DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
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finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/17/2026 has been entered.
Response to amendments
Applicant’s remarks filed 07/17/2026 with respect to the claim objections are persuasive due to claim amendments thus have been withdrawn.
Response to Arguments
Applicant’s arguments see remarks, filed 07/17/2026, with respect to the claim 1-6, 8, 10-16, 18, 20-26, 28, and 30 have been fully considered but are moot because the arguments do not apply to the current combinations of references being used in the current rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 11, 12, and 14-15, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “obtaining, using at least one processing device ……,” [Line 3].
Claim 1; recites the limitation, “using the at least one processing device ……,” [Line 6].
Claim 1; recites the limitation, “identifying, using the at least one processing device……,” [Line 9].
Claim 1; recites the limitation, “determining using at least one processing device……,” [Line 2].
Claim 11; recites the limitation, “one processing device configured to……,” [Line 2].
Claim 12; recites the limitation, “one processing device is further configured to……,” [Line 11].
Claim 14; recites the limitation, “one processing device is configured to……,” [Line 22-23].
Claim 15; recites the limitation, “one processing device is configured to……,” [Line 27].
Claim 15; recites the limitation, “one processing device is configured to……,” [Line 30].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 1, 11, 12, and 14-15:
“Processing device” (Fig. 12, #1202. Paragraph [0098])- “As shown in FIGURE 12, the device 1200 denotes a computing device or system that includes at least one processing device 1202, at least one storage device 1204, at least one communication unit 1206, and at least one input/output (I/O) unit 1208. The processing device 1202may execute instructions that can be loaded into a memory 1210. The processing device 1202 includes any suitable number(s) and type(s) of processors or other processing devices in any suitable arrangement. Example types of processing devices 1202 include one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.” thus, have sufficient structure or material wherein is hardware containing any suitable number and types of processors or other processing devices including one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling the left and right virtual ego lines and the bottom of the image are used to define a triangular region within the image at step 506, as described in Paragraph [0060] in the specification, also please see Fig. 5, does not reasonably provide enablement for explicitly disclosing “the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.”
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, not taught by the specification is determining a triangular region at a bottom of the image.
The office respectfully requests the Applicant to indicate where in the specification teaches the limitation in claim 1 or amend in order to overcome the rejected under 35 U.S.C. 112(a.).
Claim 11 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling the left and right virtual ego lines and the bottom of the image are used to define a triangular region within the image at step 506, as described in Paragraph [0060] in the specification, also please see Fig. 5, does not reasonably provide enablement for explicitly disclosing “the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.”
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, not taught by the specification is determining a triangular region at a bottom of the image.
The office respectfully requests the Applicant to indicate where in the specification teaches the limitation in claim 11 or amend in order to overcome the rejected under 35 U.S.C. 112(a.).
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling the left and right virtual ego lines and the bottom of the image are used to define a triangular region within the image at step 506, as described in Paragraph [0060] in the specification, also please see Fig. 5, does not reasonably provide enablement for explicitly disclosing “the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.”
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, not taught by the specification is determining a triangular region at a bottom of the image.
The office respectfully requests the Applicant to indicate where in the specification teaches the limitation in claim 21 or amend in order to overcome the rejected under 35 U.S.C. 112(a.).
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 11-16, 21-26, are rejected under 35 U.S.C 103 as being unpatentable over Fernandez et al. (US 20140133698 A1) hereafter referenced as Fernandez in view of Pillai et al (US 20160267331 A1) hereafter referenced as Pillai and Tan et al (US 20230005173 A1) hereafter referenced as Tan.
Regarding claim 1, Fernandez teaches a method comprising (Fig. 1, Paragraph [0020]-Fernandez discloses the present disclosure provides a method of detecting an object of interest in an image):
obtaining, using the at least one processing device (Fig. 2-3, Paragraph [0030]- Fernandez discloses the present disclosure provides a computer for detecting objects of interest comprising: [0031] a. a processor; [0032] b. a camera; and [0033] c. a memory having stored there software instructions),
a refined boundary (Fig. 4 and 9, Abstract- Fernandez discloses the RLE and SAT are used to identify candidate objects and to iteratively refine their boundaries.)
identifying a specified portion of a detected vehicle within a scene (Fig. 4 and 9, Paragraph [0078]- Fernandez discloses this idealized signature can be extrapolated to fit imperfect images, where the boundaries of a relatively rectangular object may be constructed as a "box," starting with a long run of relatively intense pixels),
repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420.)
identifying, using the at least one processing device, one or more locations of one or more components of the detected vehicle based on the identified regions and the determined similarities (Fig. 1, paragraph [0047]- Fernandez discloses for example, simple Haar features are implemented to achieve corner detection, thereby reducing MIPS. Further, the symmetry, corner, and shadow detection features are performed on vehicle(s) in the previous image frame, instead of possible vehicle(s) in the current frame, thereby providing deterministic MIPS.);
identifying, using the at least one processing device, at least one action to be performed based on the one or more locations of the one or more components of the detected vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
and performing the identified at least one action (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.).
Fernandez fails to explicitly teach determining, using at least one processing device, a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
However, Pillai explicitly teaches determining, using at least one processing device, a position of a vanishing point based on multiple collections of line segments in an image comprising image data (Fig. 2, Paragraph [0090]- Pillai discloses the pruning module 212 may receive a road prior map from the prior map module 208 or retrieve the road prior map from the memory 227. The pruning module 212 may compare the superpixel regions to the road prior map to identify a road region, a central horizon line, and an approximate vanishing point in the real-time image.);
the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image (Fig. 4B Paragraph [0086]- Pillai discloses FIG. 4B is a graphic representation 425 of an example road with a determined road region 420. Further in Fig. 4B Paragraph [0093]- Pillai discloses the pruning module 212 prunes bounding boxes that are considerably outside the road region or that have a centroid value that is located above the horizon line (wherein Fig. 4B shows the road region as a triangle formed from a vanishing point, a left and right ego line, and a bottom).);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Pillai determining, using at least one processing device, a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein determining, using at least one processing device, a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
The motivation behind the modification would have been to allow a reduction in processing time, since both Fernandez and Pillai are both systems for detecting vehicles and applying bounding boxes. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Pillai’s system provides an reduction in processing time for real time object classification. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Pillai et al. (US 20160267331 A1), Paragraph [0007]).
Fernandez in view of Pillai fails to explicitly teach and (ii) determining a similarity of the image data contained within the multiple regions.
However, Tan explicitly teaches and (ii) determining a similarity of the image data contained within the multiple regions (Fig. 1, Paragraph [0140]- Tan discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Pillai of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan (ii) determining a similarity of the image data contained within the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein (ii) determining a similarity of the image data contained within the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 2, Fernandez in view of Pillai and Tan teaches the method of Claim 1,
Fernandez explicitly teaches further comprising: obtaining an image of the scene (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle),
the image comprising the image data (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle);
generating an integral image of the scene (Fig. 1, Paragraph [0060-61]- Fernandez discloses in a separate branch of FIG. 1, in block 110, a summed area table (SAT) is produced for a prior image and The SAT is produced only once for each image, and greatly simplifies calculating sums for other operations.);
Fernandez fails to explicitly teach and during each iteration, determining a probabilistic distribution associated with each of the multiple regions based on the integral image.
However, Tan explicitly teaches during each iteration, determining a probabilistic distribution associated with each of the multiple regions based on the integral image (Fig. 1, Paragraph [0139]- Tan discloses the uncertainty based inconsistency computation computes a cross-modal inconsistency with probability distributions of each true positive pair from each object detection network. The uncertainty based inconsistency computations can be further divided two sub-groups: bounding box and heatmap.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of tan of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan during each iteration, determining a probabilistic distribution associated with each of the multiple regions based on the integral image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein during each iteration, determining a probabilistic distribution associated with each of the multiple regions based on the integral image.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 3, Fernandez in view of Pillai and Tan teaches the method of Claim 2,
Fernandez explicitly teaches wherein the probabilistic distributions represent normal distribution parameterizations (Fig. 13, Paragraph [0075]- Fernandez discloses in a real-world image with degrees of intensity and imperfect edges, the vertical sum will have an imperfect but characteristic "peak" or local maximum, with a steep rise and fall around the peak, as in FIG. 13., this shows a normal distribution).
Regarding claim 4, Fernandez in view of Pillai and Tan teaches the method of Claim 2,
Fernandez explicitly teaches wherein, during each iteration, determining the similarity of the image data contained within the multiple regions comprises (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420. Intensity sums are taken for both the left side (A) and the right side (B) of segment 422. A difference of these sums is then computed (A-B). The sum is not processor intensive because it is based on SAT 240 (FIG. 2). Shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.)
Fernandez fails to explicitly teach, determining a divergence between the probabilistic distributions associated with the multiple regions.
However, Tan explicitly teaches determining a divergence between the probabilistic distributions associated with the multiple regions (Fig. 1, Paragraph [0140]- Tan Discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan determining a divergence between the probabilistic distributions associated with the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles determining a divergence between the probabilistic distributions associated with the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 5 Fernandez in view of Pillai and Tan teaches the method of Claim 1,
Fernandez explicitly teaches wherein: each iteration generates coordinates based on the identified regions within the refined boundary (Fig. 4A, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420. Intensity sums are taken for both the left side (A) and the right side (B) of segment 422. A difference of these sums is then computed (A-B). The sum is not processor intensive because it is based on SAT 240 (FIG. 2). In a practical application, shape 420 is centered on object 410 when the difference is at a minimum, as shown in FIG. 4A.);
and identifying the one or more locations of the one or more components of the detected vehicle comprises generating a weighted combination of the coordinates while using the determined similarities (Fig. 2, Paragraph [0082]- Fernandez discloses core 220 then in block 260 performs corner, symmetry, and shadow detection operations on candidate objects in a prior iteration of the image using SAT 240. The operations of block 260 provide a set of candidate objects, which are provided to block 270, where HoG/SVM is performed on the candidate objects. Tracking is implemented in block 290 on all objects classified as vehicles for the current frame.),
the one or more locations of the one or more components of the detected vehicle representing the weighted combination (Fig. 4A, Paragraph [0064]- Fernandez discloses Shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.).
Regarding claim 6, Fernandez in view of Pillai and Tan teaches the method of Claim 1,
Fernandez explicitly teaches wherein, during each iteration, the multiple regions within the refined boundary include a first region (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the first region can be the left or right sides)) and
a mirrored second region within the refined boundary (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the mirrored second region can be the left or right sides whichever the first region is not)).
Regarding claim 11, Fernandez teaches an apparatus comprising (Fig. 2-3, Paragraph [0092]- Fernandez discloses further, the operations and steps described with reference to the preceding FIGURES illustrate only some of the possible scenarios that may be executed by, or within, the various apparatuses, processors, devices, and/or systems, described herein.):
at least one processing device (Fig. 2-3, Paragraph [0030]- Fernandez discloses the present disclosure provides a computer for detecting objects of interest comprising: [0031] a. a processor; [0032] b. a camera; and [0033] c. a memory having stored there software instructions):
configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle. Further in Fig. 4 and 9, Paragraph [0078]- Fernandez discloses this idealized signature can be extrapolated to fit imperfect images, where the boundaries of a relatively rectangular object may be constructed as a "box," starting with a long run of relatively intense pixels),
the refined boundary associated with image data (Fig. 4 and 9, Paragraph [0078]- Fernandez discloses this idealized signature can be extrapolated to fit imperfect images, where the boundaries of a relatively rectangular object may be constructed as a "box," starting with a long run of relatively intense pixels);
repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420.) and
identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities (Fig. 1, paragraph [0047]- Fernandez discloses for example, simple Haar features are implemented to achieve corner detection, thereby reducing MIPS. Further, the symmetry, corner, and shadow detection features are performed on vehicle(s) in the previous image frame, instead of possible vehicle(s) in the current frame, thereby providing deterministic MIPS.).
identify at least one action to be performed based on the one or more locations of the one or more components of the detected vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
and performing the identified at least one action (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.).
Fernandez fails to explicitly teach determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
However, Pillai explicitly teaches determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data (Fig. 2, Paragraph [0090]- Pillai discloses the pruning module 212 may receive a road prior map from the prior map module 208 or retrieve the road prior map from the memory 227. The pruning module 212 may compare the superpixel regions to the road prior map to identify a road region, a central horizon line, and an approximate vanishing point in the real-time image.);
the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image (Fig. 4B Paragraph [0086]- Pillai discloses FIG. 4B is a graphic representation 425 of an example road with a determined road region 420. Further in Fig. 4B Paragraph [0093]- Pillai discloses the pruning module 212 prunes bounding boxes that are considerably outside the road region or that have a centroid value that is located above the horizon line (wherein Fig. 4B shows the road region as a triangle formed from a vanishing point, a left and right ego line, and a bottom).);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Pillai determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
The motivation behind the modification would have been to allow a reduction in processing time, since both Fernandez and Pillai are both systems for detecting vehicles and applying bounding boxes. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Pillai’s system provides an reduction in processing time for real time object classification. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Pillai et al. (US 20160267331 A1), Paragraph [0007]).
Fernandez in view of Pillai fails to explicitly teach (ii) determine a similarity of the image data contained within the multiple regions.
However, Tan explicitly teaches (ii) determine a similarity of the image data contained within the multiple regions (Fig. 1, Paragraph [0140]- Tan discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Pillai of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan (ii) determine a similarity of the image data contained within the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein (ii) determine a similarity of the image data contained within the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 12, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 11,
Fernandez explicitly teaches wherein the at least one processing device is further configured to: obtain an image of the scene, the image comprising the image data (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle);
generate an integral image of the scene (Fig. 1, Paragraph [0060-61] Fernandez discloses in a separate branch of FIG. 1, in block 110, a summed area table (SAT) is produced for a prior image and The SAT is produced only once for each image, and greatly simplifies calculating sums for other operations.);
Fernandez fails to explicitly teach during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
However, Tan explicitly teaches during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image (Fig. 1, Paragraph [0139]- Tan discloses the uncertainty based inconsistency computation computes a cross-modal inconsistency with probability distributions of each true positive pair from each object detection network. The uncertainty based inconsistency computations can be further divided two sub-groups: bounding box and heatmap.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 13, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 12,
Fernandez explicitly teaches wherein the probabilistic distributions represent normal distribution parameterizations (Fig. 13, Paragraph [0075]- Fernandez discloses in a real-world image with degrees of intensity and imperfect edges, the vertical sum will have an imperfect but characteristic "peak" or local maximum, with a steep rise and fall around the peak, as in FIG. 13.; this shows a normal distribution).
Regarding claim 14, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 12,
Fernandez explicitly teaches wherein, to determine the similarity of the image data contained within the multiple regions during each iteration (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420. Intensity sums are taken for both the left side (A) and the right side (B) of segment 422. A difference of these sums is then computed (A-B). The sum is not processor intensive because it is based on SAT 240 (FIG. 2). Shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.).
Fernandez fail to explicitly teach, the at least one processing device is configured to determine a divergence between the probabilistic distributions associated with the multiple regions.
However, Tan explicitly teaches the at least one processing device is configured to determine a divergence between the probabilistic distributions associated with the multiple regions (Fig. 1, Paragraph [0140]- Tan discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan wherein, the at least one processing device is configured to determine a divergence between the probabilistic distributions associated with the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein, the at least one processing device is configured to determine a divergence between the probabilistic distributions associated with the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 15 Fernandez in view of Pillai and Tan teaches the apparatus of Claim 11,
Fernandez explicitly teaches wherein: during each iteration, the at least one processing device is configured to generate coordinates based on the identified regions within the refined boundary (Fig. 4A, Paragraph [0064]- Fernandez discloses in a practical application, shape 420 is centered on object 410 when the difference is at a minimum, as shown in FIG. 4A.);
and to identify the one or more locations of the one or more components of the detected vehicle, the at least one processing device is configured to generate a weighted combination of the coordinates while using the determined similarities as weights for the coordinates (Fig. 2, Paragraph [0082]- Fernandez discloses core 220 then in block 260 performs corner, symmetry, and shadow detection operations on candidate objects in a prior iteration of the image using SAT 240. The operations of block 260 provide a set of candidate objects, which are provided to block 270, where HoG/SVM is performed on the candidate objects. Tracking is implemented in block 290 on all objects classified as vehicles for the current frame.),
the one or more locations of the one or more components of the detected vehicle representing the weighted combination (Fig. 4A, Paragraph [0064]- Fernandez discloses shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.).
Regarding claim 16, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 11,
Fernandez explicitly teaches wherein, during each iteration, the multiple regions within the refined boundary include a first region (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the first region can be the left or right sides)) and
a mirrored second region within the refined boundary (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the mirrored second region can be the left or right sides whichever the first region is not)).
Regarding claim 21, Fernandez teaches a non-transitory machine-readable medium containing instructions that when executed cause (Fig. 2-3, Paragraph [0088]-Fernandez discloses moreover, it should be noted that the use of complementary electronic devices, hardware, non-transitory software, etc. offer an equally viable option for implementing the teachings of the present disclosure.):
at least one processor to (Fig. 2-3, Paragraph [0030]- Fernandez discloses the present disclosure provides a computer for detecting objects of interest comprising: [0031] a. a processor; [0032] b. a camera; and [0033] c. a memory having stored there software instructions):
obtain a refined boundary identifying a specified portion of a detected object within a scene (Fig. 4 and 9, Paragraph [0078]- Fernandez discloses this idealized signature can be extrapolated to fit imperfect images, where the boundaries of a relatively rectangular object may be constructed as a "box," starting with a long run of relatively intense pixels),
the refined boundary associated with image data (Fig. 4 and 9, Paragraph [0078]- Fernandez discloses this idealized signature can be extrapolated to fit imperfect images, where the boundaries of a relatively rectangular object may be constructed as a "box," starting with a long run of relatively intense pixels);
repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420.) and
identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities (Fig. 1, Paragraph [0047]- Fernandez discloses for example, simple Haar features are implemented to achieve corner detection, thereby reducing MIPS. Further, the symmetry, corner, and shadow detection features are performed on vehicle(s) in the previous image frame, instead of possible vehicle(s) in the current frame, thereby providing deterministic MIPS.).
identify at least one action to be performed based on the one or more locations of the one or more components of the detected vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
and perform the identified at least one action (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.).
Fernandez fails to explicitly teach determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
However, Pillai explicitly teaches determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data (Fig. 2, Paragraph [0090]- Pillai discloses the pruning module 212 may receive a road prior map from the prior map module 208 or retrieve the road prior map from the memory 227. The pruning module 212 may compare the superpixel regions to the road prior map to identify a road region, a central horizon line, and an approximate vanishing point in the real-time image.);
the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image (Fig. 4B Paragraph [0086]- Pillai discloses FIG. 4B is a graphic representation 425 of an example road with a determined road region 420. Further in Fig. 4B Paragraph [0093]- Pillai discloses the pruning module 212 prunes bounding boxes that are considerably outside the road region or that have a centroid value that is located above the horizon line (wherein Fig. 4B shows the road region as a triangle formed from a vanishing point, a left and right ego line, and a bottom).);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Pillai determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein determine a position of a vanishing point based on multiple collections of line segments in an image comprising image data; the refined boundary based on an original boundary refined based on the vanishing point and a triangular region defined by left and right virtual ego lines and a triangular region at a bottom of the image.
The motivation behind the modification would have been to allow a reduction in processing time, since both Fernandez and Pillai are both systems for detecting vehicles and applying bounding boxes. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Pillai’s system provides an reduction in processing time for real time object classification. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Pillai et al. (US 20160267331 A1), Paragraph [0007]).
Fernandez in view of Pillai fails to explicitly teach (ii) determine a similarity of the image data contained within the multiple regions.
However, Tan explicitly teaches ii) determine a similarity of the image data contained within the multiple regions (Fig. 1, Paragraph [0140]- Tan discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Pillai of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan ii) determine a similarity of the image data contained within the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein ii) determine a similarity of the image data contained within the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 22, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 21,
Fernandez explicitly teaches further containing instructions that when executed cause the at least one processor to obtain an image of the scene (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle),
the image comprising the image data (Fig. 6, Paragraph [0053]- Fernandez discloses FIG. 6 discloses an image 600 of an exemplary road scene, which may be taken, for example, from a video stream of an onboard-camera of an ADAS-equipped vehicle);
generate an integral image of the scene (Fig. 1, Paragraph [0060-61] Fernandez discloses in a separate branch of FIG. 1, in block 110, a summed area table (SAT) is produced for a prior image and The SAT is produced only once for each image, and greatly simplifies calculating sums for other operations.);
Fernandez fails to explicitly teach and during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
However, Tan explicitly teaches during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image (Fig. 1, Paragraph [0140]- Tan discloses the uncertainty based inconsistency computation computes a cross-modal inconsistency with probability distributions of each true positive pair from each object detection network. The uncertainty based inconsistency computations can be further divided two sub-groups: bounding box and heatmap.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein during each iteration, determine a probabilistic distribution associated with each of the multiple regions based on the integral image.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 23, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 22,
Fernandez explicitly teaches wherein the probabilistic distributions represent normal distribution parameterizations (Fig. 13, Paragraph [0075]- Fernandez discloses in a real-world image with degrees of intensity and imperfect edges, the vertical sum will have an imperfect but characteristic "peak" or local maximum, with a steep rise and fall around the peak, as in FIG. 13., this shows a normal distribution).
Regarding claim 24, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 22,
Fernandez explicitly teaches, wherein the instructions that when executed cause the at least one processor to determine the similarity of the image data contained within the multiple regions during each iteration (Fig. 4, Paragraph [0064]- Fernandez discloses after object 420 is divided into segments 422, a vertical axis 450 is established through the center of shape 420. Intensity sums are taken for both the left side (A) and the right side (B) of segment 422. A difference of these sums is then computed (A-B). The sum is not processor intensive because it is based on SAT 240 (FIG. 2). Shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.).
Fernandez fail to explicitly teach, comprise instructions that when executed cause the at least one processor to determine a divergence between the probabilistic distributions associated with the multiple regions.
However, Tan explicitly teaches comprise instructions that when executed cause the at least one processor to determine a divergence between the probabilistic distributions associated with the multiple regions (Fig. 1, Paragraph [0140]- Tan Discloses the Jensen-Shannon (JS) divergence measures the similarity between two probability distributions, such as those associated with bounding boxes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Tan comprise instructions that when executed cause the at least one processor to determine a divergence between the probabilistic distributions associated with the multiple regions.
Wherein having Fernandez’s system of object detection particularly for vehicles comprise instructions that when executed cause the at least one processor to determine a divergence between the probabilistic distributions associated with the multiple regions.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Tan are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Tan’s system provides an increase in efficiency of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Tan et al. (US 20230005173 A1), Paragraph [0034]).
Regarding claim 25, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 21,
Fernandez explicitly teaches wherein: the instructions when executed cause the at least one processor to generate, during each iteration, coordinates based on the identified regions within the refined boundary (Fig. 4A, Paragraph [0064]- Fernandez discloses in a practical application, shape 420 is centered on object 410 when the difference is at a minimum, as shown in FIG. 4A.); and
and the instructions that when executed cause the at least one processor to identify the one or more components of the detected object comprise instructions that when executed cause the at least one processor to generate a weighted combination of the coordinates while using the determined similarities as weights for the coordinates (Fig. 2, Paragraph [0082]- Fernandez discloses core 220 then in block 260 performs corner, symmetry, and shadow detection operations on candidate objects in a prior iteration of the image using SAT 240. The operations of block 260 provide a set of candidate objects, which are provided to block 270, where HoG/SVM is performed on the candidate objects. Tracking is implemented in block 290 on all objects classified as vehicles for the current frame.),
the one or more locations of the one or more components of the detected object representing the weighted combination (Fig. 4A, Paragraph [0064]- Fernandez discloses shape 420 is then slid left and right, and contracted and expanded horizontally. With each change, additional differences of sums are taken. In an ideal image, the two sides of object 410 are exactly symmetrical, and the difference of the sums will be exactly zero when shape 420 is centered on object 410 and equal in width.).
Regarding claim 26, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 21,
Fernandez explicitly teaches wherein, during each iteration, the multiple regions within the refined boundary include a first region (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the first region can be the left or right sides)) and
a mirrored second region within the refined boundary (Fig. 1, Paragraph [0062]- Fernandez discloses in block 140, an image is provided, including vehicles or other candidate objects identified in a previous frame. In block 144, a symmetry check is performed to refine detection of left and right sides. This is useful for detecting vehicles because almost all motor vehicles are essentially symmetrical along a vertical axis when viewed from behind. (Wherein the mirrored second region can be the left or right sides whichever the first region is not)).
Claims 8, 18, and 28 are rejected under 35 U.S.C 103 as being unpatentable over Fernandez et al. (US 20140133698 A1) hereafter referenced as Fernandez in view of Pillai et al (US 20160267331 A1) hereafter referenced as Pillai and Tan et al (US 20230005173 A1) hereafter referenced as Tan and further in view of Wilbert et al (US 20180300578 A1) hereafter referenced as Wilbert.
Regarding claim 8, Fernandez in view of Pillai and Tan teaches the method of Claim 1,
Fernandez explicitly teaches, the specified portion of the detected vehicle comprises a rear portion of the vehicle (Fig. 6, paragraph [0002]- Fernandez discloses the present disclosure relates generally to object detection and more particularly to detection of motor vehicles in a video stream for forward collision warning (FCW) systems. It can be seen in the figure that the images being scanned are from the rear.);
Fernandez in view of Tan fails to explicitly teach the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
However, Wilbert explicitly teaches the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle (Fig. 29, paragraph [0188]- Wilbert discloses the system then initiates a plurality of processes, including plate detector 2902, car detector 2911, logo detector 2914, and/or tail light and characteristic detector 2917.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Wilbert the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Wilbert are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Wilbert’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Wilbert et al. (US 20180300578 A1), Paragraph [0101 and 00182]).
Regarding claim 18, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 11,
Fernandez explicitly teaches, the specified portion of the detected vehicle comprises a rear portion of the vehicle (Fig. 6, paragraph [0002]- Fernandez discloses the present disclosure relates generally to object detection and more particularly to detection of motor vehicles in a video stream for forward collision warning (FCW) systems. It can be seen in the figure that the images being scanned are from the rear.);
Fernandez in view of Tan fails to explicitly teach the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
However, Wilbert explicitly teaches the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle (Fig. 29, paragraph [0188]- Wilbert discloses the system then initiates a plurality of processes, including plate detector 2902, car detector 2911, logo detector 2914, and/or tail light and characteristic detector 2917.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Wilbert the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Wilbert are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Wilbert’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Wilbert et al. (US 20180300578 A1), Paragraph [0101 and 00182]).
Regarding claim 28, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 21,
Fernandez explicitly teaches, the specified portion of the detected vehicle comprises a rear portion of the vehicle (Fig. 6, paragraph [0002]- Fernandez discloses the present disclosure relates generally to object detection and more particularly to detection of motor vehicles in a video stream for forward collision warning (FCW) systems. It can be seen in the figure that the images being scanned are from the rear.);
Fernandez fails to explicitly teach the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
However, Wilbert explicitly teaches the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle (Fig. 29, paragraph [0188]- Wilbert discloses the system then initiates a plurality of processes, including plate detector 2902, car detector 2911, logo detector 2914, and/or tail light and characteristic detector 2917.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Wilbert the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein the one or more components of the vehicle comprise at least one of: one or more taillights of the vehicle or a license plate of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Wilbert are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Wilbert’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Wilbert et al. (US 20180300578 A1), Paragraph [0101 and 00182]).
Claims 10, 20, and 30 are rejected under 35 U.S.C 103 as being unpatentable over Fernandez et al. (US 20140133698 A1) hereafter referenced as Fernandez in view of Pillai et al (US 20160267331 A1) hereafter referenced as Pillai and Tan et al (US 20230005173 A1) hereafter referenced as Tan and further in view of Takahashi et al. (US 20160014406 A1) hereafter referenced as Takahashi, and Lubbe et al (Brake reactions of distracted drivers to pedestrian Forward Collision Warning systems) hereafter referenced as Lubbe.
Regarding claim 10, Fernandez in view of Pillai and Tan the method of Claim 1,
Fernandez explicitly teaches, wherein the identified at least one action comprises at least one of: an adjustment to at least one of (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.):
a braking of the vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
Fernandez in view of Pillai and Tan fail to explicitly teach a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
However, Takahashi explicitly teaches a steering of a vehicle (Fig. 1, Paragraph [0112]- Takahashi discloses the vehicle drive control unit 104 performs the cruise assist control such as reporting a warning to a driver of the vehicle 100, and controlling the steering and brakes of the vehicle),
a speed of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.),
an acceleration of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Takahashi a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Takahashi are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Takahashi’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Takahashi et al. (US 20160014406 A1), Paragraph [0009-10]).
Fernandez in view of Pillai, Tan, and further in view of Takahashi fails to explicitly teach an activation of an audible, visible, or haptic warning.
However, Lubbe explicitly teaches an activation of an audible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
visible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
or haptic warning (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan and Takahashi of having a method comprising: obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; and repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary with the teachings of Lubbe an activation of an audible, visible, or haptic warning.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein an activation of an audible, visible, or haptic warning.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Lubbe are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Lubbe’s system provides an increase in driver reaction based on data obtained. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Lubbe et al. (Brake reactions of distracted drivers to pedestrian Forward Collision Warning systems), Section 1.1 Paragraph [0002-3] and Section 5).
Regarding claim 20, Fernandez in view of Pillai and Tan teaches the apparatus of Claim 11,
Fernandez explicitly teaches, wherein the identified at least one action comprises at least one of: an adjustment to at least one of (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.):
a braking of the vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
Fernandez in view of Pillai and Tan fail to explicitly teach a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
However, Takahashi explicitly teaches a steering of a vehicle (Fig. 1, Paragraph [0112]- Takahashi discloses the vehicle drive control unit 104 performs the cruise assist control such as reporting a warning to a driver of the vehicle 100, and controlling the steering and brakes of the vehicle),
a speed of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.),
an acceleration of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having an apparatus comprising: at least one processing device configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary and identify one or more locations of one or more components of the detected object based on the identified regions and the determined similarities with the teachings of Takahashi a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Takahashi are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Takahashi’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Takahashi et al. (US 20160014406 A1), Paragraph [0009-10]).
Fernandez in view of Pillai, Tan, and further in view of Takahashi fails to explicitly teach an activation of an audible, visible, or haptic warning.
However, Lubbe explicitly teaches an activation of an audible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
visible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
or haptic warning (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having an apparatus comprising: at least one processing device: configured to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary with the teachings of Lubbe an activation of an audible, visible, or haptic warning.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein an activation of an audible, visible, or haptic warning.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Lubbe are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Lubbe’s system provides an increase in driver reaction based on data obtained. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Lubbe et al. (Brake reactions of distracted drivers to pedestrian Forward Collision Warning systems), Section 1.1 Paragraph [0002-3] and Section 5).
Regarding claim 30, Fernandez in view of Pillai and Tan teaches the non-transitory machine-readable medium of Claim 21,
Fernandez explicitly teaches, wherein the identified at least one action comprises at least one of: an adjustment to at least one of (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.):
a braking of the vehicle (Fig. 6, Paragraph [0053]- Fernandez discloses in response to the detection, the FCW system may take an appropriate action such as warning the driver or applying brakes.);
Fernandez in view of Pillai and Tan fail to explicitly teach a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
However, Takahashi explicitly teaches a steering of a vehicle (Fig. 1, Paragraph [0112]- Takahashi discloses the vehicle drive control unit 104 performs the cruise assist control such as reporting a warning to a driver of the vehicle 100, and controlling the steering and brakes of the vehicle),
a speed of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.),
an acceleration of the vehicle (Fig. 1, Paragraph [0121]- Takahashi discloses when performing these processing, vehicle operation information such as vehicle speed, acceleration (acceleration in front-to-rear direction of vehicle), steering angle, and yaw rate of the vehicle 100 can be input using the data IF 124, and such information can be used as parameters for various processing. Data output to the external unit can be used as input data used for controlling various devices of the vehicle 100 such as brake control, vehicle speed control, and warning control.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary with the teachings of Takahashi a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein a steering of a vehicle, a speed of the vehicle, and acceleration of the vehicle.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Takahashi are both systems for detecting objects particularly vehicles. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Takahashi’s system provides a further increase in accuracy of obtaining data. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Takahashi et al. (US 20160014406 A1), Paragraph [0009-10]).
Fernandez in view of Pillai, Tan, and further in view of Takahashi fails to explicitly teach an activation of an audible, visible, or haptic warning.
However, Lubbe explicitly teaches an activation of an audible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
visible (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.),
or haptic warning (Fig. 1, Section 3.1 Paragraph [0001]- Lubbe discloses of particular interest to this study were three HMIs: an audio-visual warning, a combination of brake pulse and audio-visual warning, and a novel HUD design highlighting the threat in combination with an audio-visual warning.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Fernandez in view of Tan of having a non-transitory machine-readable medium containing instructions that when executed cause at least one processor to: obtain a refined boundary identifying a specified portion of a detected object within a scene, the refined boundary associated with image data; repeatedly, during multiple iterations, (i) identify multiple regions within the refined boundary with the teachings of Lubbe an activation of an audible, visible, or haptic warning.
Wherein having Fernandez’s system of object detection particularly for vehicles wherein an activation of an audible, visible, or haptic warning.
The motivation behind the modification would have been to allow for more accurate data to be obtained, since both Fernandez and Lubbe are both systems that use detection of parts of a car. Wherein Fernandez’s system wherein improved the accuracy of the data obtained, while Lubbe’s system provides an increase in driver reaction based on data obtained. Please see Fernandez et al. (US 20140133698 A1), Paragraph [0069] and Lubbe et al. (Brake reactions of distracted drivers to pedestrian Forward Collision Warning systems), Section 1.1 Paragraph [0002-3] and Section 5).
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Bell et al. (US 20170069092 A1)- An industrial vehicle is provided comprising a drive mechanism, a steering mechanism, a vehicle controller, a camera, and a navigation module. The camera is communicatively coupled to the navigation module, the vehicle controller is responsive to commands from the navigation module, and the drive mechanism and the steering mechanism are responsive to commands from the vehicle controller. The camera is configured to capture an input image of a warehouse ceiling comprising elongated skylights characterized by different rates of image intensity change along longitudinal and transverse axial directions, and ceiling lights characterized by a circularly symmetric rate of image intensity change. The navigation module is configured to distinguish between the ceiling lights and the skylights and send commands to the vehicle controller for localization, or to navigate the industrial vehicle through the warehouse based upon valid ceiling light identification, valid skylight identification, or both.............Please see Fig. 1. Abstract.
Yokoi et al. (US 20170091565 A1)- A vehicle detection apparatus detects an object based on an image captured from a vehicle. In the vehicle detection apparatus, a camera is mounted in the vehicle. A search region determining unit determines a region including a road surface in the image to be a search region. A cost minimizing unit divides the search region into two, an upper region and a lower region, by a boundary line, determines corresponding points in a plurality of images captured by the camera at different time points by applying an optical flow model having a fixed magnification factor to the upper region and an optical flow model having a planar constraint condition to the lower region, and determines a boundary line that minimizes cost related to a difference between pixel values of the corresponding points. A vehicle presence determining unit determines presence of a forward vehicle based on the boundary line...............Please see Fig. 1. Abstract.
Katoh et al. (US 20120099763 A1)- An image recognition part of an image recognition apparatus recognizes an object based on a target area in an outside-vehicle image obtained by a camera installed in a vehicle. A position identifying part identifies an optical axis position of the camera relative to the vehicle based on the outside-vehicle image, and an area changing part changes a position of the target area in the outside-vehicle image according to the optical axis position of the camera. Therefore, it is possible to recognize an object properly based on the target area in the outside-vehicle image even though the optical axis position of the camera is displaced...............Please see Fig. 1. Abstract.
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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