributions);
in a case where the parameter value of the first pixel is within a parameter interval of a first sub-model, determining that the first pixel matches the first sub-model, the first sub-model being at least one sub-model of the one or more sub-models of the second pixel corresponding to the first pixel (¶0074: determining if pixels are inside or outside of the 99% confidence interval); and
in a case where the parameter value of the first pixel is outside the parameter interval of the first sub-model, determining that the first pixel does not match the first sub-model (¶0074: determining if pixels are inside or outside of the 99% confidence interval).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Reyzin (US 20170161905 A1) in view of Fan et al. (US 20160063344 A1, hereinafter Fan) and Zhao et al. (US 20180211104 A1, hereinafter Zhao).
Concerning claim 8, Reyzin in view of Fan teaches the method of claim 1. Reyzin further teaches the method, wherein before the inputting the foreground image and at least one comparison image into a preset tracking model to obtain at least one tracking result, the method further comprises: acquiring one or more background images (fig. 2: step 300). Not explicitly taught is the method, determining a background sub-image of each background image of the one or more background images, a position of the background sub-image in the background image corresponding to a position of the foreground image in the first to-be-detected image; inputting the foreground image and the one or more background sub-images into a verification model to obtain at least one second parameter value, the second parameter value is used to indicate a similarity between the foreground image and the background sub-image; and in a case where the at least one second parameter value is less than a fifth threshold, determining that the foreground image exists in the first to-be-detected image.
Zhao, in a similar field of endeavor, teaches a method for tracking a target, comprising:
determining a sub-image of each image of the one or more images (¶0006; ¶0055),
a position of the sub-image in the image corresponding to a position of the foreground image in the first to-be-detected image (¶0006; ¶0055: the tracking ROI corresponds to the location of the target throughout the images);
inputting the foreground image and the one or more sub-images into a verification model to obtain at least one second parameter value, the second parameter value is used to indicate a similarity between the foreground image and the sub-image (¶¶0068-0071: verifying the status of the tracked target); and
in a case where the at least one second parameter value is less than a fifth threshold, determining that the foreground image exists in the first to-be-detected image (¶¶0068-0071: verifying the status of the tracked target). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Zhao into the Reyzin in view of Fan invention and track a specific region of interest (i.e., background sub-image) to locate an object of interest. Such a modification would reduce processing times of the system.
Allowable Subject Matter
Claims 4 and 11-12 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.
Response to Arguments
Applicant’s arguments, see page 2 of the remarks, filed 05/08/2026, with respect to restriction requirement have been fully considered and are persuasive. The restriction requirement has been withdrawn and all pending claims are under examination.
Conclusion
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/James M Anderson II/Primary Examiner, Art Unit 2425
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