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 .
Notice to Applicants
This communication is filed in response to the action filed on 05/02/2024.
Claims 1-12 are pending.
Information Disclosure Statement
The information disclosure statements (IDS’s) both filed on 05/02/2024 have both been considered.
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 non-obviousness.
Claims 1-12 are rejected under 35 § U.S.C. 103 as being obvious over US 12,561,840 B1 to GRANSKOG et al. (hereinafter “GRANSKOG”) in view of US 11,430,132 B1 to GLASSNER et al. (hereinafter “GLASSNER”).
As per claim 1, GRANSKOG discloses a computer implemented method for determining a color of a tracked object (a computing system and method for tracking objects and determining pixel color values related to said object in the RGB color spectrum; abstract; column 3, line 2 - column 4, line 34), comprising the steps of: providing a first video sequence depicting a scene, the first video sequence comprising a plurality of image frames (the system is adapted to receive image frames from a video sequence recorder which is a camera device, and captures vise of a scene comprising an environment; figs 3-4B and fig 9; column 3, line 2 - column 4, line 65; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 21, lines 1-11); for each image frame, detecting foreground objects (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images; abstract; column 3, line 2 - column 4, line 34); for each area of a plurality of areas in the scene, analyzing the first video sequence and calculating an object color probability vector associated with the area of the scene, wherein each value in the object color probability vector relates to a color from a set of predefined colors and indicates a probability that a foreground object located in the area of the scene has the color (the computing system is adapted to for each region depicted in the scene comprising tracked objects determine a motion vector and probability value for a target pixel of a tracked object and further can include generating adjusted weightings between current and previous color values for pixel locations that are determined to correspond to motion vector difference regions that have a probability of image artifacts/objects; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); for each area, calculating a measurement of variability of the probabilities indicated by the object color probability vector associated with the area of the scene, and associating the measurement of variability with the area of the scene (the computing system is adapted to find a value relating to the variation in probabilities and can be shown as a Gaussian filtering ratio for each region and the variable is expressed as a ratio for example a large ratio e.g., 9x up sampling process can result in a relatively jagged or low resolution appearing image, as there will in general be three pixel by three pixel blocks that share a same color value, further a down sampling filter can be applied, such as may involve 2x2 down sampling with a regular box filter, although other filters and ratios can be utilized as well; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); providing a second video sequence depicting the scene (the camera is adapted to capture video and image frame sequences and would be adapted to capture a second video sequence subsequently following the first; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; claim 1); tracking a foreground object in the second video sequence, the tracked foreground object being located in a first area of the plurality of areas in the scene in a first image frame of the second video sequence, and in a second different area of the plurality of areas in the scene in a second image frame of the second video sequence (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images in the first and second video sequence depicting the regions of interest; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 34, lines 1-59); determining a first set of colors of the tracked foreground object in the first image frame, and determining a second different set of colors of the tracked foreground object in the second image frame (the computing system is adapted to track an object and determine its colors in the RGB spectrum and is adapted to track a first color of the objects pixel and a second set of colors of an objects pixel wherein the colors differ; figs 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59; column 35, lines 59-column 36 line 64); and upon determining that the measurement of variability associated with the first area of the scene is lower than the measurement of variability associated with the second area of the scene, determining that the color(s) of the tracked foreground object is the first set of colors, and otherwise determining that the color(s) of the tracked foreground object is the second set of colors (a process 460 for generating an image can be performed as illustrated in FIG. 4C. In at least one embodiment, one or more temporal variations of a first color of one or more pixels can be determined at step 462 of the tracked object in the first and second video sequence, and can also further determine using temporal variations can be provided 464 as input to one or more neural networks, along with color data and other feature inputs and provides one or more second colors of these one or more pixels can be determined 466 using these one or more neural networks based, adapted for color recognition; figs 3-4C, 9, and 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59), wherein the first and second video sequence is captured by a camera with a same field of view in the scene, wherein an area of the scene corresponds to a same pixel region in the image frames of the first and second video sequence (the computing system includes a fixed position video camera which has the same field of view when capturing all video frames and video sequences; fig 9; column 9, line 15-column 10, line 57; column 21, lines 1-60; column 23, lines 1-43; column 39, lines 8-53). GRANSKOG fails to disclose or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, wherein the method further comprises the step of: determining a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom.
GLASSNER discloses or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, wherein the method further comprises the step of: determining a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom (the computing system adapted for pixel color determination is adapted to capture multiple frames of video sequences in order to track an object and is adapted to perform camera parameter/feature adjustments which means zooming and rotating of camera view to better see pixels of the objects of interest; figs 5-6, 8, 9; column 5, lines 1-2; column 9, lines 5-64; column 10, lines 63-67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GRANSKOG to have camera parameters comprising one or more of: pan, tilt, roll or zoom of GLASSNER reference. The Suggestion/motivation for doing so would have been to provide camera parameters so that the camera may move and or zoom in order to more effectively track objects frame to frame by providing adjustable field of vision as suggested by column 9, lines 5-54 of GLASSNER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GLASSNER with GRANSKOG to obtain the invention as specified in claim 1.
As per claim 2, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses further comprising the steps of: labelling all occurrences of the tracked foreground object in the second video sequence with the determined color(s) (the object that is being tracked has its pixels assigned a pixel value in the RGB spectrum for color value which acts as a label in order to tell the user what color within RGB the pixel represents; column 2, line 63-column 3, line 66; column 4, lines 4-22; column 10, lines 13-57).
As per claim 3, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein each of the first and second set of colors comprises one of: a single color value (the computing system is adapted to compute and assign a color value of a single pixel/point can be determined and is assigned as a first color value; abstract; fig 4b; column 3, lines 2-29; column 11, lines 1-53); a plurality of color values (the computing system is further adapted to compute and assign a color value for a plurality of pixels/points is determined in relation to the tracked object region and can apply a second color to said pixel if determined; abstract; abstract; fig 4b; column 3, lines 2-29; column 4, lines 4-22; column 11, lines 1-53); or a plurality of color values, each color value associated with a probability that the tracked object has the color ( and is further adapted to in some circumstances after applying color values to a plurality of pixels generate/adjust weightings between current and previous color values for the plurality of pixel locations that are determined to correspond to motion vector difference regions that have a probability of image artifacts/objects; abstract; figs 3-4B; column 3, lines 2-29; column 4, lines 4-22; column 10, lines 13-57).
As per claim 4, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein each area of the scene corresponds to a single pixel coordinate in the image frames of the first and second video sequence (images of a specific resolution will have pixels grouped based on the resolution and color value will be determined and assigned based on the center pixel of the pixels encompassed by the area determined by the user in the example provided the resolution was up sampled 4X of the captured frames of the first or second video sequence captured via the camera; column 13, lines 3-35).
As per claim 5, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein the step of detecting foreground objects in the image frame comprises: determining a location and an extent of each detected foreground object in the image frame, wherein the location and extent comprises one of: a pixel mask, or a bounding box (in order to identify objects which, comprise pixels of interest the user finds the X,Y position of each point/pixel and finds these using a color variance masking techniques; column 10, lines 13-58; column 11, lines 20-54).
As per claim 6, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein the step of analyzing the first video sequence comprises, for each foreground object located in the area of the scene in an image frame of the first video sequence: determining one or more colors of the foreground object from pixel data depicting the foreground object in the image frame (the tracked object which would be in the foreground of the captured images includes a first color of a set of one or more pixels is found at step 462, and second color of those pixels may be determined at step 466; abstract; figs 4A-C; column 11, lines 3-19); and using the determined one or more colors when calculating the object color probability vector associated with the area of the scene (a neural network can accept both temporal and spatial variations as inputs for determining pixel color values, such as may be used in determining how to weight two or more color values to be blended for the particular set of pixels and includes a probability of image artifacts; abstract; figs 3-4B; column 3, lines 2-29; column 4, lines 4-22; column 10, lines 13-57; column 11, lines 3-19).
As per claim 7, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein the step of analyzing the first video sequence comprises, for each foreground object located in the area of the scene in an image frame of the first video sequence: receiving a plurality of color values, each color value associated with a probability that the foreground object has the color in the image frame (a neural network can accept both temporal and spatial variations as inputs for determining pixel color values, such as may be used in determining how to weight two or more color values to be blended for the particular set of pixels and includes a probability of image artifacts; abstract; figs 3-4B; column 3, lines 2-29; column 4, lines 4-22; column 10, lines 13-57; column 11, lines 3-19); and using the plurality of color values and their associated probabilities when calculating the object color probability vector associated with the area of the scene (the first and second color values of the pixels are considered when determining probability of artifacts in the captured vides image frames; abstract; figs 3-4B; column 3, lines 2-29; column 4, lines 4-22; column 10, lines 13-57; column 11, lines 3-19).
As per claim 8, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein the step of calculating an object color probability vector for an area of the plurality of areas in the scene comprises detecting at least a threshold number of foreground objects in the area of the scene (the computing system has a threshold minimum of one object in the frames to be tracked or the method for tracking object(s) would not be performed if there was not at least one obj3ect and therefore the threshold is one object; column 2, line 63-column 3, line 66).
As per claim 9, GRANSKOG in view of GLASSNER discloses the method of claim 1. Modified GRANSKOG further discloses wherein the first video sequence is captured during a first time period of a day (the camera is adapted to capture video and image frame sequences and would be adapted to capture a first video during a desired first time period determined to be suitable by the user and applied as desired; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; column 55, lines 12-14; claim 1), wherein the second video sequence is captured during a second time period of a subsequent day, wherein the second time period is entirely encompassed within the first time period (the computing system is further adapted to capture videos and subsequent images or video frames in a sequence and would be programed by the user via camera parameters to capture the second video subsequently a day after the first and may choose to include the second video time period within the first; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; column 55, lines 12-14; claim 1).
As per claim 10, GRANSKOG in view of GLASSNER discloses the method of claim 9. Modified GRANSKOG further discloses wherein the measurement of variability is at least one of: variance, standard deviation, mean absolute deviation, median absolute deviation or coefficient of variations (the computing system measures variability of the performed method using a plurality of methods including a variance mask, standard deviations, and mean value averaging; column 3, lines 2-44; column 5, lines 26-47).
As per claim 11, GRANSKOG discloses a system comprising: one or more processors (a computing system and method for tracking objects and determining pixel color values related to said object in the RGB color spectrum the computing system comprising a memory and processor component; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35); and one or more non-transitory computer-readable media storing first computer executable instructions that, when executed by the one or more processors (the computing system comprising a memory and processor component which store and execute data, programs, and instructions; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35), cause the system to perform actions comprising: providing a first video sequence depicting a scene, the first video sequence comprising a plurality of image frames (the system is adapted to receive image frames from a video sequence recorder which is a camera device, and captures vise of a scene comprising an environment; figs 3-4B and fig 9; column 3, line 2 - column 4, line 65; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 21, lines 1-11); for each image frame, detecting foreground objects in the image frame (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images; abstract; column 3, line 2 - column 4, line 34); for each area, analyzing the first video sequence and calculating an object color probability vector associated with the area of the scene, wherein each value in the object color probability vector relates to a color from a set of predefined colors and indicates a probability that a foreground object located in the area of the scene has the color (the computing system is adapted to for each region depicted in the scene comprising tracked objects determine a motion vector and probability value for a target pixel of a tracked object and further can include generating adjusted weightings between current and previous color values for pixel locations that are determined to correspond to motion vector difference regions that have a probability of image artifacts/objects; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); for each area, calculating a measurement of variability of the probabilities indicated by the object color probability vector associated with the area of the scene, and associating the measurement of variability with the area of the scene (the computing system is adapted to find a value relating to the variation in probabilities and can be shown as a Gaussian filtering ratio for each region and the variable is expressed as a ratio for example a large ratio e.g., 9x up sampling process can result in a relatively jagged or low resolution appearing image, as there will in general be three pixel by three pixel blocks that share a same color value, further a down sampling filter can be applied, such as may involve 2x2 down sampling with a regular box filter, although other filters and ratios can be utilized as well; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); providing a second video sequence depicting the scene (the camera is adapted to capture video and image frame sequences and would be adapted to capture a second video sequence subsequently following the first; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; claim 1); tracking a foreground object in the second video sequence, the tracked foreground object being located in a first area of the plurality of areas in the scene in a first image frame of the second video sequence, and in a second different area of the plurality of areas in the scene in second image frame of the second video sequence (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images in the first and second video sequence depicting the regions of interest; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 34, lines 1-59); determining a first set of colors of the tracked foreground object in the first image frame, and determining a second different set of colors of the tracked foreground object in the second image frame (the computing system is adapted to track an object and determine its colors in the RGB spectrum and is adapted to track a first color of the objects pixel and a second set of colors of an objects pixel wherein the colors differ; figs 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59; column 35, lines 59-column 36 line 64); upon determining that the measurement of variability associated with the first area of the scene is lower than the measurement of variability associated with the second area of the scene, determining that the color(s) of the tracked foreground object is the first set of colors, and otherwise determining that the color(s) of the tracked foreground object is the second set of colors (a process 460 for generating an image can be performed as illustrated in FIG. 4C. In at least one embodiment, one or more temporal variations of a first color of one or more pixels can be determined at step 462 of the tracked object in the first and second video sequence, and can also further determine using temporal variations can be provided 464 as input to one or more neural networks, along with color data and other feature inputs and provides one or more second colors of these one or more pixels can be determined 466 using these one or more neural networks based, adapted for color recognition; figs 3-4C, 9, and 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59), wherein the first and second video sequence is captured by a camera with a same field of view in the scene, wherein an area of the scene corresponds to a same pixel region in the image frames of the first and second video sequence (the computing system includes a fixed position video camera which has the same field of view when capturing all video frames and video sequences; fig 9; column 9, line 15-column 10, line 57; column 21, lines 1-60; column 23, lines 1-43; column 39, lines 8-53). GRANSKOG fails to disclose or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, and wherein a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene is determined using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom.
GLASSNER discloses or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, and wherein a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene is determined using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom (the computing system adapted for pixel color determination is adapted to capture multiple frames of video sequences in order to track an object and is adapted to perform camera parameter/feature adjustments which means zooming and rotating of camera view to better see pixels of the objects of interest; figs 5-6, 8, 9; column 5, lines 1-2; column 9, lines 5-64; column 10, lines 63-67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GRANSKOG to have camera parameters comprising one or more of: pan, tilt, roll or zoom of GLASSNER reference. The Suggestion/motivation for doing so would have been to provide camera parameters so that the camera may move and or zoom in order to more effectively track objects frame to frame by providing adjustable field of vision as suggested by column 9, lines 5-54 of GLASSNER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GLASSNER with GRANSKOG to obtain the invention as specified in claim 11.
As per claim 12, GRANSKOG discloses a system comprising a color matching system (a computing system and method for tracking objects and determining pixel color values related to said object in the RGB color spectrum the computing system comprising a memory and processor component; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35) and a forensic search application (the forensic search application would be integrated using neural network models with adjustable weighted parameters which would be trained to accomplish substantially the same tasks as the forensic search model/system claimed and would be stored in the computing systems memory and executed by its processors; figs 9 and 32; column 12, lines 2-56), wherein the color matching system comprises: one or more processors (the computing system comprising a memory and processor component which store and execute data, programs, and instructions; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35); and one or more non-transitory computer-readable media storing first computer executable instructions that, when executed by the one or more processors (the computing system comprising a memory and processor component which store and execute data, programs, and instructions; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35), cause the system to perform actions comprising: providing a first video sequence depicting a scene, the first video sequence comprising a plurality of image frames (the system is adapted to receive image frames from a video sequence recorder which is a camera device, and captures vise of a scene comprising an environment; figs 3-4B and fig 9; column 3, line 2 - column 4, line 65; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 21, lines 1-11); for each image frame of the plurality of image frames, detecting foreground objects in the image frame (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images; abstract; column 3, line 2 - column 4, line 34); for each area, analyzing the first video sequence and calculating an object color probability vector associated with the area of the scene, wherein each value in the object color probability vector relates to a color from a set of predefined colors and indicates a probability that a foreground object located in the area of the scene has the color (the computing system is adapted to for each region depicted in the scene comprising tracked objects determine a motion vector and probability value for a target pixel of a tracked object and further can include generating adjusted weightings between current and previous color values for pixel locations that are determined to correspond to motion vector difference regions that have a probability of image artifacts/objects; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); for each area, calculating a measurement of variability of the probabilities indicated by the object color probability vector associated with the area of the scene, an associating the measurement of variability with the area of the scene (the computing system is adapted to find a value relating to the variation in probabilities and can be shown as a Gaussian filtering ratio for each region and the variable is expressed as a ratio for example a large ratio e.g., 9x up sampling process can result in a relatively jagged or low resolution appearing image, as there will in general be three pixel by three pixel blocks that share a same color value, further a down sampling filter can be applied, such as may involve 2x2 down sampling with a regular box filter, although other filters and ratios can be utilized as well; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56); providing a second video sequence depicting the scene (the camera is adapted to capture video and image frame sequences and would be adapted to capture a second video sequence subsequently following the first; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; claim 1); providing a second video sequence depicting the scene (the camera is adapted to capture video and image frame sequences and would be adapted to capture a second video sequence subsequently following the first; column 3, lines 2-64; column 4, lines 8-65; column 48, lines 52-67; claim 1); tracking a foreground object in the second video sequence, the tracked foreground object being located in a first area of the plurality of areas in the scene in a first image frame of the second video sequence, and in a second different area of the plurality of areas in the scene in second image frame of the second video sequence (the system is adapted to perform object tracking by tracking object feature points, and detects objects that are in the foreground of the captured images in the first and second video sequence depicting the regions of interest; abstract; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 12, lines 1-56; column 34, lines 1-59); determining a first set of colors of the tracked foreground object in the first image frame, and determining a second different set of colors of the tracked foreground object in the second image frame (the computing system is adapted to track an object and determine its colors in the RGB spectrum and is adapted to track a first color of the objects pixel and a second set of colors of an objects pixel wherein the colors differ; figs 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59; column 35, lines 59-column 36 line 64); upon determining that the measurement of variability associated with the first area of the scene is lower than the measurement of variability associated with the second area of the scene, determining that the color(s) of the tracked foreground object is the first set of colors, and otherwise determining that the color(s) of the tracked foreground object is the second set of colors (a process 460 for generating an image can be performed as illustrated in FIG. 4C. In at least one embodiment, one or more temporal variations of a first color of one or more pixels can be determined at step 462 of the tracked object in the first and second video sequence, and can also further determine using temporal variations can be provided 464 as input to one or more neural networks, along with color data and other feature inputs and provides one or more second colors of these one or more pixels can be determined 466 using these one or more neural networks based, adapted for color recognition; figs 3-4C, 9, and 13-14B; column 3, line 2 - column 4, line 34; column 5, line 27-column 6, line 38; column 11, lines 3-19; column 12, lines 1-56; column 34, lines 1-59); receiving a search request comprising a first color value (assign a color value of a single pixel/point can be determined and is assigned as a first color value; abstract; fig 4b; column 3, lines 2-29; column 10, lines 13-57; column 11, lines 1-53); determining that the first color value matches a color determined for the foreground object (the color value is compared to a second color feature detected of the pixels and neural network models are used to “blend” the color to arrive at the true color determination for the tracked object and may have color filter applied to further alter color; abstract; fig 4b; column 3, lines 2-29; column 10, lines 13-57; column 11, lines 1-53); and returning a search response based at least in part on the foreground object (abstract; fig 4b; column 3, lines 2-29; column 10, lines 13-57; column 11, lines 1-53); wherein the forensic search application comprises: one or more processors (the forensic search application would be integrated using neural network models with adjustable weighted parameters which would be trained to accomplish substantially the same tasks as the forensic search model/system claimed and would be stored in the computing systems memory and executed by its processors; figs 9 and 32; column 12, lines 2-56); and one or more non-transitory computer-readable media storing second computer executable instructions that, when executed by the one or more processors (the computing system comprising a memory and processor component which store and execute data, programs, and instructions; abstract; column 3, line 2 - column 4, line 34; column 16, lines 3-35), cause the forensic search application to perform actions comprising: providing the search request comprising the first color value to the color matching system (to extract a first color value from the first tracked object of the first image frames of the captured video sequence based on the determination and input of a frame comprising an object to be tracked; abstract; fig 4b; column 3, lines 2-29; column 10, lines 13-57; column 11, lines 1-53); receiving a search response from the color matching system (finding a color match based on analyzing the pixels and pulling two color features and performing a weighted blending operation performed via neural network models which substantially act like a forensics model and are ran operated and stored on the computing system; abstract; fig 4b; column 3, lines 2-29; column 10, lines 13-57; column 11, lines 1-53); and displaying data from the search response to a user (the data is output to a display and user interface to be viewed by the user; column 38, lines 35-54), wherein the first and second video sequence is captured by a camera with a same field of view in the scene, wherein an area of the scene corresponds to a same pixel region in the image frames of the first and second video sequence (the computing system includes a fixed position video camera which has the same field of view when capturing all video frames and video sequences; fig 9; column 9, line 15-column 10, line 57; column 21, lines 1-60; column 23, lines 1-43; column 39, lines 8-53). GRANSKOG fails to disclose or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, and wherein a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene is determined using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom.
GLASSNER discloses or wherein the first and second video sequence is captured by a camera with a changing field of view in the scene, and wherein a pixel region in an image frame from the first or the second video sequence that corresponds to an area of the scene is determined using camera parameters, the camera parameters comprising one or more of: pan, tilt, roll or zoom (the computing system adapted for pixel color determination is adapted to capture multiple frames of video sequences in order to track an object and is adapted to perform camera parameter/feature adjustments which means zooming and rotating of camera view to better see pixels of the objects of interest; figs 5-6, 8, 9; column 5, lines 1-2; column 9, lines 5-64; column 10, lines 63-67).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GRANSKOG to have camera parameters comprising one or more of: pan, tilt, roll or zoom of GLASSNER reference. The Suggestion/motivation for doing so would have been to provide camera parameters so that the camera may move and or zoom in order to more effectively track objects frame to frame by providing adjustable field of vision as suggested by column 9, lines 5-54 of GLASSNER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GLASSNER with GRANSKOG to obtain the invention as specified in claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 10,122,940 B2
US 7,136,524 B1
US 7,760,942 B2
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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