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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/13/2024 was filed and is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-20 are pending in the present application.
Claim 14 is rejected under 35 USC 112(b).
Claims 15-19 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claims 1-14 and 20, these claims contain eligible subject matter under 35 USC 101.
Claims 1-3, 7-16, and 18-20 are rejected under 35 USC 103 as being unpatentable over Chandrashekar et al. (US 10,540,759A1) in view of Coppock et al. (US 2019/0238762 A1).
Claims 4-6 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.
No prior art rejection is currently applied to claims 4-6 and 17.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, the claim recites: an auto pilot system configured to control an autonomous aerial refueling operation based on the estimate of the center; a thermal imaging sensor configured to generate the thermal image data; the object includes a drogue; a probe configured to be coupled to the object and, when coupled to the object, receive fuel via the object; a memory configured to store size information that indicates a dimension of the object, the thermal image data, or a combination thereof; or a combination thereof. In particular, the recitation of the object includes a drogue. It is unclear what is the purpose of the drogue within the context of the claim and how it is part of the device. A clarification is needed for the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental processes (concepts performed in a human mind, including as an observation, evaluation, judgment, opinion, organizing human activity and/or mathematical concepts and calculations). The independent claims recite a vision processor that identifies an object in a thermal image. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claims 15 and 19 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 15 and 19 are directed to a device and a method for object recognition in thermal image.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental processes and/or mathematical concepts (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claims 15 and 19 comprise mental processes and/or mathematical concepts that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding independent claim(s) 15 and 10, the limitations recite:
obtaining, based on thermal image data of an image depicting at least a portion of an object, a location associated with a point-of-convergence of multiple structures of the object (The step of obtaining a location associated with a point-of-convergence of multiple structures of the object falls into the “mental processes” grouping of abstract ideas because obtaining a location associated with a point-of-convergence of multiple structures of the object can be performed in the human mind as an observation, evaluation, judgement or opinion. A person can determine where is the point intersection of spokes of a tire.);
identifying the object based on the location (The step of identifying an object based on location of intersection of structure of an object falls into the “mental processes” grouping of abstract ideas because identifying an object based location of intersection of structure of an object can be performed in the human mind as an observation, evaluation, judgement or opinion. A person can determine the size and scope of a tire based on the location of intersection of structure of the tire. .)
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could mentally determine the intersection of structures of an object and identify the object. The mere nominal recitation that the various steps are being executed by a processor does not take the limitations out of the mental process and/or mathematical concepts groupings. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Independent claims 15 and 19 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Independent claims 15 and 19 discloses a thermal image, which is insignificant pre solution extra activity of gathering information that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea in a system.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Independent claim(s) 15 and 19 do not recite any additional elements that are not well-understood, routine or conventional. The use of a generic computer elements are routine, well-understood and conventional process that is performed by computers.
Thus, since independent claims 15 and 19 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claims 15 and 19 are not eligible subject matter under 35 U.S.C 101.
Regarding claim 16: the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation(s): a thermal imaging sensor configured to generate the thermal image data is generic computer component and insignificant pre-solution activity of gathering data that does not add a meaningful limitation to the abstract idea.
Regarding claim 17: the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitations: a probe configured to be coupled to the object and, when coupled to the object, receive fuel via the object, and wherein the object includes a drogue merely adds definition to previous limitation that do not add a meaningful limitation to the abstract idea.
Regarding claim 18: the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation(s): a memory configured to store size information that indicates a dimension of the object, the thermal image data, or a combination thereof is generic computer components and insignificant pre-solution activity of gathering data that does not add a meaningful limitation to the abstract idea.
Regarding claim 20, the additional limitation(s): obtaining, based on the thermal image data, a gradient of at least a portion of the image, the portion of the image including multiple pixels associated with multiple structures of the object;
for each pixel of the multiple pixels of the portion of the image, projecting, based on the gradient, a vector from the pixel;
populating an accumulator map based on the projected vectors; and
determining an estimate of a center of the object based on the accumulator map are NOT directed toward an abstract idea since it recites additional elements that integrate the judicial exception into a practical application and add significantly more that the judicial exception. Therefore, claim 20 is not directed to an abstract idea and therefore is/are not rejected under 35 USC 101.
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 1-3, 7-16, and 18-20 are rejected under 35 USC 103 as being unpatentable over Chandrashekar et al. (US 2013/0259386 A1, hereinafter Chandrashekar) in view of Coppock et al. (US 2019/0238762 A1, hereinafter Coppock).
Regarding claim 1, Chandrashekar discloses
A device comprising: a vision processor configured to: obtain,…, a gradient of at least a portion of the image, the portion of the image including multiple pixels associated with multiple structures of the object (Para [0015]: “In an embodiment, the circular object identification system compares the gradient direction of each of the prospective circumference points with a direction defined by each of the prospective circumference points and a corresponding prospective center point, with respect to the reference axis, to find a match. The circular object identification system determines convergence of the gradient direction of each of the prospective circumference points to a corresponding prospective center point on finding the match, for establishing that each of the prospective circumference points lies on a circumference of the circular object. In an embodiment, the circular object identification system traverses the predetermined distance from each of the prospective circumference points to reach a corresponding prospective center point for determining convergence of the gradient directions of the prospective circumference points to the corresponding prospective center point.”);
for each pixel of the multiple pixels of the portion of the image, project, based on the gradient, a vector from the pixel (Para [0043]: “A representation of the gradient direction according to the polar coordinate system reduces memory requirements, but is computationally intensive since the coordinates of the pixel point need to be derived from the angles, and this necessitates a greater number of processing clock cycles. For example, consider a pixel point having a gradient direction of 45°. The gradient direction image can store the gradient direction information as a gradient angle value [45°] or as a vector, that is, as [cos( 45°), sin (45°)].”);
populate an accumulator map based on the projected vectors (Para [0017]: “The circular object identification system stores each decision vote assigned to a corresponding prospective center point for each of the pixel points in an accumulator array. As used herein, the term "accumulator array" refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point.”, Para [0042]: “The gradient directions of some of the pixel points in the image are exemplarily illustrated in FIG. 4. Consider an example where the image is a low contrast image. The gradient directions do not specify whether they point inside or outside the circular object. Therefore, the circular object identification system determines the actual center point of the circular object from among the prospective center points identified along the gradient direction pointing inside and outside the circular object, at a predetermined radius defined for the circular object.”, Para [0043]: “In an embodiment, the circular object identification system constructs a gradient direction image. The gradient direction image is an image comprising gradient directions of each of the pixel points at their corresponding locations. The gradient direction image is of the same size as the original image.”); and
determine an estimate of a center of the object based on the accumulator map (Para [0042]: “If the predetermined radius of the circular object is R, incrementing an accumulator array at a distance of R from each of the pixel points in either direction results in a large number of decision votes at the actual center point of the circular object and a substantially lesser number of decision votes at a distance of 2R from the actual center point of the circular object. As used herein, the term “accumulator array” refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point. Also, as used herein, the term “decision votes” refers to votes used for conclusively identifying the center point of the circular object.”).
However, Chandrashekar does not disclose
based on thermal image data representing an image that depicts at least a portion of an object.
Coppock teaches
based on thermal image data representing an image that depicts at least a portion of an object (Para [0052]: “As another example, the turn event controller 110 can process the one or more thermal images using one or more feature detection techniques to identify one or more objects from the one or more thermal images.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Chandrashekar with identifying objects in a thermal image and other aspects of Coppock as Coppock suggested identifying objects with certain shape including circle in Para [0052] and Chandrashekar focuses on identifying the center of a circular object.
Regarding claims 15 and 19, Chandrashekar discloses
Claim 15: A device comprising: a vision processor configured to:
Claim 19: A method comprising:
obtaining,…, a location associated with a point-of-convergence of multiple structures of the object (Fig. 9c-9d, Para [0042]: “If the predetermined radius of the circular object is R, incrementing an accumulator array at a distance of R from each of the pixel points in either direction results in a large number of decision votes at the actual center point of the circular object and a substantially lesser number of decision votes at a distance of 2R from the actual center point of the circular object. As used herein, the term “accumulator array” refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point. Also, as used herein, the term “decision votes” refers to votes used for conclusively identifying the center point of the circular object.”, Para [0073]: “The circular object identification system disclosed herein performs local voting by performing the steps 201, 202, 203, 204, and 205, during storage of each decision vote assigned to a corresponding prospective center point for each of the pixel points in an accumulator array as disclosed in the detailed description of FIGS. 2A-2B.”, Para [0089]: “The circular object identification system disclosed herein may be employed in a number of practical applications, for example, toll collection applications, highway surveying, etc. For example, the size and structure of a vehicle tire identified using the circular object identification system can be used to analyze and identify the type of vehicle for charging a toll fee based on the type of vehicle.”); and
identifying the object based on the location (Para [0074]: “After determination of the center point of the circular object, the circular object identification system identifies 301 each of the pixel points in the image that are at the predetermined distance from the determined center point as exemplarily illustrated in FTG. 6C:, and that contribute a decision vote to the determined center point of the circular object. The circular object identification system, for example, constructs a circle with reference to the determined center of the circular object using a radius equal to the predetermined distance. The circular object identification system then identifies all the pixel points that lie on the circumference of the circular object and each of which has contributed a decision vote to the determined center point of the circular object. Further, the circular object identification system filters out noise pixel points that contribute decision votes to the center point but do not lie on the circumference of the circular object. That is, the circular object identification system filters out all pixel points that do not lie on the circumference of the constructed circle.”).
However Chandrashekar does not disclose
based on thermal image data of an image depicting at least a portion of an object.
Coppock teaches
based on thermal image data of an image depicting at least a portion of an object (Para [0052]: “As another example, the turn event controller 110 can process the one or more thermal images using one or more feature detection techniques to identify one or more objects from the one or more thermal images.”).
Regarding claim 2, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
calculate the gradient of the portion of the image (Para [0015]: “In an embodiment, the circular object identification system compares the gradient direction of each of the prospective circumference points with a direction defined by each of the prospective circumference points and a corresponding prospective center point, with respect to the reference axis, to find a match. The circular object identification system determines convergence of the gradient direction of each of the prospective circumference points to a corresponding prospective center point on finding the match, for establishing that each of the prospective circumference points lies on a circumference of the circular object. In an embodiment, the circular object identification system traverses the predetermined distance from each of the prospective circumference points to reach a corresponding prospective center point for determining convergence of the gradient directions of the prospective circumference points to the corresponding prospective center point.”, Para [0041]: “In another example, for obtaining the gradient directions of the pixel points in the image, the circular object identification system calculates, for example, the second partial derivatives of the first partial derivatives dI/dx and dI/dy or a square of the partial derivatives of the pixel points of the image from the partial derivatives dI/dx and dI/dy, with respect to the variables X and Y. The circular object identification system enters the calculated second partial derivatives or the square of the partial derivatives of the image intensity function I in a Hessian matrix. The Hessian matrix is a square matrix of second order partial derivatives of a function and describes a local curvature of a function of many variables. The circular object identification system computes a set of eigen values and eigen vectors from the Hessian matrix that characterize the image intensity function, and selects a dominant eigenvalue and a dominant eigenvector from the set of eigenvalues and eigenvectors. The circular object identification system obtains a direction of the dominant eigenvalue as the gradient direction of the image intensity function at a particular pixel point.”);
the multiple structures of the object include multiple spokes that extend radially away from the center of the object (Fig. 9c-9d); and
for each pixel of the multiple pixels of the portion of the image, the gradient includes a gradient magnitude value, a vector, a direction, or a combination thereof (Para [0015]: “In an embodiment, the circular object identification system compares the gradient direction of each of the prospective circumference points with a direction defined by each of the prospective circumference points and a corresponding prospective center point, with respect to the reference axis, to find a match. The circular object identification system determines convergence of the gradient direction of each of the prospective circumference points to a corresponding prospective center point on finding the match, for establishing that each of the prospective circumference points lies on a circumference of the circular object. In an embodiment, the circular object identification system traverses the predetermined distance from each of the prospective circumference points to reach a corresponding prospective center point for determining convergence of the gradient directions of the prospective circumference points to the corresponding prospective center point.”, Para [0041]: “In another example, for obtaining the gradient directions of the pixel points in the image, the circular object identification system calculates, for example, the second partial derivatives of the first partial derivatives dI/dx and dI/dy or a square of the partial derivatives of the pixel points of the image from the partial derivatives dI/dx and dI/dy, with respect to the variables X and Y. The circular object identification system enters the calculated second partial derivatives or the square of the partial derivatives of the image intensity function I in a Hessian matrix. The Hessian matrix is a square matrix of second order partial derivatives of a function and describes a local curvature of a function of many variables. The circular object identification system computes a set of eigen values and eigen vectors from the Hessian matrix that characterize the image intensity function, and selects a dominant eigenvalue and a dominant eigenvector from the set of eigenvalues and eigenvectors. The circular object identification system obtains a direction of the dominant eigenvalue as the gradient direction of the image intensity function at a particular pixel point.”).
Coppock further teaches
the vision processor is further configured to: receive the thermal image data (Para [0052]: “As another example, the turn event controller 110 can process the one or more thermal images using one or more feature detection techniques to identify one or more objects from the one or more thermal images.”).
Regarding claim 3, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
for each pixel of the multiple pixels of the portion of the image, the gradient includes a gradient magnitude value and a direction or vector; and the vision processor is configured to, for each pixel of the multiple pixels of the portion of the image, project the vector from the pixel based on the direction of the gradient at the pixel (Para [0041]: “In another example, for obtaining the gradient directions of the pixel points in the image, the circular object identification system calculates, for example, the second partial derivatives of the first partial derivatives dI/dx and dI/dy or a square of the partial derivatives of the pixel points of the image from the partial derivatives dI/dx and dI/dy, with respect to the variables X and Y. The circular object identification system enters the calculated second partial derivatives or the square of the partial derivatives of the image intensity function I in a Hessian matrix. The Hessian matrix is a square matrix of second order partial derivatives of a function and describes a local curvature of a function of many variables. The circular object identification system computes a set of eigen values and eigen vectors from the Hessian matrix that characterize the image intensity function, and selects a dominant eigenvalue and a dominant eigenvector from the set of eigenvalues and eigenvectors. The circular object identification system obtains a direction of the dominant eigenvalue as the gradient direction of the image intensity function at a particular pixel point.”).
Regarding claim 7, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
the vector is orthogonal to an edge normal vector (Fig. 5, Para [0050]: “Furthermore, the gradient direction of a pixel point that lies exactly on the circumference of the circular object is perpendicular to the tangent of the circular object at the pixel point.”); and
the vector has a length that is based on a size of the object (Para [0051]: “The circular object identification system determines the convergence of the gradient directions of the prospective circumference points C and B, for example, by traversing a predetermined radius distance in either direction from each of the prospective circumference points C and B to reach the prospective center point of the circular object at G and along the other direction at H and I respectively.”).
Regarding claim 8, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
identify an edge normal vector of the pixel (Fig. 5, Para [0050]: “Furthermore, the gradient direction of a pixel point that lies exactly on the circumference of the circular object is perpendicular to the tangent of the circular object at the pixel point.”, Para [0053]: “Consider an example where there are two externally tangent circular objects touching one another in an image as exemplarily illustrated in FIG. 5. Consider a pixel point A that lies at the point of contact of the two externally tangent circular objects. The circular object identification system identifies the prospective center points G and D at a predetermined distance R equal to the radius of the circular object, along the gradient direction of the pixel point A.”);
project a first vector from the pixel at 90 degrees from the edge normal vector; and project a second vector from the pixel at -90 degrees from the edge normal vector (Fig. 5, Para [0045]: “Consider the pixel point A exemplarily illustrated in FIG. 5. The circular object identification system moves a predetermined distance R equal to the radius of the circular object along the gradient direction of the pixel point A represented by the line I and identifies the pixel points G and D as the prospective center points. The circular object identification system constructs 103 an axis that joins corresponding prospective center points and a corresponding pixel point.”, Para [0053]: “Consider an example where there are two externally tangent circular objects touching one another in an image as exemplarily illustrated in FIG. 5. Consider a pixel point A that lies at the point of contact of the two externally tangent circular objects. The circular object identification system identifies the prospective center points G and D at a predetermined distance R equal to the radius of the circular object, along the gradient direction of the pixel point A.”).
Regarding claim 9, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
to populate the accumulator map, the vision processor is configured to, for each vector of the projected vectors, populate, based on the vector, one or more cells of the accumulator map based on at least a portion of the vector (Para [0042]: “If the predetermined radius of the circular object is R, incrementing an accumulator array at a distance of R from each of the pixel points in either direction results in a large number of decision votes at the actual center point of the circular object and a substantially lesser number of decision votes at a distance of 2R from the actual center point of the circular object. As used herein, the term “accumulator array” refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point. Also, as used herein, the term “decision votes” refers to votes used for conclusively identifying the center point of the circular object.”).
Regarding claim 10, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
the vision processor is further configured to generate the accumulator map that includes a set of cells associated with the portion of the image; and a resolution of the accumulator map is the same or lower than a resolution of the portion of the image (Para [0055]: “In an embodiment, the circular object identification system stores each decision vote assigned to a corresponding prospective center point for each of the pixel points, in an accumulator array. The accumulator array is, for example, two-dimensional since there are two unknown parameters corresponding to the X and Y coordinates of the center point of the circular object. The accumulator array is designed as a set of cells, where each of the cells defines the coordinates of each of the pixel points in the image. The accumulator array is initially populated with zeroes since none of the pixel points are assumed to be a prospective center point. The circular object identification system accumulates the decision votes for each pixel point in a particular cell of the accumulator array whose coordinates are equal to the coordinates of the identified pixel point.”).
Regarding claim 11, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
determine a candidate of the estimate of the center based on the accumulator map (Para [0055]: “In an embodiment, the circular object identification system stores each decision vote assigned to a corresponding prospective center point for each of the pixel points, in an accumulator array. The accumulator array is, for example, two-dimensional since there are two unknown parameters corresponding to the X and Y coordinates of the center point of the circular object. The accumulator array is designed as a set of cells, where each of the cells defines the coordinates of each of the pixel points in the image. The accumulator array is initially populated with zeroes since none of the pixel points are assumed to be a prospective center point. The circular object identification system accumulates the decision votes for each pixel point in a particular cell of the accumulator array whose coordinates are equal to the coordinates of the identified pixel point.”, Para [0058]: “This, for example, allows the circular object identification system to determine the center point of the circular object when there are multiple local maxima with a relatively equal number of resultant decision votes which does not allow the center point of the circular object to be directly determined”);
determine a peak value of the accumulator map (Para [0057]: “In an embodiment, the circular object identification system determines that the center point of the circular object is the prospective center point with the highest number of resultant decision votes, that is, a local maximum with the highest number of resultant decision votes.”, Para [0058]: “This, for example, allows the circular object identification system to determine the center point of the circular object when there are multiple local maxima with a relatively equal number of resultant decision votes which does not allow the center point of the circular object to be directly determined”); and
determine a score of the candidate based on characteristic information, the peak value of the accumulator map, or a combination thereof (Para [0058]: “This, for example, allows the circular object identification system to determine the center point of the circular object when there are multiple local maxima with a relatively equal number of resultant decision votes which does not allow the center point of the circular object to be directly determined”).
Regarding claim 12, dependent upon claim 11, Chandrashekar in view of Coppock teaches everything regarding claim 11.
Chandrashekar further discloses
perform a blob characterization operation on the accumulator map to generate the characteristic information; and select, based on a score of the candidate, the candidate for use as the estimate of the center of the object (Para [0059]: “In an embodiment, the circular object identification system sets a center threshold for a number of resultant decision votes that qualifies a prospective center point as the center point of the circular object. For example, consider a circular object that is a complete circle. If the radius of the circle is R, the circular object identification system sets a center threshold of pi*R. Therefore, a prospective center point is determined as the center point of the circle only when the number of resultant decision votes accumulated by the prospective center point is at least pi*R.”).
Regarding claim 13, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Chandrashekar further discloses
identify the object based on the estimate of the center; and track the object based on the estimate of the center (Para [0086]: “The circular object identification system then tracks the changing X-Y coordinates of the center point of the vehicle tire in accordance with the linear movement of the center point of the vehicle tire, when the vehicle tire is in motion.”); and
the center of the object is associated with a point-of-convergence, and the point- of-convergence is associated with the multiple structures of the object (Fig. 9c-9d, Para [0089]: “The circular object identification system disclosed herein may be employed in a number of practical applications, for example, toll collection applications, highway surveying, etc. For example, the size and structure of a vehicle tire identified using the circular object identification system can be used to analyze and identify the type of vehicle for charging a toll fee based on the type of vehicle.”);.
Regarding claim 14, dependent upon claim 1, Chandrashekar in view of Coppock teaches everything regarding claim 1.
Coppock further teaches
an auto pilot system configured to control an autonomous aerial refueling operation based on the estimate of the center; a thermal imaging sensor configured to generate the thermal image data; the object includes a drogue; a probe configured to be coupled to the object and, when coupled to the object, receive fuel via the object; a memory configured to store size information that indicates a dimension of the object, the thermal image data, or a combination thereof; or a combination thereof (Para [0050]; “For example, the tum event controller 110 may request thermal imaging from one or more of the thermal imaging devices 104, 106.”).
Regarding claim 16, dependent upon claim 15, Chandrashekar in view of Coppock teaches everything regarding claim 15.
Coppock further teaches
a thermal imaging sensor configured to generate the thermal image data (Para [0050]; “For example, the tum event controller 110 may request thermal imaging from one or more of the thermal imaging devices 104, 106.”)..
Regarding claim 18, dependent upon claim 15, Chandrashekar in view of Coppock teaches everything regarding claim 15.
Coppock further teaches
a memory configured to store size information that indicates a dimension of the object, the thermal image data, or a combination thereof (Para [0072]: “The memory device(s) 606 can further store data 610 that can be accessed by the processors 604. For example, the data 610 can include thermal images, thermal image video, vehicle parameters, prior thermal inspection data, as described herein.”).
Regarding claim 20, dependent upon claim 19, Chandrashekar in view of Coppock teaches everything regarding claim 19.
Chandrashekar further discloses
A device comprising: a vision processor configured to: obtaining,…, a gradient of at least a portion of the image, the portion of the image including multiple pixels associated with multiple structures of the object (Para [0015]: “In an embodiment, the circular object identification system compares the gradient direction of each of the prospective circumference points with a direction defined by each of the prospective circumference points and a corresponding prospective center point, with respect to the reference axis, to find a match. The circular object identification system determines convergence of the gradient direction of each of the prospective circumference points to a corresponding prospective center point on finding the match, for establishing that each of the prospective circumference points lies on a circumference of the circular object. In an embodiment, the circular object identification system traverses the predetermined distance from each of the prospective circumference points to reach a corresponding prospective center point for determining convergence of the gradient directions of the prospective circumference points to the corresponding prospective center point.”);
for each pixel of the multiple pixels of the portion of the image, projecting, based on the gradient, a vector from the pixel (Para [0043]: “A representation of the gradient direction according to the polar coordinate system reduces memory requirements, but is computationally intensive since the coordinates of the pixel point need to be derived from the angles, and this necessitates a greater number of processing clock cycles. For example, consider a pixel point having a gradient direction of 45°. The gradient direction image can store the gradient direction information as a gradient angle value [45°] or as a vector, that is, as [cos( 45°), sin (45°)].”);
populating an accumulator map based on the projected vectors(Para [0017]: “The circular object identification system stores each decision vote assigned to a corresponding prospective center point for each of the pixel points in an accumulator array. As used herein, the term "accumulator array" refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point.”, Para [0042]: “The gradient directions of some of the pixel points in the image are exemplarily illustrated in FIG. 4. Consider an example where the image is a low contrast image. The gradient directions do not specify whether they point inside or outside the circular object. Therefore, the circular object identification system determines the actual center point of the circular object from among the prospective center points identified along the gradient direction pointing inside and outside the circular object, at a predetermined radius defined for the circular object.”, Para [0043]: “In an embodiment, the circular object identification system constructs a gradient direction image. The gradient direction image is an image comprising gradient directions of each of the pixel points at their corresponding locations. The gradient direction image is of the same size as the original image.”); and
determining an estimate of a center of the object based on the accumulator map (Para [0042]: “If the predetermined radius of the circular object is R, incrementing an accumulator array at a distance of R from each of the pixel points in either direction results in a large number of decision votes at the actual center point of the circular object and a substantially lesser number of decision votes at a distance of 2R from the actual center point of the circular object. As used herein, the term “accumulator array” refers to an array of cells, where each of the cells marks a spatial location of a prospective center point in the image, and is configured to store decision votes assigned to the prospective center point. Also, as used herein, the term “decision votes” refers to votes used for conclusively identifying the center point of the circular object.”).
Coppock further teaches
based on the thermal image data (Para [0052]: “As another example, the turn event controller 110 can process the one or more thermal images using one or more feature detection techniques to identify one or more objects from the one or more thermal images.”).
Allowable Subject Matter
Claims 4-6 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.
Relevant Prior Art Directed to State of Art
Fleming (US 9,978,140 B2, hereinafter Fleming) is prior art not applied in the rejection(s) above. Fleming discloses a computer-readable storage medium storing instructions that can cause a processor to process image data defining an image of a vascular structure of temporal vascular arcades of a retina to estimate a location of the fovea of the retina in the image by transforming received image data such that the vascular structure in the image defined by the transformed image data is more circular than the vascular structure defined by the image data; calculating, for each of a plurality of pixels of the transformed image data, a respective local orientation vector indicative of the orientation of any blood vessel present in the image; calculating a normalized local orientation vector for each of the pixels; operating on an array of accumulators; and estimating the location of the fovea in the image of the retina using the location of a pixel of the transformed image data.
Kelly et al. (US 8,818,031 Bl, hereinafter Kelly) is prior art not applied in the rejection(s) above. Kelly discloses a method for determining the geographic location of an object, the method comprising retrieving at a vision system a plurality of images from a database, each image related to a respective vantage point, detecting, with the vision system, the object in at least two of the plurality of images, generating a vector in relation to the respective vantage point for each image in which the object was detected, and triangulating, based on the intersection of at least two vectors, the geographic location of the object.
Greenspan et al. (US 12,417,552 B2, hereinafter Greenspan) is prior art not applied in the rejection(s) above. Greenspan discloses A method of processing a point cloud to determine a pose of an object according to a set of keypoints, each keypoint being defined within a frame of reference of the object, the point cloud having been obtained using a 3D depth sensing device
Conclusion
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/J. C./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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