Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/21/2024 is considered and attached.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of:
EP23218884.7, filed in Europe on 12/20/2023.
Copies of certified papers required by 37 CFR 1.55 have been retrieved.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 13, “an object identification module configured to …” The corresponding structure is disclosed in the specification, ¶ [0034 and 0041-0043]: “an object identification module arranged to analyse an image frame to identify an object and create a bounding box around the object … Creation of the bounding box may be performed, for example, using a convolutional neural network, such as those mentioned previously” Therefore, the interpretation of the “an object identification module” is a computing system and equivalent thereof.
Claim 13, “an event data segmentation module configured to …” (Regarding the event data segmentation module, the specification does not include enough information to determine the structure for the event data segmentation module. More specifically, the specification does not clarify what the structure of the event data segmentation module is (i.e. processor, CPU, camera) in enough detail to determine whether or not to interpret the event data segmentation module and its corresponding functional language as a computer-implemented 112(f) limitation. See the 112(b) rejections below regarding this matter. However, for the purposes of prior art, it is assumed that applicant intends for the corresponding structure of the event data segmentation module to be a processing unit. As such, solely for the purposes of prior art searching, the “event data segmentation module” is being interpreted as computer-implemented 112(f), wherein the structure for the event data segmentation module is a processing/computing system (or equivalent structure) and the corresponding algorithm. However, regarding the event data segmentation module, along with the lack of structure, applicant fails to include any corresponding algorithm regarding the steps required to carry out the function of the event data segmentation module. This is further explained in the 112(b) rejections below.)
Claim 13, “a trajectory estimation module configured to …” The corresponding structure is disclosed in the specification, ¶ [0072]: “The trajectory estimation module may include a tensor processing unit (TPU) …” Therefore, the interpretation of the “a trajectory estimation module” is a computing system and equivalent thereof.
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.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When reviewing independent claim 1, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claim(s) 1-17 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze Claim 1, and similar rationale applies to independent claim/s 13. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a process since the claim is directed to a method.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 1 comprise a mental process that can be practicably performed in the human mind therefore, an abstract idea.
Claim 1 recites:
analyzing an image frame to identify an object (a human can identify an object in an image, using a pen and paper, as a mental process as an abstract idea);
creating a bounding box around the object (a human can create a bounding box that encloses a region of interest of the identified object (e.g., draw a rectangle/ boundary on a diagram/drawing), using a pen and paper, as a mental process as an abstract idea);
filtering event data based on the position of the bounding box to obtain filtered event data for the object (a human can review an image and disregard the information outside a bounding box, using a pen and paper, as a mental process as an abstract idea);
analyzing the filtered event data to determine a trajectory of the object (a human can review the position of a bounding box of an object in an image over time, and estimate a trajectory/path of the object, using a pen and paper, as a mental process as an abstract idea).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Because both product and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
As such, a person could identify an object in an image, and create a rectangle that encloses a region of interest (bounding box of an object), review another image and disregard image information outside a bounding box, review positions of a bounding box over time to estimate a trajectory/path of the object, either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a device/in a device (e.g. processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1-15 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Claim 13 recites
“an object identification module”, “an event data segmentation module”, “a trajectory estimation module” (instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea).
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
With regard to (2b) the Guidance provided the following examples of limitations that may be enough to qualify as “significantly more" when recited in a claim with a judicial exception:
Improvement to another technology or technical field
Improvement to functioning of computer itself and/or applying the judicial exception with, or by use of, a particular machine
Effecting a transformation or reduction of a particular article to a different state or thing.
Adding a specific limitation other that what is well understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application
Meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment.
The Guidance further set forth limitations that were found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
Adding words to “apply it” (or an equivalent) with the judicial exception or mere instructions to implement abstract ideas on a computer
Simply appending well-understood, routine and conventional activities previously known to the industry specified at a high level of generality to the judicial exception, e.g. a claim to an abstract idea requiring no more than a generic
Computer to perform generic computer functions that are well -understood, routine and conventional activities previously known to the industry.
Adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering in conjunction with a law of nature or abstract idea
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Claims 1-15 do not recite any additional elements that are not well-understood, routine or conventional.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above identified additional computer components, using instructions to apply the judicial exception, are merely generic computer components that are well-known, routine, and conventional as is evidenced by Bancorp Services v. Sun Life (Fed. Cir. 2012) and Alice Corp. v. CLS Bank (2014).
Thus, since claims 1 and 13 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1 and 13 are not eligible subject matter under 35 U.S.C 101. Similar analysis is made for the dependent claims 2-12 and 14-15 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
Regarding claim 13, claim limitation “event data segmentation module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Regarding the “event data segmentation module”, applicant appears to describe the event data segmentation module in paragraphs 0034, 0069 and figure 2. However, none of these paragraphs describe a structure for the event data segmentation module. It is not even clear if the event data segmentation module is a structure or computer implemented 112(f) limitation, which requires a structure (usually a storage medium) and a specific algorithm. The event data segmentation module is merely shown as a box within the system in the Figure 2, and there is nothing in the specification that would imply a structure for the event data segmentation module, or that the event data segmentation module is a computer-implement 112(f) limitation. Therefore, in this instance event data segmentation module” is interpreted as a 112(f) limitation and the specification fails to disclose a specific structure for the event data segmentation module. However, as noted above in the 112(f) section, solely for the purposes of prior art searching and claim interpretation, the “event data segmentation module” is being interpreted as computer-implemented 112(f), wherein the structure for the preprocessing submodule is a processor (or equivalent structure) and the corresponding algorithm. Since the applicant fails to include a corresponding algorithm or structure for the event data segmentation module, the applicant further fails to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the event data segmentation module.
Claims 14-15 are rejected as being dependent upon claim 13.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 8-9, 11-12, and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US-20250191329-A1, foreign priority claimed 12/12/2023, hereinafter Zhou) in view of Zhang et al. (Zhang, Yu, et al. "Real-time vehicle detection based on improved yolo v5." Sustainability 14.19, published 2022, hereinafter Zhang).
CLAIM 1
Regards Claim 1, Zhou teaches a method for identifying objects (Zhou, ¶ [0019-0022]: “an event camera-based time-to-collision estimation method… a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, … the target front vehicle in the front image is identified by YOLOv5, and tracked by a DeepSort algorithm …”) and determining their trajectories (Zhou, ¶ [0031-0033]: “normalized coordinates of a target contour point corresponding to each target event are transformed into normalized coordinates of a reference moment tref by time-variant affine transformation … tk represents a time stamp of the target event, v=[vx, vy, vz] represents a relative instantaneous speed of the host vehicle compared with the target front vehicle at the reference moment tref, vx, vy, vz respectively represent the components of v in the xyz directions … ”, see reconstructed text of ¶0033 below. Zhou teaches determining coordinates of a point at a future time based on historical position and motion data),
the method comprising:
analyzing an image frame to identify an object (Zhou, ¶ [0019-0022]: “an event camera-based time-to-collision estimation method… a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, … the target front vehicle in the front image is identified by YOLOv5, and tracked by a DeepSort algorithm …”);
Zhou teaches bounding box (Zhou, ¶ [0019-0022]: “a bounding box of a target front vehicle is tracked”), however, Zhou does not explicitly disclose creating a bounding box around the object.
Zhang is in the same field of art of object detection and classification using YOLOv5. Further, Zhang teaches creating a bounding box around the object. (Zhang, section 4.1. YOLOv5 Algorithm: “The process of the YOLO algorithm is as follows: First, the image is divided into S × S meshes. Each grid is responsible for predicting the target where the actual box will fall in the center of the grid. A total of S × S × B bounding boxes are generated from these meshes. Each bounding box contains five parameters: Target center point coordinates, target width and height dimensions (x, y, w, h), and confidence of whether the target is contained. S × S grids predict the category probability of the target in that grid. The prediction bounding box confidence and category probability are then multiplied to obtain the category score for each prediction box. These prediction boxes are filtered by non-maximum suppression (NMS) to obtain the final prediction results”, see section 4.2 and FIG. 5. Zhang teaches YOLOv5 output the detection and classification result as generated bounding box)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou by incorporating the YOLOv5 based system that is taught by Zhang, to make an improved YOLOv5 network trained with a diverse dataset; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the performance of the task object detection, especially for small targets (Zhang, page 16, section 6: “this article used the improved YOLO v5 network for object detection. Diverse datasets increased the accuracy of the detection of vehicle targets at the root cause. Based on this network, the Flip-Mosaic data enhancement method was studied, which significantly improved the recognition rate of similar small targets and was more in line with the requirements of engineering practice. These improvements can play a significant role in practical applications.”).
The combination of Zhou and Zhang then teaches filtering event data based on the position of the bounding box to obtain filtered event data for the object (Zhou, ¶ [0020, 0023 and 0028]: “S101, a stream of events is acquired in real time by an event camera of a host vehicle, a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, events located beyond the bounding box are eliminated … S102, a stream of events within time At are extracted from the front vehicle events as target events” Zhou teaches removing events that placed outside the bounding box, and extracting events within bounding box); and
analyzing the filtered event data to determine a trajectory of the object (Zhou, ¶ [0031-0033]: “normalized coordinates of a target contour point corresponding to each target event are transformed into normalized coordinates of a reference moment tref by time-variant affine transformation … tk represents a time stamp of the target event, v=[vx, vy, vz] represents a relative instantaneous speed of the host vehicle compared with the target front vehicle at the reference moment tref, vx, vy, vz respectively represent the components of v in the xyz directions … ”, see reconstructed text below. Zhou teaches determining coordinates of a point at a future time based on historical position and motion data. The Examiner note without a definition of ‘trajectory’ in the claim, the Examiner interprets trajectory as position information of an object over time).
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Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 8
Regarding claim 8, the combination of Zhou and Zhang teaches the method of Claim 1. In addition, the combination of Zhou and Zhang teaches analyzing the image frame to identify the object comprises using a convolutional neural network. (Zhou, ¶ [0019-0022]: “an event camera-based time-to-collision estimation method… a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, … the target front vehicle in the front image is identified by YOLOv5, and tracked by a DeepSort algorithm …”)
CLAIM 9
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Regarding claim 9, the combination of Zhou and Zhang teaches the method of Claim 1. In addition, the combination of Zhou and Zhang teaches analyzing the image frame comprises classifying the object into one of a plurality of predefined classes (Zhang, section 3.2: “Target vehicles were divided into 8 categories according to the difficulty of distinguishing between vehicle classes in the images captured by the high camera, including Bus, Minibus, Family Sedan, Taxi, Heavy Truck, Truck, SUV, and Special Vehicle”, see FIG. 3 and annotated FIG. 5 below)
CLAIM 11
Regarding claim 11, the combination of Zhou and Zhang teaches the method of Claim 1. In addition, the combination of Zhou and Zhang teaches the event data includes data for one or more events; the one or more events corresponds to a change in light intensity (Zhou, ¶ [0021]: “the event camera has the characteristics of low latency, high dynamic range, very low power consumption compared to the conventional frame camera, and outputs a change in luminance of a pixel, and outputs an event when the luminance change of a pixel accumulates to a threshold value … When the luminance of a large number of pixels changes in a scenario caused by object motion or illumination changes, a series of events are generated, which are output in a stream of events”); and the event data includes one or more of: a time of the event; a position of the event; and a polarity of the change in light intensity. (Zhou, ¶ [0021]: “An event has three elements: a time stamp, pixel coordinates and polarity (lightening or darkening)”)
CLAIM 12
Regarding claim 12, the combination of Zhou and Zhang teaches the method of Claim 1. In addition, the combination of Zhou and Zhang teaches the event data includes data for one or more events (Zhou, ¶ [0021]: “the event camera has the characteristics of low latency, high dynamic range, very low power consumption compared to the conventional frame camera, and outputs a change in luminance of a pixel, and outputs an event when the luminance change of a pixel accumulates to a threshold value … When the luminance of a large number of pixels changes in a scenario caused by object motion or illumination changes, a series of events are generated, which are output in a stream of events”); and
filtering event data based on the position of the bounding box comprises excluding events not substantially within the bounding box. (Zhou, ¶ [0020, 0023 and 0028]: “S101, a stream of events is acquired in real time by an event camera of a host vehicle, a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, events located beyond the bounding box are eliminated … S102, a stream of events within time At are extracted from the front vehicle events as target events” Zhou teaches removing events that placed outside the bounding box, and extracting events within bounding box)
CLAIM 13
Regarding Claim 13, Zhou teaches a system (Zhou, abstract: “an event camera-based time-to-collision estimation method, an electronic device and a storage medium”) for identifying objects (Zhou, ¶ [0019-0022]: “an event camera-based time-to-collision estimation method… a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, … the target front vehicle in the front image is identified by YOLOv5, and tracked by a DeepSort algorithm …”) and determining trajectories of the objects (Zhou, ¶ [0031-0033]: “normalized coordinates of a target contour point corresponding to each target event are transformed into normalized coordinates of a reference moment tref by time-variant affine transformation … tk represents a time stamp of the target event, v=[vx, vy, vz] represents a relative instantaneous speed of the host vehicle compared with the target front vehicle at the reference moment tref, vx, vy, vz respectively represent the components of v in the xyz directions … ”, see reconstructed text of ¶0033 below. Zhou teaches determining coordinates of a point at a future time based on historical position and motion data), comprising:
an object identification module (Zhou, ¶ [0050]: “ the electronic device includes a memory module 21 and a processor 22, the memory module 21 including instructions loaded and executed by the processor 22 which, when executed, cause the processor 22 to perform steps according to various exemplary embodiments of the present disclosure that are described in the above-mentioned section of the description of the event camera-based time-to-collision estimation method”) configured to analyze an image frame to identify an object (Zhou, ¶ [0019-0022]: “an event camera-based time-to-collision estimation method… a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, … the target front vehicle in the front image is identified by YOLOv5, and tracked by a DeepSort algorithm …”)
Zhou teaches bounding box (Zhou, ¶ [0019-0022]: “a bounding box of a target front vehicle is tracked”), however, Zhou does not explicitly disclose creating a bounding box around the object.
Zhang is in the same field of art of object detection and classification using YOLOv5. Further, Zhang teaches creating a bounding box around the object. (Zhang, section 4.1. YOLOv5 Algorithm: “The process of the YOLO algorithm is as follows: First, the image is divided into S × S meshes. Each grid is responsible for predicting the target where the actual box will fall in the center of the grid. A total of S × S × B bounding boxes are generated from these meshes. Each bounding box contains five parameters: Target center point coordinates, target width and height dimensions (x, y, w, h), and confidence of whether the target is contained. S × S grids predict the category probability of the target in that grid. The prediction bounding box confidence and category probability are then multiplied to obtain the category score for each prediction box. These prediction boxes are filtered by non-maximum suppression (NMS) to obtain the final prediction results”, see section 4.2 and FIG. 5. Zhang teaches YOLOv5 output the detection and classification result as generated bounding box)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou by incorporating the YOLOv5 based system that is taught by Zhang, to make an improved YOLOv5 network trained with a diverse dataset; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the performance of the task object detection, especially for small targets (Zhang, page 16, section 6: “this article used the improved YOLO v5 network for object detection. Diverse datasets increased the accuracy of the detection of vehicle targets at the root cause. Based on this network, the Flip-Mosaic data enhancement method was studied, which significantly improved the recognition rate of similar small targets and was more in line with the requirements of engineering practice. These improvements can play a significant role in practical applications.”).
The combination of Zhou and Zhang then teaches an event data segmentation module (Zhou, ¶ [0050]: “ the electronic device includes a memory module 21 and a processor 22, the memory module 21 including instructions loaded and executed by the processor 22 which, when executed, cause the processor 22 to perform steps according to various exemplary embodiments of the present disclosure that are described in the above-mentioned section of the description of the event camera-based time-to-collision estimation method”) configured to filter event data based on the position of the bounding box to obtain filtered event data for the object (Zhou, ¶ [0020, 0023 and 0028]: “S101, a stream of events is acquired in real time by an event camera of a host vehicle, a front image is acquired in real time by a frame camera of the host vehicle, a bounding box of a target front vehicle is tracked in real time, events located beyond the bounding box are eliminated … S102, a stream of events within time At are extracted from the front vehicle events as target events” Zhou teaches removing events that placed outside the bounding box, and extracting events within bounding box); and
a trajectory estimation module (Zhou, ¶ [0050]: “ the electronic device includes a memory module 21 and a processor 22, the memory module 21 including instructions loaded and executed by the processor 22 which, when executed, cause the processor 22 to perform steps according to various exemplary embodiments of the present disclosure that are described in the above-mentioned section of the description of the event camera-based time-to-collision estimation method”) configured to analyze the filtered event data to determine a trajectory of the object (Zhou, ¶ [0031-0033]: “normalized coordinates of a target contour point corresponding to each target event are transformed into normalized coordinates of a reference moment tref by time-variant affine transformation … tk represents a time stamp of the target event, v=[vx, vy, vz] represents a relative instantaneous speed of the host vehicle compared with the target front vehicle at the reference moment tref, vx, vy, vz respectively represent the components of v in the xyz directions … ”, see reconstructed text below. Zhou teaches determining coordinates of a point at a future time based on historical position and motion data. The Examiner note without a definition of ‘trajectory’ in the claim,
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the Examiner interprets trajectory as position information of an object over time).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 14
Regarding claim 14, the combination of Zhou and Zhang teaches the system of Claim 13. In addition, the combination of Zhou and Zhang teaches an event camera configured to capture the event data. (Zhou, ¶ [0008 and 0020]: “acquiring a stream of events in real time by an event camera of a host vehicle”)
CLAIM 15
Regarding claim 15, the combination of Zhou and Zhang teaches the system of Claim 13. In addition, the combination of Zhou and Zhang teaches a Red Green Blue (RGB) camera configured to capture the image frame. (Zhou, ¶ [0008 and 0020]: “acquiring a front image in real time by a frame camera of the host vehicle”) (Zhang, page 11, first paragraph: “YOLO v5 inherits the meshing ideas of the YOLO algorithm. The network input dimensions are 640 × 640 × 3. That is the three-channel RGB color picture with a length and width of 640 after the original image preprocessing”)
Claim(s) 2 and 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Zhang, and further in view of Shair et al. (El Shair, Zaid, and Samir A. Rawashdeh. "High-temporal-resolution object detection and tracking using images and events." Journal of imaging 8.8, published 2022, hereinafter Shair).
CLAIM 2
In regards to Claim 2, the combination of Zhou and Zhang teaches the method of Claim 1.
The combination of Zhou and Zhang does not explicitly disclose filtering event data based on the position of the bounding box is performed in a synchronous manner.
Shair is in the same field of art of combining event data and image frame data for object identification. Further, Shair teaches filtering event data based on the position of the bounding box is performed in a synchronous manner. (Shair, pages 6-8, section 3.2.1: “an event-representation method is required. In our work, we accumulate events for a certain interval and incorporate them into a window frame, along with any available image frames … Accordingly, whenever a window frame containing an image is read, frame-based object detectors output a list of 2D bounding boxes with corresponding object classes for each, as described in Section 3.1. Whenever these detections are fed into the object tracker, we generate an event mask per object detected. These event masks are used to accurately detect and localize the identified objects, using the event data, in the subsequent window frames containing events only … Event-based masks are produced by extracting all the accumulated events (available in the most recent window frame) that are located within the bounding box of each object detected in the image, as shown in Figure 4.” Zhou teaches synchronizing image frame data and event data into a representation called window frame, and using bounding box detected from image frame data to extract corresponding events. See modified
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FIG. 3 below)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou and Zhang by incorporating the synchronized image-event window frame representation that is taught by Shair, to make an object identification system with specialized data representation that include synchronized image and events; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve tracking of dynamic shaped objects at rapid rates (Shair, section 3.2.1, last paragraph: “In general, the window frame would include all of the events available within the time interval l{t∈R+|ti−50ms≤t≤ti} at a given time instant ti. Incorporating a longer temporal history of events can produce higher tracking accuracy”; page 19, second paragraph: “our work shows that a hybrid approach that leverages both image and event data to generate higher tracking temporal resolutions is feasible, with very consistent performance … Moreover, when considering tracking different object types, we note that classical approaches might not be ideal for objects of dynamic shapes that change at very rapid rates”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 5
In regards to Claim 2, the combination of Zhou and Zhang teaches the method of Claim 1.
The combination of Zhou and Zhang does not explicitly disclose analyzing a second image frame to identify the object; creating a second bounding box around the object; filtering event data based on the position of the second bounding box to obtain filtered event data for the object for the second image frame.
Shair is in the same field of art of combining event data and image frame data for object identification. Further, Shair teaches analyzing a second image frame to identify the object; creating a second bounding box around the object; filtering event data based on the position of the second bounding box to obtain filtered event data for the object for the second image frame. (Shair, section 3.1. and section 3.2.1: “an event-representation method is required. In our work, we accumulate events for a certain interval and incorporate them into a window frame, along with any available image frames … Accordingly, whenever a window frame containing an image is read, frame-based object detectors output a list of 2D bounding boxes with corresponding object classes for each, as described in Section 3.1. Whenever these detections are fed into the object tracker, we generate an event mask per object detected. These event masks are used to accurately detect and localize the identified objects, using the event data, in the subsequent window frames containing events only … Event-based masks are produced by extracting all the accumulated events (available in the most recent window frame) that are located within the bounding box of each object detected in the image, as shown in Figure 4.” Zhou teaches detecting bounding box in multiple subsequent image frames, then combine them with event data to obtain event-based mask of detected objects.
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See modified FIG. 3 below)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou and Zhang by incorporating the synchronized image-event window frame representation that is taught by Shair, to make an object identification system with specialized data representation that include synchronized image and events; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve tracking of dynamic shaped objects at rapid rates (Shair, section 3.2.1, last paragraph: “In general, the window frame would include all of the events available within the time interval l{t∈R+|ti−50ms≤t≤ti} at a given time instant ti. Incorporating a longer temporal history of events can produce higher tracking accuracy”; page 19, second paragraph: “our work shows that a hybrid approach that leverages both image and event data to generate higher tracking temporal resolutions is feasible, with very consistent performance … Moreover, when considering tracking different object types, we note that classical approaches might not be ideal for objects of dynamic shapes that change at very rapid rates”).
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The combination of Zhou, Zhang and Shair then teaches analyzing the filtered event data for the second image frame to determine the trajectory of the object. (Zhou, ¶ [0031-0033]: “normalized coordinates of a target contour point corresponding to each target event are transformed into normalized coordinates of a reference moment tref by time-variant affine transformation … tk represents a time stamp of the target event, v=[vx, vy, vz] represents a relative instantaneous speed of the host vehicle compared with the target front vehicle at the reference moment tref, vx, vy, vz respectively represent the components of v in the xyz directions … ”, see reconstructed text below. Zhou teaches determining coordinates of a point at a future time based on historical position and motion data).
(Also see Shair, page 12, section 3.3: “Euclidean distance is a metric that is used to find the optimal assignments to be able to track objects across subsequent frames at any given point with a low computational cost … ” Shair teaches using a Euclidean distance based method to track and predict position of object across subsequent frames.)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 6
In regards to Claim 6, the combination of Zhou, Zhang and Shair teaches the method of Claim 5. In addition, the combination of Zhou, Zhang and Shair teaches between the analyzing of the first image frame and the second image frame: filtering the event data is based on the position of the first bounding box (Shair, section 3.2.1: “Accordingly, whenever a window frame containing an image is read, frame-based object detectors output a list of 2D bounding boxes with corresponding object classes for each, as described in Section 3.1. Whenever these detections are fed into the object tracker, we generate an event mask per object detected…the first window frame would contain an image as well as events, whereas the second would only contain events. Similarly, the third window frame would contain both, while the fourth would contain only events, and so on, as shown in Figure 3”, Shair teaches extracting first event data based on first detected bounding box in the first window frame, then second event data based on second detected bounding box in the third window frame;
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see annotated FIG. 3 below);
and the filtered event data for the first image frame is analyzed to determine the trajectory of the object. (Shair, see FIG. 9, the tracking result is updated at each window frame, meaning between consecutive image frames)
CLAIM 7
In regards to Claim 7, the combination of Zhou, Zhang and Shair teaches the method of Claim 5. In addition, the combination of Zhou, Zhang and Shair teaches after the analyzing of the second image frame:
filtering the event data is based on the position of the second bounding box (Shair, section 3.2.1: “Accordingly, whenever a window frame containing an image is read, frame-based object detectors output a list of 2D bounding boxes with corresponding object classes for each, as described in Section 3.1. Whenever these detections are fed into the object tracker, we generate an event mask per object detected…the first window frame would contain an image as well as events, whereas the second would only contain events. Similarly, the third window frame would contain both, while the fourth would contain only events, and so on, as shown in Figure 3”, Shair teaches extracting first event data based on first detected bounding box in the first window frame, then second event data based on second detected bounding box in the third window frame; see annotated FIG. 3 below);
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and the filtered event data for the second image frame is analyzed to determine the trajectory of the object. (Shair, see FIG. 9, the tracking result is updated at each window frame, see FIG. 3 for example, the tracking result at window frame 3 is updated after analyzing second image frame)
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Zhang, and further in view of Wang et al. (Wang, Song, et al. "Spikemot: Event-based multi-object tracking with sparse motion features." IEEE, published 09/29/2023, hereinafter Wang).
CLAIM 3
In regards to Claim 3, the combination of Zhou and Zhang teaches the method of Claim 1.
The combination of Zhou and Zhang does not explicitly disclose analyzing the filtered event data to determine a trajectory of the object is performed in an asynchronous manner.
Wang is in the same field of art of event-based object identification and tracking system. Further, Wang teaches analyzing the filtered event data to determine a trajectory of the object is performed in an asynchronous manner. (Wang, section 2.3, first paragraph: “SNNs are naturally suitable for addressing event-driven vision challenges due to their inherent asynchronous computational abilities and adeptness in processing sparse spatiotemporal data … Unlike conventional artificial neural networks (ANNs) that depend on continuous activations, SNNs rely on discrete spike events for inter-neuronal communication.”; pages 3-5, section 3. Wang teaches Spiking Neural Network based system that identify and track object across time based on discrete event data. Specifically, SpikeMOT embeds SNNs into a Siamese architecture to extract and associate sparse spatiotemporal features from the incoming stream of events. The system isolates and tracks moving objects by focusing on the active "spikes" of events asynchronously.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou and Zhang by incorporating the Spiking Neural Network based system for object identification and tracking that is taught by Wang, to make a system that identify and track objects based on a certain “spikes” of events; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to achieve high tracking accuracy while reducing computational load for such system (Wang, pages 4-5, section 3.2.1: “our model eliminates pre-synaptic kernels located preceding the synapse network and incorporates post-synaptic kernels within the neurons. This modification significantly reduces computational costs.”; page 12, section 6: “Experimental results reveal that SpikeMOT achieves state-of-the-art tracking accuracy”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 4
In regards to Claim 4, the combination of Zhou and Zhang teaches the method of Claim 1.
The combination of Zhou and Zhang does not explicitly disclose analyzing the filtered event data to determine the trajectory of the object comprises using a spiking neural network..
Wang is in the same field of art of event-based object identification and tracking system. Further, Wang teaches analyzing the filtered event data to determine the trajectory of the object comprises using a spiking neural network.. (Wang, section 2.3, first paragraph: “SNNs are naturally suitable for addressing event-driven vision challenges due to their inherent asynchronous computational abilities and adeptness in processing sparse spatiotemporal data … Unlike conventional artificial neural networks (ANNs) that depend on continuous activations, SNNs rely on discrete spike events for inter-neuronal communication.”; pages 3-5, section 3. Wang teaches a Spiking Neural Network based system that identify and track object across time based on discrete event data.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou and Zhang by incorporating the Spiking Neural Network based system for object identification and tracking that is taught by Wang, to make a system that identify and track objects based on a certain “spikes” of events; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to achieve high tracking accuracy while reducing computational load for such system (Wang, pages 4-5, section 3.2.1: “our model eliminates pre-synaptic kernels located preceding the synapse network and incorporates post-synaptic kernels within the neurons. This modification significantly reduces computational costs.”; page 12, section 6: “Experimental results reveal that SpikeMOT achieves state-of-the-art tracking accuracy”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 10
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Zhang, and further in view of Sanket et al. (Sanket, Nitin J., et al. "Evdodgenet: Deep dynamic obstacle dodging with event cameras." IEEE, published 2020, hereinafter Sanket).
In regards to Claim 10, the combination of Zhou and Zhang teaches the method of Claim 1.
The combination of Zhou and Zhang teaches does not explicitly disclose analyzing the trajectory of the object to avoid a collision between an aircraft and the object.
Sanket is in the same field of art of event-based neural network for object identification and tracking. Further, Sanket teaches analyzing the trajectory of the object to avoid a collision (Sanket, pages 10654-10655, sections A and B. Sanket teaches predicting trajectory of both known and unknown object, analyzing predicted trajectory to determine a safe direction for the drone to avoid the object) between an aircraft and the object. (Sanket, Abstract: “we present a deep learning based solution for dodging multiple dynamic obstacles on a quadrotor with a single event camera and on-board computation” The Examiner notes quadrotors are unmanned aerial vehicles (UAVs) or drones, see FIG. 1a)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhou and Zhang by incorporating the even-based neural network that is taught by Sanket, to make a system to identify, track and dodge objects; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve object identification and tracking, especially for unknown shaped objects (Sanket, abstract: “We successfully evaluate and demonstrate the proposed approach in many real-world experiments with obstacles of different shapes and sizes, achieving an overall success rate of 70% including objects of unknown shape and a low light testing scenario”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHUT HUY (JEREMY) PHAM whose telephone number is (703)756-5797. The examiner can normally be reached Mo - Fr. 8:30am - 6pm ET.
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/NHUT HUY PHAM/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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