DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
Claims 1-20 are pending. Claims 1, 12, and 20 are the independent claims. Claims 1, 6-12, and 16-20 have been amended. This office action is in response to the Amendments received on 03/02/2026.
Response to Arguments
With respect to Applicant’s remarks filed on 03/02/2026, “Applicant Arguments/Remarks Made in an Amendment” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant's arguments according to the rejections of claims 1-3, 5-7, 9-11,12-13, 15-16, and 18-19 under 35 U.S.C § 101, has been considered, but is not persuasive. Regarding claims 1 and 12, the amended claims encompass the newly added limitations of “generate, in view of the first determination associated with the first confidence value and the second determination associated with the second confidence value, one or more instructions to a vehicle control system to change at least a lane of motion of the vehicle in the driving environment or a speed of the vehicle in the driving environment.”. The recitation of generating instruction to a vehicle control system to change lane of motion of the vehicle or the speed of the vehicle, would not be suffice to overcome the rejection of the claim under 35 U.S.C § 101, because the controlling mechanism of the vehicle has not been positively recited in the claim, rather it is just generating an instruction to a vehicle control system which can be performed in human mind and falls under mental process. For example, according to paragraph [0048] of the instant specification, the instruction which is output by VCS (vehicle control system) can first be delivered to the vehicle electronics (which can include in-car entertainment systems), therefore it could be merely, for example, displaying the instruction and not specifically controlling the vehicle. Also, applicants argued that using determination based on machine learning model, integrate abstract idea into a practical application, but the argument is not persuasive. The machine learning model is recited at a high level of generality such that it amounts to no more than mere instructions to use the machine learning model as a tool to perform the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept. See MPEP § 2106.05(f). Note: Claim 20 recites the limitation “direct the autonomous vehicle based on the one or more instructions”. Under the broadest reasonable interpretation of the examiner in of the present specification, this limitation has been interpreted as controlling the vehicle. For example, paragraph [0034] of the specification, discloses “a planner system of the vehicle can cause the vehicle control system to direct the vehicle to the open lanes.”. Accordingly, directing the autonomous vehicle has been interpreted as controlling the autonomous vehicle, therefore, claim 20 has not been rejected under 35 U.S.C § 101.
Applicant’s arguments with respect to the rejections of claims under 35 U.S.C § 103, on page 12 title “Response to Rejections under 35 U.S.C § 103”, have been fully considered but they are not persuasive. First, according to Argument I, applicant states that none of the prior art relied upon for rejection of claim 1 teaches generating instruction in view of the first and the second limitation, i.e. none of the prior arts teach using both a first and second determination together to identify block lanes. As cited for the rejection of claim 1, in non-final office action filed on 12/02/2025, Zhang teaches the method applied for first determination and Famiglietti teaches method applied for the second determination. Therefore, the combination of two previously known method/teaching would be obvious to one ordinary in the art to arrive at the claimed limitation. See MPEP 2144.06 I. Further according to MPEP 2141 [R-01.2024] I, the combination of two pre-existing element that yield no more than one would expect, would be obvious. Id. “In Anderson’s-Black Rock, Inc. v. Pavement Salvage Co., . . . [t]he two [pre-existing elements] in combination did no more than they would in separate, sequential operation.” Id. at 416-17, 82 USPQ2d at 1395. (3) “[I]n Sakraida v. AG Pro, Inc., the Court derived . . . the conclusion that when a patent simply arranges old elements with each performing the same function it had been known to perform and yields no more than one would expect from such an arrangement, the combination is obvious.” Id. at 417, 82 USPQ2d at 1395-96 (Internal quotations omitted.). Also, applicant is reminded that one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The claim, as written, recites generating instruction to vehicle control system in view of first determination and second determination. This is the office stance that combining the prior arts of record which teaches first determination and second determination would be arrived at the claimed limitation.
Second, according to Argument II, applicant states that none of the prior arts relied upon teaches or suggested combining a marker based first determination with an MLM-based second determination, therefore, the motivation statement is a conclusory statement. The office, respectfully, disagrees. The rationale to modify or combine the prior art does not have to be expressly stated in the prior art; the rationale may be expressly or impliedly contained in the prior art or it may be reasoned from knowledge generally available to one of ordinary skill in the art, established scientific principles, or legal precedent established by prior case law. See MPEP 2144 [R-01.2024] I and II.
Third, Argument II, applicant’s argument regarding the newly added limitation of first and second determination being associated with a first and second confidence value, respectfully, are considered. The applicant’s argument is not persuasive. Please see the rejections for the aforementioned limitations, as currently presented in office action below.
Office Note: Due to applicant’s amendments, further claim rejections and clarification appear on the record as stated in the below Office Action.
It is the Office’ stance that all of applicant arguments have been considered.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 contains a typographical error at the last line: “of a speed of the vehicle in the driving environment”, should be “or a speed of the vehicle in the driving environment”.
Appropriate correction is required.
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.
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: a sensing system in claims 1, and 20 and data processing system in claims 1, 3, 5-8, 10-11, and 20. According to paragraphs [0025]- [0026], [0041]-[0044] of the specification of the instant application, sensing system is interpreted as various electromagnetic (e.g., optical) and non-electromagnetic (e.g., acoustic) sensing subsystems and/or devices including lidars, radars, cameras, on-board microphone and/or the like, and/or other sensors, such as sonars. Furthermore, data processing system is interpreted according [0044]-[0047], [0020]-[0022], and [0117]-[0124].
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 1-3, 5-7, and 9-13, 15-16, and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category – Yes
Claims 1-20 are directed to a method. Therefore, the claim falls within at least one of the four statutory categories. See MPEP 2106.03
Step 2A Prong I evaluation: Judicial Exception – Yes – Mental processes
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
In this case independent claims 1 and 12 are directed to an abstract idea without significantly more. Claim 1 recites:
“A system comprising: a sensing system of a vehicle, the sensing system configured to acquire sensing data associated with a driving environment; a data processing system of the vehicle, the data processing system configured to: identify one or more obstruction markers associated with the driving environment based on the sensing data; obtain, based on the one or more obstruction markers, a first determination associated with a first confidence value that an object is obstructing traffic in the driving environment; obtain a second determination associated with a second confidence value that the object is obstructing traffic in the driving environment by applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data; and generate, in view of the first determination associated with the first confidence value and the second determination associated with the second confidence value, one or more instructions to a vehicle control system to change at least a lane of motion of the vehicle in the driving environment of a speed of the vehicle in the driving environment.”
The Office submits that the foregoing bolded limitations constitute judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind”. See MPEP 2106.04(a)(2)(III). For example, the limitations of “identifying one or more obstruction markers… obtaining, based on the one or more obstruction markers, a first and second determination whether an object is obstructing traffic in the driving environment…, and generate, in view of the first determination associated with the first confidence value and the second determination associated with the second confidence value, one or more instructions to a vehicle control system to change at least a lane of motion of the vehicle in the driving environment of a speed of the vehicle in the driving environment” in the context of this claim encompasses processes that can be performed in human mind it falls under mental process that is a category of abstract idea. Accordingly, the claim recites at least one abstract idea.
Step2A Prong II evaluation: Practical Application – No
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitations recite additional elements that do not integrate the recited judicial exception into a practical application. The claim recites the additional element of “applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data” that is recited at a high level of generality and does not use the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The limitations of “a sensing system of a vehicle, the sensing system configured to acquire sensing data associated with a driving environment; a data processing system of the vehicle,” are no more than mere instructions to apply the exception using a computer. 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 evaluation: Inventive Concept – No
In Step 2B of the 2019 PEG, the claim(s) is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data” amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the Office submits that these limitations are insignificant extra-solution activities. Further, additional element of “a sensing system of a vehicle, the sensing system configured to acquire sensing data associated with a driving environment; a data processing system of the vehicle,” is gathering data which is considered as extra solution activity and amount to no more that the judicial exception.
Claim 12 recites limitation for a system that comprise the same abstract of claim 1. Therefore, claim 20 is also patent ineligible for the same reasons stated in the above for claim 1 rejection.
Dependent claims 2-3, 5-7, and 9-11 (and corresponding claims 13, 15, 16, 18 and 19) do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Claims 2-3, 5-7 (and their corresponding claims) recites more description about the limitations of determining whether an object is obstructing traffic. Claim 9 and 18 recite performing a statistical analysis on the output data of machine learning that can be done in human mind by using pen and paper and falls under mental process analysis. Claims 10-11 (and corresponding claim 19) recite the steps of modifying the driving path based on a determining a cost associated with travel in a blocked lane, which is a conceptual process and falls under the mental process analysis, and do not impose any meaningful limits on practicing the abstract idea. Therefore, dependent claims 2-3, 5-7, and 9-11 (and corresponding claims 13, 15, 16, 18 and 19) are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6, 12-13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, US 20250242799 A1, hereinafter “Fearon”, in view of Zhang et al., US20230399021, hereinafter “Zhang”, further in view of Famiglietti et al., US 20240290143 A1, hereinafter “Famiglietti”.
Regarding claims 1 and 12, Fearon teaches a system comprising: a sensing system of a vehicle, the sensing system configured to acquire sensing data associated with a driving environment ([0025]); a data processing system of the vehicle ([0017], “processing system”, [0024], [0032]),, the data processing system configured to: identify one or more obstruction markers associated with the driving environment based on the sensing data ([0017], [0043], [0033] “determine the size and orientation of a box encompasses the object” __the obstruction markers according to claim 2 can be heading direction of the object which is the orientation of the object (According to paragraph [0062] of the instant application). The disclosure of Fearon according to at least Abstract teaches determining the size, position and orientation (heading direction) of the object intruding the lane, which reads on the claimed limitation__);
Although Fearon teaches determination whether an object is obstructing traffic in the driving environment, according to Fearon paragraph [0043], however, Fearon doesn’t explicitly teach obtain, based on the one or more obstruction markers, a first determination associated with a first confidence value that an object is obstructing traffic in the driving environment
Nevertheless, Zhang teaches obtain, based on the one or more obstruction markers, a first determination associated with a first confidence value that an object is obstructing traffic in the driving environment (([0059], “the perception system can classify the objects into one or more of the following semantic labels: [], emergency lights, emergency signs, emergency vehicles ”__Note: according to the specification of the current application, e.g., paragraph [0027] and also claim 2, the obstruction markers are defined as factors that are used to identify if an object obstructs a traffic lane. One of these factors is, for example, determining a presence of one or more emergency vehicles. The cited paragraph of Zhang meets this limitation. __ , [0083], “determine a confidence level for each set of data associated with the restricted traffic zone.”, __Note: according to Zhang’s disclosure, an example of determining the boundary of the restricted traffic zones is a determining a lane closure, therefore Zhang teaches determination associated with a confidence value as recited in the claim__);
Further, Although Fearon teaches obtain a second determination associated with a second confidence value that the object is obstructing traffic in the driving environment by applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data ([0003], “computing a metric that captures an extent to which the object intrudes on the lane based on one or more vertices of the bounding box, determining that the metric satisfies a threshold, and triggering a collision avoidance response.”, [0014], [0019], __metric value reads on a confidence level__, Fig. 7, 0045], “metric ”);
However, for the purpose of compact prosecution, and in alternative rejection for this part of the claim, Famiglietti also teaches obtain a second determination associated with a second confidence value that the object is obstructing traffic in the driving environment by applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data; ([0014], “Examples of temporary traffic scenes can include a stopped school bus, a construction zone, a road closure, a human controlling traffic (e.g., police officer or construction worker), an emergency vehicle (e.g., police car, fire truck, ambulance, etc.), a road blockage (e.g., due to a vehicle accident), a traffic redirection, etc.”, [0017], “the scene selectors may also include a confidence level threshold that is associated with the AV detector output.”, __Note: confidence level threshold is associated with the AV detector output. An AV detector corresponds to the output of an AV software module (e.g., a perception stack) that is configured to detect objects in the environment. Therefore, confidence level taught by Famiglietti reads on the confidence value recited in the claim__, Also see [0045], “the outputs of the perception stack 112 (e.g., object classification, object position, confidence score, etc.).”, [0049], “the AV (e.g., the perception stack 112) may output a confidence score that is associated with the AV detector.”, [0071], “scene selector 306 may be implemented using a machine learning algorithm. For example, scene selector 306 may include a machine learning model that is configured to identify scene datasets 308 corresponding to a selected traffic scene based on sensor data that is collected by the AV and is part of AV road data 302 and/or AV simulation data 304.”, [0095]-[0096].
Although none of the forgoing references teaches generating in view of the first determination and the second determination, one or more instructions to a vehicle control system to change at least a lane of motion of the vehicle in the driving environment or a speed of the vehicle in the driving environment, however, Zhang teaches the aforementioned limitation according to the first determination and Fearon (or in alternative Famiglietti) teaches the aforementioned limitation according to the second determination as follow:
Zhang teaches generate, in view of the first determination associated with the first confidence value [ (Zhang:[0036, [0041], “The course of action to be taken may include slowing, stopping, moving into a shoulder, changing route, changing lane while staying on the same general route, and the like.”, [0047]-[0049]
Fearon teaches generate, in view of [change at least a lane of motion of the vehicle in the driving environment or a speed of the vehicle in the driving environment ([0018]-[0019], “initiating a corrective action”, Fig. 6, [0038], “actions include a driver alert or warning and intervention to apply brakes or perform evasive steering.”, [0046], __evasive steering reads on changing lane__).
Also, in alternative, Famiglietti teaches generate, in view of [ ([Famiglietti: 0039], “generate and transmit instructions regarding the operation of the AV 102”) [0020], [0029], “The planning stack 118 can determine multiple sets of one or more mechanical operations that the AV 102 can perform (e.g., go straight at a specified rate of acceleration, including maintaining the same speed or decelerating;”, [0044], Fig. 2, [0062], [0066]-[0067]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane by determining its size, position and orientation as taught by Fearon to include first determination in identifying obstacles as obstruction markers (like any emergency vehicle blocking the road) as taught by Zhang and combine it with second determination in identifying the blocked lane as taught by Fearon, or in alternative, Famiglietti, with a reasonable expectation of success, to predict whether an object is obstructing traffic in the driving environment. The motivation of combining the forgoing references would be improving efficiency, precision and reliability of the system in determination whether an object is constructing traffic and instruct the autonomous vehicle to perform accordingly.
Regarding claim 2, Fearon in view of prior arts relied upon teach the system of claim 1, however, Fearon doesn’t explicitly teach wherein the one or more obstruction markers comprise one or more of: a heading direction of the object, a presence of one or more emergency vehicles (EVs) in the driving environment, a presence of one or more uniformed officers in the driving environment, or a presence of one or more emergency signals in the driving environment.
However, Zhang teaches wherein the one or more obstruction markers comprise a presence of one or more emergency vehicles (EVs) in the driving environment ([0059], “the perception system can classify the objects into one or more of the following semantic labels: [], emergency lights, emergency signs, emergency vehicles”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane by determining its size, position and orientation as taught by Fearon to include identifying obstacles as obstruction markers (like any emergency vehicle blocking the road) as taught by Zhang, with the motivation of increasing precision and safety of the driving path of an autonomous vehicle by accurately identifying various objects in the driving environment of the vehicle.
Regarding claim 3, Fearon in view of prior arts relied upon teaches the system of claim 2, and although Fearon discloses determining a metric/ratio based on positioning of the object relative to lane width and determining whether that the ratio satisfies a threshold as a factor considered by a collision avoidance system for triggering the intervention (at least according to abstract and paragraph [0019] of Fearon), which implicitly reads on the limitation (ii) of claim 3 however, Fearon doesn’t explicitly teach wherein to obtain the first determination, the data processing system is configured to determine that at least (i) a number of the one or more EVs in the driving environment is greater than one, or (ii) an angle between a reference direction in the driving environment and the heading direction of the object exceeds a threshold angle.
Zhang teaches wherein to obtain the first determination, the data processing system is configured to determine that at least (i) a number of the one or more EVs in the driving environment is greater than one, or (ii) an angle between a reference direction in the driving environment and the heading direction of the object exceeds a threshold angle ([0062]- [0063], “relative positioning”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane as taught by Fearon to include the determination of a blocked lane based on comparing the angle between the object and a reference in the driving environment exceeding a threshold as taught by Zhang, with the motivation of using this factor for the determination step if the lane is blocked in the driving path of the vehicle and increasing the accuracy of the results of the perception system of the vehicle.
Regarding claim 6, Fearon in view of aforementioned prior arts teaches the system of claim 1, and Fearon teaches wherein to generate the one or more instructions ([0018]-[0019], “initiating a corrective action”, Fig. 6, [0038]), the data processing system is configured to: determine that, according to at least one of the first determination or the second determination, the object is obstructing an individual lane in the driving environment (at least [0001], [0019], “determine the extent to which an object blocks a lane traveled by a vehicle”, [0029], [0043]).
Regarding claim 13, it encompasses the limitations of claims 2 and 3, therefore, it is rejected for the same rational as for claims 2 and 3.
Regarding claim 20, Fearon teaches An autonomous vehicle comprising ([0023]): a sensing system configured to acquire sensing data associated with a driving environment, the sensing data comprising one or more of: one or more camera images of the driving environment ([0025]), one or more lidar images of the driving environment ([0025], [0043], [0051]), or one or more radar images of the driving environment ([0025], [0029], [0043]); a data processing system configured to: identify one or more obstruction markers associated with the driving environment based on the sensing data ([0017], [0043], [0033] “determine the size and orientation of a box encompasses the object” __the obstruction markers according to claim 2 can be heading direction of the object which is the orientation of the object (According to paragraph [0062] of the instant application). The disclosure of Fearon according to at least Abstract teaches determining the size, position and orientation (heading direction) of the object intruding the lane, which reads on the claimed limitation__);
Although Fearon teaches determination whether an object is obstructing traffic in the driving environment, according to Fearon e.g. paragraphs [0043] and [0033], however, Fearon doesn’t explicitly teach obtain, based on the one or more obstruction markers, a first determination associated with a first confidence value that an object is obstructing traffic in the driving environment
Nevertheless, Zhang teaches obtain, based on the one or more obstruction markers, a first determination associated with a first confidence value that an object is obstructing traffic in the driving environment (([0059], “the perception system can classify the objects into one or more of the following semantic labels: [], emergency lights, emergency signs, emergency vehicles ”__Note: according to the specification of the current application, e.g., paragraph [0027] and also claim 2, the obstruction markers are defined as factors that are used to identify if an object obstructs a traffic lane. One of these factors is, for example, determining a presence of one or more emergency vehicles. The cited paragraph of Zhang meets this limitation. __ , [0083], “determine a confidence level for each set of data associated with the restricted traffic zone.”, __Note: according to Zhang’s disclosure, an example of determining the boundary of the restricted traffic zones is a determining a lane closure, therefore Zhang teaches determination associated with a confidence value as recited in the claim__);
Further, Although Fearon teaches obtain a second determination associated with a second confidence value that the object is obstructing traffic in the driving environment by applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data ([0003], “computing a metric that captures an extent to which the object intrudes on the lane based on one or more vertices of the bounding box, determining that the metric satisfies a threshold, and triggering a collision avoidance response.”, [0014], [0019], __metric value reads on a confidence level__, Fig. 7, 0045], “metric ”);
However, for the purpose of compact prosecution, and in alternative rejection for this part of the claim, Famiglietti also teaches obtain a second determination associated with a second confidence value that the object is obstructing traffic in the driving environment by applying a first machine learning model (MLM) to a first input comprising at least a portion of the sensing data; ([0014], “Examples of temporary traffic scenes can include a stopped school bus, a construction zone, a road closure, a human controlling traffic (e.g., police officer or construction worker), an emergency vehicle (e.g., police car, fire truck, ambulance, etc.), a road blockage (e.g., due to a vehicle accident), a traffic redirection, etc.”, [0017], “the scene selectors may also include a confidence level threshold that is associated with the AV detector output.”, __Note: confidence level threshold is associated with the AV detector output. An AV detector corresponds to the output of an AV software module (e.g., a perception stack) that is configured to detect objects in the environment. Therefore, confidence level taught by Famiglietti reads on the confidence value recited in the claim__, Also see [0045], “the outputs of the perception stack 112 (e.g., object classification, object position, confidence score, etc.).”, [0049], “the AV (e.g., the perception stack 112) may output a confidence score that is associated with the AV detector.”, [0071], “scene selector 306 may be implemented using a machine learning algorithm. For example, scene selector 306 may include a machine learning model that is configured to identify scene datasets 308 corresponding to a selected traffic scene based on sensor data that is collected by the AV and is part of AV road data 302 and/or AV simulation data 304.”, [0095]-[0096].
Although none of the forgoing references teaches generate in view of the first determination and the second determination, one or more instructions to a vehicle control system to change at least a lane of motion of the vehicle in the driving environment or a speed of the vehicle in the driving environment, however, Zhang teaches the aforementioned limitation according to the first determination and Fearon (or in alternative Famiglietti) teaches the aforementioned limitation according to the second determination as follow:
Zhang teaches generate, in view of the first determination associated with the first confidence value [ (Zhang: [0036], [0041], “The course of action to be taken may include slowing, stopping, moving into a shoulder, changing route, changing lane while staying on the same general route, and the like.”, [0047]-[0049]
Fearon teaches generate, in view of [([0018]-[0019], “initiating a corrective action”, Fig. 6, [0038], “actions include a driver alert or warning and intervention to apply brakes or perform evasive steering.”, [0046], __evasive steering reads on changing lane__).
Also, in alternative, Famiglietti teaches generate, in view of [ ([Famiglietti: 0039], “generate and transmit instructions regarding the operation of the AV 102”) [0020], [0029], “The planning stack 118 can determine multiple sets of one or more mechanical operations that the AV 102 can perform (e.g., go straight at a specified rate of acceleration, including maintaining the same speed or decelerating;”, [0044], Fig. 2, [0062], [0066]-[0067]).
Further, Fearon teaches a driving control system configured to: direct the autonomous vehicle based on the one or more instructions ([0024], Fig. 6 block 640, [0046], “The intervention can be in the form of a driver warning or alert or corrective action such as applying vehicle brakes or performing evasive steering,”), or in alternative, Zhang also teaches a driving control system configured to: direct the autonomous vehicle based on the one or more instructions ([0039], “the VCU 150 may send information to the vehicle control subsystems 146 to direct the trajectory,”, [0041], “The course of action to be taken may include [] changing route,”, “The vehicle control subsystems 146 then cause the autonomous vehicle 105 to operate in accordance with the course of action to be taken that was received from the VCU”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane by determining its size, position and orientation as taught by Fearon to include first determination in identifying obstacles as obstruction markers (like any emergency vehicle blocking the road) as taught by Zhang and combine it with second determination in identifying the blocked lane as taught by Fearon, or in alternative, Famiglietti, with a reasonable expectation of success, to predict whether an object is obstructing traffic in the driving environment. The motivation of combining the forgoing references would be improving efficiency, precision and reliability of the system in determination whether an object is constructing traffic and instruct the autonomous vehicle to perform accordingly.
Claim(s) 4, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, in view of Zhang, further in view of Famiglietti, and further in view of Malla et al., US 11577757 B2, hereinafter “Malla” (or in alternative rejection in view of Afshar et al., US20240132112, hereinafter “Afshar”).
Regarding claims 4 and 14, although Fearon teaches using the machine learning method for determining an obstacle intruding a lane, however, Fearon doesn’t explicitly disclose/teach the limitations recited in claim 4. However, these limitations encompass common steps in neural network of machine learning method that are well-known in prior arts. As an example, and for the purpose of compact prosecution, Malla also teaches the limitations recited in the claim. For clarity, Malla teaches wherein the first MLM comprises: an encoder neural network (NN) configured to process one or more of: one or more road graph features representing a map of the driving environment (at least Abstract, Fig.4, Col 5 Lines 30-36, Col 8 Lines 37-55, Col 9 last 2 paragraph, Col 15 2nd and 3rd paragraphs); one or more traffic light features representing status of one or more traffic lights in the driving environment (Col 8 Lines 52-55); and one or more object track features representing motion history of one or more objects in the driving environment (at least Col 13, 3rd paragraph, Col 15 2nd paragraph, Col 16 2nd paragraph); and a decoder NN configured to process an output of the encoder NN (Col 16 Lines15-43); and one or more classification heads configured to classify, using an output of the decoder NN, the one or more objects among a plurality of types associated with traffic obstruction (Col 5 Line 20-36, Col 6 Line 30-49, Col 9, Lines 33-47, “object classification”, Col 12, Lines 30-43), or as another example of alternative rejection, Afshar teaches wherein the first MLM comprises: an encoder neural network (NN) configured to process one or more of: one or more road graph features representing a map of the driving environment ([0074], [0119]-[0122], Fig. 8); ([0069], “classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like).”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane using machine learning method as taught by Fearon to include well-known and routine steps in neural network algorithm as encoding input data, decoding and classifying the output in order to determine if an object is obstructing a lane as ,for example, taught by Malla or Afshar examples, with reasonable expectation of success, with the motivation of improving the precision and safety of driving path selected by an autonomous vehicle.
Claim(s) 5, 8, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, in view of Zhang, further in view of Famiglietti, and further in view of Deshmukh et al., US 20250292459 A1, hereinafter “Deshmukh”.
Regarding Claims 5 and 15, Fearon in view of aforementioned prior arts teaches the system of claims 1 and 12, and Fearon teaches wherein the sensing data comprises one or more camera images of the driving environment ([0025]), and wherein the data processing system is further configured to: obtain a third determination whether the object is obstructing traffic in the driving environment by applying a vision MLM to a second input to ([0033], [0043], “machine learning and computer vision technologies may be utilized to analyze data collected by sensors to identify objects such as other vehicles”), wherein the second input comprises: one or more camera images of the driving environment (at least [0033], “analysis of local vehicle sensor data, external sensor data, or both including camera images or video. For example, an object's shape, size, and orientation can be determined through image processing of images provided by cameras mounted on a vehicle as well as traffic cameras or other sensors. In one instance, object detection can be employed by the bounding component 210, which employs computer vision and image processing techniques to detect instances of particular objects”)
Fearon doesn’t disclose one or more text tokens, each associated with a corresponding type of one or more types of traffic obstruction.
However, Deshmukh teaches one or more text tokens, each associated with a corresponding type of one or more types of traffic obstruction ([0251], “a CNN executing on the DLA or dGPU (e.g., the GPU(s) 1920) may include a text and word recognition, allowing the supercomputer to read and understand traffic signs,”, [0252], “the text “Flashing lights indicate icy conditions” may be interpreted by a second deployed neural network”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane using machine learning and computer vision methods based on the input data such as camera images as taught by Fearon to include input data as text tokens associated with traffic obstruction as taught by Deshmukh, with reasonable expectation of success, with the motivation of improving the accuracy of the method by processing variety of different kind of input data such as images and texts.
Regarding claims 8 and 17, modified Fearon teaches the system of claim 1, however, modified Fearon doesn’t explicitly disclose/teach the limitations recited in claim 8 and 17.
Nevertheless, Deshmukh teaches wherein to generate the one or more instructions (At least Abstract, “provide guidance for autonomous driving”, [0072], “use this information (e.g., instances of obstacles) to localize its position in a map, to navigate, plan, or otherwise perform one or more operations (e.g., obstacle or protuberance avoidance, lane keeping, lane changing, merging, splitting, adapting a suspension system of the ego-machine to match the current road surface”, [0072], [0075]-[0076], “The obstacle perceiver [] based on where the vehicle 1900 is allowed to drive or is capable of driving (e.g., based on the location of the drivable or other navigable paths defined by avoiding detected obstacles in the environment and/or detected protuberances in the road surface)”, [0087]), the data processing system is configured to: process, using an encoder NN of a blocked lane detection MLM, a second input, wherein the second input comprises: one or more roadgraph features representing a map of the driving environment (at least Figs. 1 and 2 “Road graph generator”, [0008], [0048], [0068]-[0069]); one or more lane features, each representing an individual lane in the driving environment (at least Abstract, [0006]-[0008], “The inferred lane data, such as cross-sections and/or connection indicators, may be used to generate a lane graph that represents lanes on a road”); and one or more blockage features representing presence of one or more blocking accessories in the driving environment ([0002], [0072], “use this information (e.g., instances of obstacles) to localize its position in a map, to navigate, plan, or otherwise perform one or more operations (e.g., obstacle or protuberance avoidance,”, [0075]-[0076] __identifying the location of detected obstacles along the path/lane and perceiving where the vehicle is allowed to drive or capable of driving based on that reads representing a blockage in the driving environment__); and generate an indication of the one or more blocked lanes by processing a third input using a decoder NN of the blocked lane detection MLM, wherein the third input comprises: an output of the encoder NN, and the one or more lane features representing individual lanes in the driving environment (at least Figs. 1 and 2, Figs. 17 and 18, [0010], “a DNN for use in generating lane data includes encoder and decoder components”, [0011], [0069]-[0070], [0110]-[0114], “The decoder(s) 234 is generally configured to take, as input, output associated with the encoder”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane using machine learning method as taught by Fearon to include steps of data processing using neural network algorithm as inputting road graph features, encoding input data and decoding the output of the encoder, in order to determine if an object is obstructing a lane as taught by Deshmukh, with reasonable expectation of success, with the motivation of improving the precision and safety of driving path selected by an autonomous vehicle.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, in view of Zhang, further in view of Famiglietti, and further in view of Chen et al., US20220204029A1, hereinafter “Chen”.
Regarding claims 7 and 16, Fearon in view of aforementioned prior arts teaches the system of claims 1 and 12, and wherein to generate the one or more instructions ([0018]-[0019], “initiating a corrective action”, Fig. 6, [0038]), the data processing system is configured to: identify, using a heading direction of the object, a bounding box for the object ([0003], “generating a bounding box that captures size and position of an object in a lane”, [0029], [0033], “an object's shape, size, and orientation can be determined []. Once an object is detected, the bounding component 210 can determine the size and orientation of a box that encompasses the object.”); and identify one or more lanes intersecting the bounding box as the one or more blocked lanes (at least [0028]-[0030]).
Fearon doesn’t explicitly teaches wherein a size of the bounding box exceeds a size of the object by a predetermined amount.
Nevertheless, Chen teaches wherein a size of the bounding box exceeds a size of the object by a predetermined amount (at least [0054], “a bounding box can be expanded by a threshold amount, which may be based on a size of the object”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method pertain to identifying obstacles within a traffic lane by generating a bounding box to capture size and position of an object in a traffic lane as taught by Fearon with generating a size of the bounding box exceeding a size of the object as taught by Chen, with reasonable expectation of success, in order to allow the bounding box including all portions of the object (e.g., vehicle side mirrors as disclosed in paragraph [0054] of Chen), with the motivation of improving the safety by considering the desired safety margin.
Claim(s) 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, in view of Zhang, further in view of Famiglietti, and Deshmukh, further in view of Boydton et al., US 20220185330 A1, hereinafter “Boydton”.
Regarding claims 9 and 18, Although Fearon in view of prior arts relied upon teaches the system of claims 1 and 12, however, Fearon doesn’t explicitly teach wherein wherein to generate the one or more instructions, the data processing system is further to use a third determination whether the object is obstructing traffic, wherein the third determination is obtained using a heatmap of probabilities, outputted by a roadgraph drivability MLM, wherein an input in the roadgraph drivability MLM comprises: the sensing data, and a roadgraph information for the driving environment.
Nevertheless, Deshmukh teaches wherein to identify the one or more blocked lanes (([0072], “use this information (e.g., instances of obstacles) to localize its position in a map, to navigate, plan, or otherwise perform one or more operations (e.g., obstacle or protuberance avoidance, lane keeping, lane changing, merging, splitting, adapting a suspension system of the ego-machine to match the current road surface”, [0075]-[0076], “The obstacle perceiver [] based on where the vehicle 1900 is allowed to drive or is capable of driving (e.g., based on the location of the drivable or other navigable paths defined by avoiding detected obstacles in the environment and/or detected protuberances in the road surface)”, [0087]),, the data processing system is further to use a third determination whether the object is obstructing traffic, wherein the third determination is obtained (at least Figs. 1 and 2 “Road graph generator”, [0008], [0048], [0068]-[0069], ([0002], [0072], “use this information (e.g., instances of obstacles) to localize its position in a map, to navigate, plan, or otherwise perform one or more operations (e.g., obstacle or protuberance avoidance,”, [0075]-[0076]), wherein an input in the roadgraph drivability MLM comprises: the sensing data, and a roadgraph information for the driving environment (Fig. 1, step 104 and 112 ).
However, Deshmukh doesn’t teach determination is obtained using a heatmap of probabilities, outputted by a roadgraph drivability MLM
Nevertheless, Boydton teaches wherein to identify the one or more blocked lanes, the data processing system is further to use a third determination whether the object is obstructing traffic, wherein the third determination is obtained using a heatmap of probabilities, outputted by a roadgraph drivability MLM, wherein an input in the roadgraph drivability MLM comprises: the sensing data, and a roadgraph information for the driving environment (at least Abstract, [0001], [0012]-[0015], [0027], [0034], [0066], [0071], [0076], [0079], [0086], [0094]-[0096]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane using machine learning method as taught by Fearon to include steps of data processing using neural network algorithm as inputting road graph features as taught by Deshmukh and further to use heatmap probabilities as taught by Boydton, with reasonable expectation of success, with the motivation of improving the accuracy of the method of detecting a blocked driving path.
Claim(s) 10-11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fearon, in view of Zhang, further in view of Famiglietti, further in view of Beller, US20210094539, hereinafter “Beller”.
Regarding claim 10, Fearon in view of aforementioned prior arts relied upon teaches the system of claim 1, however, Fearon doesn’t teach generate the one or more instructions, the data processing system is configured to: determine a cost associated with travel in a blocked lane, wherein the cost increases with decreased distance to a blocked portion of the blocked lanes; and generate the one or more instructions in view of the determined cost.
Beller teaches generate the one or more instructions, the data processing system is configured to: determine a cost associated with travel in a blocked lane, wherein the cost increases with decreased distance to a blocked portion of the blocked lanes (Fig. 3 block 314, [0047], “the comfort cost may be associated with a distance between the vehicle 104 performing the associated action and the blocking object(s)”, [0046]-[0047], “the safety cost may increase the closer a vehicle 104 (traveling on a vehicle trajectory) gets to the blocking object(s)”); and generate the one or more instructions in view of the determined cost (at least Fig. 3, [0010], “The vehicle computing system may determine [] modify the trajectory of the vehicle to navigate around the object based on a likelihood that the object will continue to block the path.”, [0019], “the vehicle computing system may select the action based on an action having a lowest cost associated therewith”, [0065]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane as taught by Fearon to include determinin a cost associated with travel in a blocked lane as ta taught by Beller, with reasonable expectation of success, with the motivation of improving the accuracy of the system in determining the necessity of performing evasive action like modifying the driving path when approaching a blocked lane by considering a cost associating with the blocked lanes which increases by approaching to the blocked lanes.
Regarding claim 11, Fearon in view of aforementioned prior arts relied upon teaches the system of claim 1, however, Fearon doesn’t teach generate the one or more instructions, the data processing system is configured to: determine a cost associated with lateral encroachment, by the vehicle, into a blocked lane; and generate the one or more instructions in view of the determined cost.
Beller teaches generate the one or more instructions, the data processing system is configured to: determine a cost associated with lateral encroachment, by the vehicle, into a blocked lane ([0004], [0036], [0041], [0068], [0130]-[0138]); and generate the one or more instructions in view of the determined cost (at least Fig. 3, ([0010], [0019], [0036], [0047]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method pertain to identifying obstacles within a traffic lane as taught by Fearon to include determining a cost associated with lateral encroachment as ta taught by Beller, with reasonable expectation of success, with the motivation of improving the accuracy of the system in determining the necessity of performing evasive action like modifying the driving path by considering a cost associating with lateral movement of the vehicle into a blocked lane.
Regarding claim 19, it encompasses the limitations of claims 10 and 11, therefore, it is rejected for the same rationale as for claims 10 and 11.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.H./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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