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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/27/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7, 10-14, 16, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Tofolo et al (US Pub. 2016/0350922) in view of Lin et al (US Pub. 2022/0398747).
With respect to claim 1, Tofolo discloses a system, comprising:
an imager capturing a sequence of images of a ball in flight; and a processor configured to perform the following operations (see figure 2):
detect the ball in a first and second image from the sequence of images, (see paragraph 0006, wherein …method can include detecting the golf ball in a field of view of the camera…);
estimate an outline of the ball in each of the first and second images, (see figure 5, and paragraphs 58-59, wherein …The partitioning can generate the segmented image 530 where the image of the golf ball can be separated from other regions of the image…);
apply
compute three-dimensional spin parameters for the ball based on the pixel displacements in the
However, Tofolo fails to explicitly disclose apply a dense optical flow (DOF) model to compute a DOF map of pixel displacements between every pixel of at least a portion of the ball within the outline of the ball in the first image and every pixel of the portion of the ball within the outline of the ball in the second image, wherein the portion of the ball is selected without regard to markings on the ball, (emphasis added), as claimed.
Lin teaches a dense optical flow (DOF) model to compute a DOF map of pixel displacements between every pixel of at least a portion of the ball within the outline of the ball in the first image and every pixel of the portion of the ball within the outline of the ball in the second image, wherein the portion of the ball is selected without regard to markings on the ball, (emphasis added, see paragraph 0040, wherein …An optical flow map can include one or more optical flow vectors corresponding to motion between two frames. In some examples, an optical flow map can include an optical flow vector for each pixel (or approximately each pixel) within a frame. Optical flow maps that include an optical flow vector for each pixel (or approximately each pixel) within a frame can be referred to as dense optical flow maps; see paragraph 0058, wherein … the optical flow map 108 may indicate that a pixel located at pixel location L1 had an optical flow value (e.g., an optical flow vector) of V1. The supplemental history data 112 included in optical flow history 110 may include data classifying pixel P1 as part of a particular foreground object, such as a ball…; and see paragraph 0062, wherein …the flow target engine 102 may perform object detection or semantic segmentation and determine that pixel at location L4 in the current source frame is part of the same ball…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are solving similar problem of object detection in images using image analysis. The teaching of Lin i.e. use a dense optical flow map of a ball in order to obtain the movements between the two frames can be incorporated into Tofolo system as suggested (see Tofolo paragraph 0072, wherein … determining …displacement of the fiduciary markers in … frame), for suggestion, and modifying the system will yields a ball spin determination system by detecting more accurate image of the object “ball”, for motivation.
With respect to claim 2, combination of Tofolo and Lin further discloses wherein the imager is configured to crop the first and second images to eliminate portions of the images not including the ball prior to passing the first and second images to the processor, (see Tofolo figure 5 and paragraph 0060, wherein …the segmented image 530…pixel values 510 can be reintroduced over the ball region 540 while the background remains black…), as claimed.
With respect to claim 3, combination of Tofolo and Lin further discloses wherein the first and second images are cropped to center the ball at a same pixel location in each of a first cropped image and a second cropped image, (see Tofolo figure 5 the ball is center in the image, and see the segmentation in paragraph 0057, where the background is taken out other than the ball i.e. same pixels of the ball are segmented out), as claimed.
With respect to claims 4 and 5, combination of Tofolo and Lin further discloses wherein detecting the ball in the first and second images is based on a deep learning (DL) based ball model; and the DL based ball model is based on a segmentation network, (see Lin paragraph 0065, wherein … the flow target engine 102 can determine the initial estimated pixel and/or feature locations in the target frame using a machine learning system and/or algorithm. For example, the machine learning system and/or algorithm can be any type or form of deep neural network (DNN)…), as claimed.
with respect to claim 7, combination of Tofolo and Lin further discloses wherein the DOF model is implemented in a DOF inferencing to generate a flow for a pair of images and wherein the operations further comprise: detecting the ball in n>1 images from the sequence of images; and implementing the DOF model to generate a flow for each pair of consecutive images so that n-1 flows are generated, (see Tofolo paragraph 0072-0074 for calculating the flow for the pair of images; and Lin paragraph 0062, wherein …the flow target engine 102 can combine the supplemental history information with the optical flow map 108 to determine initial estimates. For example, the optical flow map 108 may indicate that pixel P3 moved from location L3 in the previous source frame to location L4 in the previous target frame. In some implementations, this displacement vector of the movement from L3 to L4 can be represented by the optical flow vector at location L3 of the optical flow map 108. The supplemental history information may indicate that pixel at location L3 in the previous source frame was part of a ball moving from point A to point B across multiple frames. In such a case, the flow target engine 102 may perform object detection or semantic segmentation and determine that pixel at location L4 in the current source frame is part of the same ball. In such a case, the pixel belonging to the ball may have moved from location L3 to L4 across the previous pair of frames, so the last location of pixel belonging the ball is known by the target map engine. The flow target engine 102 can provide an initial estimate that the pixel at location L4 in the current source frame will move to location L5 in the current target frame, where the offset between location L4 and location L5 is the same as the offset between locations L3 and L4 in the previous iteration of the optical flow estimation system), as claimed.
with respect to claim 20, combination of Tofolo and Lin further discloses wherein the three-dimensional spin parameters calculated for the ball based on the pixel displacements in the DOF map include a spin axis, (see Lin paragraph 0103, wherein …The CDF 602 of FIG. 6A illustrates a CDF of optical flow vector components in the width dimension (e.g., movement left and right) of the first dataset. The CDF 602 of FIG. 6A can be obtained by performing optical flow estimation using an optical flow estimation such as optical flow estimation system 100 and plotting the resulting width dimension component of the optical flow estimation values. The y-axis value of the cumulative distribution function represents the percentage of values of optical flow vector components in the width dimension that have a value less than the corresponding x-axis value of the plot…[this is read as parameters calculated for the ball based on the pixel displacements in the DOF map include a spin axis]), as claimed.
Claims 10-14 and 16 are rejected for the same reasons as set forth for the rejections of claims 1-5 and 7, because claims 10-14 and 16 are claiming subject matter of similar scope as claimed in claims 1-5 and 7.
Claim 21 is rejected for the same reasons as set forth for the rejections of claim 1, because claim 21 is claiming subject matter of similar scope as claimed in claim 1.
Allowable Subject Matter
Claims 6, 8-9, 15 and 17-18 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.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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