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 Claims
This Office action is in response to the amendments filed on August 12, 2026. Claims 1-10 are currently pending, with Claims 1, 4, 7, and 9 being amended.
Response to Amendments
In response to Applicant’s amendments, filed August 12, 2026, the Examiner maintains the previous 35 U.S.C. 112(f) claim interpretation, withdraws the previous 35 U.S.C. 112 rejections, maintains the previous 35 U.S.C. 101 rejections, and withdraws the previous 35 U.S.C. 102 and 103 rejections.
Response to Arguments
Applicant’s arguments, filed August 12, 2026, pertaining to the 35 U.S.C. 101 rejection (see pages 1-4 of instant arguments), the Examiner is unpersuaded. Adding elements of how the Gaussian calculations are performed amounts to merely using a generic computer to implement mathematical concepts. There are no additional elements which implement the claims out of an abstract idea into a practical application. As such, the Examiner maintains the corresponding 35 U.S.C. 101 rejections.
Applicant’s arguments, filed August 12, 2026, with respect to the rejections of Claims 1-10 under Ditty, in view of Zhang, Kim, and Zheng, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Ditty, in view of Xiong, Zhang, Kim, and Zheng.
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 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 limitations use 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 limitations are:
“a perception sensor module …” in Claim 6.
“a data collection module …”, “a data-preprocessing module …”, and “a system positioning module …” in Claim 7.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
Regarding the limitations of “a perception sensor module …”, the “data collection module …”, “a data-preprocessing module …”, and “a system positioning module …”, the instant specification at Paragraphs [0017]-[0018] at least states “the perception sensor module, the data collection module, the data pre-processing module, and the system positioning module are embodied by at least one processor and at least one memory coupled to the at least one processor …” and “the hardware implementation of the data collection module, the data preprocessing module, and the system positioning module … are connect to the in-vehicle communication unit …”. The structure for these modules is software or hardware coupled to a processor for executing certain functions.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid 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 limitations recite sufficient structure to perform the claimed function so as to avoid 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea, determining information of a vehicle, including relative position, velocity, wheel speed, steering angle, and determining the relative position of another vehicle using Gaussian belief propagation calculations, which constitutes a mathematical concept. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims only recite steps of performing mechanical calculations for changing coordinate schemes and fusing data.
101 Analysis – Step 1
Claims 1 and 7 are directed to a fault-tolerant connected vehicle collaborative positioning method/system (i.e., a process). Therefore, Claims 1 and 7 are within at least one of the four statutory categories. Claims 2-6 and 8-10 are rejected due to their dependency on Claims 1 and 7, respectively.
101 Analysis – Step 2A, Prong I
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 following groups of a) an abstract idea, b) a law of nature, or c) a natural phenomenon.
In the present case, the additional limitations beyond the noted abstract ideas are as follows (where the bolded portions represent an “abstract idea”; and where the underlined portions are the “additional limitations”):
Claim 1 recites the following:
A fault-tolerant intelligent connected vehicle collaborative positioning method, comprising:
S1, collecting information of longitude, latitude and altitude of a current position of a target vehicle, data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle, and an estimated value of a relative distance between the target vehicle and a collaborative vehicle in real time;
S2, pre-processing the information of longitude, latitude and altitude of the current position of the target vehicle, and the data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle to obtain pre-processed information of longitude, latitude and altitude of the current position of the target vehicle and pre-processed data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle; and
calibrating the estimated value of the relative distance between the target vehicle and the collaborative vehicle to obtain a calibrated estimated value of the relative distance between the target vehicle and the collaborative vehicle; and
S3, performing iterative fusion of Gaussian belief propagation on the pre-processed information of longitude, latitude and altitude of the current position of the target vehicle, the pre- processed data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle and the calibrated estimated value to obtain a resultant position of the target vehicle; wherein the iterative fusion of Gaussian belief propagation comprises: performing a distributed edge fault detection and elimination algorithm on an edge of a factor graph corresponding to a positioning observation, wherein the distributed edge fault detection and elimination algorithm comprises: calculating a residual of the edge; performing a w-test based on the residual to obtain a test statistic of the w-test; and determining whether the positioning observation is faulty based on the test statistic of the w-test and a detection threshold; and in response to determining that the positioning observation is faulty, disconnecting the edge in the factor graph so that a message of the edge is prevented from being propagated to other nodes;
wherein the fault-tolerant intelligent connected vehicle collaborative positioning method further comprises: matching, based on the resultant position of the vehicle, a map to determine road signs, traffic signals and lane positions;
evaluating a relative position between obstacles ahead and the vehicle based on the resultant position of the vehicle; and
planning a path of the vehicle based on the road signs, the traffic signals, the lane positions and the relative position between obstacles ahead and the vehicle.
Regarding the limitations of “obtain…”, “calibrating …”, and “performing iterative fusion …”, the Examiner submits that these limitations consist of an abstract idea, including performing multiple calculations iteratively to determine a vehicle position estimate, which comprises mathematical concepts. A person can reasonably perform these steps with pencil and paper.
Regarding the limitations of “matching…”, “evaluating …”, and “planning …”, the Examiner submits that these limitations consist of data gathering, which is a form of insignificant extra-solution activity. The Examiner further submits that these limitations consist of a mental concept, as human can reasonably evaluate or make judgments mentally or by using pencil and paper to estimate the vehicle position. A driver can determine where they are on the road and compare it to a previously acquired map of that route, visually observe their location relative to objects, traffic signs, and other vehicles, and plan an action in response.
101 Analysis – Step 2A, Prong II
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 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 limitation of “A fault-tolerant intelligent connected vehicle collaborative positioning method …”, amounts to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
For the following reason(s), the Examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “collecting …”, “preprocessing …”, and “performing a distributed edge …”, the Examiner submits that these limitations comprise of the implementation of mathematical concepts, by processing, calibrating, and iteratively performing calculations. These limitations consist of data gathering, which is a form of insignificant extra-solution activity. The Parker v. Flook, and Bancorp Services v. Sun Life cases have indicated that performing repetitive calculations have been held by the courts as being well-understood, routine, and conventional in the art, and that In re Grams, SAP America, Inc. v. InvestPic, LLC, have shown that claims directed to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, implement/ use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Analysis of Step 2B is performed to determine if the claim as a whole amounts to significantly more than the exception itself, and further analysis is required for all functions that are identified as well-understood, routine, and conventional.
The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that merely inputting data is a well-understood, routine, and conventional function of data gathering and performing calculations on the data acquired to input into a model, when it is claimed in a merely generic manner (as it is here). The specification also demonstrates the well-understood, routine, and conventional nature of additional elements as it describes these elements as well-understood, routine, or conventional (or an equivalent term), as a commercially available produce, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a).
Dependent Claims 2-6 and 8-10 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of 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. The dependent claims further recite features of determining and changing coordinate schemes, determining a fault in a sensor, and using various sensors to acquire the data (i.e., extra-solution activity, as presented in the analysis above, and field of use).
Therefore, dependent Claims 2-6 and 8-10 are not patent eligible under the same rationale as provided for in the rejection of Claims 1 and 7, respectively.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0176577 A1, to Ditty, et al (hereinafter referred to as Ditty; previously of record), in view of “Integrity for Belief Propagation-Based Cooperative Positioning”, to Xiong, et al (hereinafter referred to as Xiong; newly of record).
As per Claim 1, and similarly for Claim 7, Ditty discloses the features of a fault-tolerant intelligent connected vehicle collaborative positioning method (e.g. Paragraphs [0124], [0163], [0272]; where the estimates of vehicle position, velocity, and attitude are determined to provide improved fault tolerance by using sensor fusion), comprising:
S1, collecting information of longitude, latitude and altitude of a current position of a target vehicle, data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle, and an estimated value of a relative distance between the target vehicle and a collaborative vehicle in real time (e.g. Paragraphs [0123]-[0124], [0135], [0163], [0538]-[0539], [0581]; where the sensors may gather position, velocity, and attitude information of the vehicle using a GPS-Aided Inertial Navigation System (GPS/INS), and 3D point and velocity information, acceleration, wheel speed, acceleration, roll, pitch, yaw, GPS position data, and a distance to a target object are determined; and the controller (100) uses an inertial measurement unit (IMU) to monitor the movement of the vehicle body (i.e., mileage or distance traveled));
S2, pre-processing the information of longitude, latitude and altitude of the current position of the target vehicle, and the data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle to obtain pre-processed information of longitude, latitude and altitude of the current position of the target vehicle and pre-processed data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle (e.g. Paragraphs [0272]-[0273]; where the system may perform preprocessing, which may include sensor fusion); and
calibrating the estimated value of the relative distance between the target vehicle and the collaborative vehicle to obtain a calibrated estimated value of the relative distance between the target vehicle and the collaborative vehicle (e.g. Paragraphs [0487], [0581]; where sensor calibration can be used to determine a sequence of sensor poses, where the lidar and radar point segments can be used to provide distance and bearing observations); and
S3, performing iterative fusion of Gaussian belief propagation on the pre-processed information of longitude, latitude and altitude of the current position of the target vehicle, the pre- processed data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle and the calibrated estimated value to obtain a resultant position of the target vehicle (e.g. Paragraphs [0290], [0618]; where the system iteratively performs determination of perception, planning and control, using a Gaussian mode for localization); ‘…’ wherein the fault-tolerant intelligent connected vehicle collaborative positioning method further comprises:
matching, based on the resultant position of the vehicle, a map to determine road signs, traffic signals and lane positions (e.g. Paragraphs [0537], [0648]; where the live perception may find traffic lights, and mapping may present the position of the traffic lights in 3D, and perform matching between mapped and lived perceived traffic lights;
evaluating a relative position between obstacles ahead and the vehicle based on the resultant position of the vehicle (e.g. Paragraphs [0021]-[0022], [0260], [0340], [0613]; where the system determines the vehicle’s position, distance, and speed of the vehicle ahead to determine an action; and receives information from other vehicles; and where position of road markings and road boundaries is determined relative to a vehicle pose); and
planning a path of the vehicle based on the road signs, the traffic signals, the lane positions and the relative position between obstacles ahead and the vehicle (e.g. Paragraphs [0257]-[0258], [0275], [0340], [0712]; where the planner negotiates with other vehicles; and where the system can read and understand traffic signs and flashing lights to inform the vehicle’s path planning software; and where the system can perform path planning based on perception and a generated occupancy grid, and the system may indicate that it is taking appropriate action in response to an identified traffic signal or sign, bicycle, pedestrian, or obstacle in the road).
Ditty fails to disclose every feature of where the iterative fusion of Gaussian belief propagation comprises: performing a distributed edge fault detection and elimination algorithm on an edge of a factor graph corresponding to a positioning observation, wherein the distributed edge fault detection and the elimination algorithm comprises: calculating a residual of the edge; performing a w-test based on the residual to obtain a test statistic of the w-test; and determining whether the positioning observation is faulty based on the test statistic of the w-test and a detection threshold; and in response to determining that the positioning observation is faulty, disconnecting the edge in the factor graph so that a message of the edge is prevented from being propagated to other nodes.
However, Xiong, in a similar field of endeavor, teaches the features of where the iterative fusion of Gaussian belief propagation comprises: performing a distributed edge fault detection and elimination algorithm on an edge of a factor graph corresponding to a positioning observation.
teaches a belief-propagation (BP) based cooperative integrity monitoring (BP-ICM) algorithm, where the BP-based cooperative integrity (BP-CIM) algorithm detects faulty observations at every edge of a factor graph (FG) in a distributed way, and excludes them from the estimation process using a fault detection and exclusion (FDE) process (e.g. Page 11346, Paragraph beginning with “To address these …”; Page 11351, Paragraph beginning with “Integrity monitoring algorithm …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for improving autonomous driving of Ditty, with the feature of performing edge fault detection and elimination in the system of Xiong, in order to improve the positioning performance of a vehicle (see at least Page 11345, Paragraph beginning with “In recent years …” of Xiong).
Xiong further teaches the features of wherein the distributed edge fault detection and the elimination algorithm comprises: calculating a residual of the edge.
Xiong teaches a belief-propagation (BP) based cooperative integrity monitoring (BP-ICM) algorithm, where the system detects abnormal positioning residuals, and filtering innovations (e.g. Page 11351, Paragraph beginning with “FDE is usually designed …” and Paragraph beginning with “Considering an edge …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for improving autonomous driving of Ditty, with the feature of calculating a residual in the system of Xiong, in order to remove faulty detections (see at least Page 11352, Paragraph beginning with “In GBP framework …” of Xiong).
Xiong further teaches the features of performing a w-test based on the residual to obtain a test statistic of the w-test; and determining whether the positioning observation is faulty based on the test statistic of the w-test and a detection threshold.
Xiong teaches a belief-propagation (BP) based cooperative integrity monitoring (BP-ICM) algorithm, where a w-test is performed to detect the faulty observations using a test statistic, and each edge compares its test statistic with detection threshold to detect the presence of faulty observations (e.g. Page 11352, Paragraph beginning with “To detect the faulty observations …” through Paragraph beginning with “In GBP framework …).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for improving autonomous driving of Ditty, with the feature of performing a w-test in the system of Xiong, in order to detect faulty detections (see at least Page 11352, Paragraph beginning with “In GBP framework …” of Xiong).
Xiong further teaches the features of in response to determining that the positioning observation is faulty, disconnecting the edge in the factor graph so that a message of the edge is prevented from being propagated to other nodes.
Xiong teaches a belief-propagation (BP) based cooperative integrity monitoring (BP-ICM) algorithm, where each edge compares its test statistic with detection threshold to detect the presence of faulty observations, and if detected as a faulty observation, the corresponding edge will be removed from the factor graph (FG) model (e.g. Page 11352, Paragraph beginning with “To detect the faulty observations …” through Paragraph beginning with “In GBP framework …).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for improving autonomous driving of Ditty, with the feature of disconnecting the edge in the system of Xiong, in order to remove faulty detections (see at least Page 11352, Paragraph beginning with “In GBP framework …” of Xiong).
As per Claim 6, Ditty, in view of Xiong, teaches the features of Claim 1, and Ditty further discloses the features of wherein the estimated value of the relative distance between the target vehicle and the collaborative vehicle is obtained through a perception sensor module (e.g. Paragraphs [0135], [0143], [0487]; where various perceptors are informed by sensors, including cameras for measuring distance to another object, or lidar sensors).
Claim 2-3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable Ditty, in view of Xiong, as applied to Claim 1 above, and further in view of “Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks”, to Zhang, et al (hereinafter referred to as Zhang; previously of record).
As per Claim 2, Ditty, in view of Xiong, teaches the features of Claim 1, but the combination of Ditty, in view of Xiong, fails to teach every feature of wherein a coordinate of the target vehicle, corresponding to the information of longitude, latitude and altitude of the current position of the target vehicle, in a longitude-latitude- altitude (LLA) coordinate system is converted to a coordinate of the target vehicle in an east-north- up (EDU) coordinate system before positioning calculation.
However, Zhang, in a similar field of endeavor, teaches a method for improving vehicle position information through cooperative driving, where the vehicle position in the LLA coordinate system is converted to the north-east-up coordinate system (e.g. Page 9036, Paragraph beginning with “Choosing the correct …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of converting to a common coordinate system in the system of Zhang, in order to execute successful navigation of the vehicle (see at least Page 9036, Paragraph beginning with “Choosing the correct …” of Zhang).
As per Claim 3, Ditty, in view of Xiong, and Zhang, teaches the features of Claim 2, and Zhang further teaches the features of wherein the coordinate of the target vehicle in the LLA coordinate system is first converted to a coordinate of the target vehicle in an Earth-centered Earth-fixed (ECEF) coordinate system, and then the coordinate of the target vehicle in the ECEF coordinate system is converted to the coordinate of the target vehicle in the EDU coordinate system.
Zhang teaches a method for improving vehicle position information through cooperative driving, where the vehicle position is converted from the LLA coordinate system to the earth centered earth fixed (ECEF) coordinate system, and then converted to the ENU coordinate system (e.g. Page 9036, Paragraph beginning with “Choosing the correct …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of converting to a common coordinate system in the system of Zhang, in order to execute successful navigation of the vehicle (see at least Page 9036, Paragraph beginning with “Choosing the correct …” of Zhang).
As per Claim 8, Ditty, in view of Xiong, teaches the features of Claim 7, but the combination of Ditty, in view of Xiong, fails to teach every feature of wherein a coordinate of the target vehicle, corresponding to the information of longitude, latitude and altitude of the current position of the target vehicle, in a LLA coordinate system is converted to a coordinate of the target vehicle in an EDU coordinate system before positioning calculation.
However, Zhang, in a similar field of endeavor, teaches a method for improving vehicle position information through cooperative driving, where the vehicle position is converted from the LLA coordinate system to the earth centered earth fixed (ECEF) coordinate system, and then converted to the ENU coordinate system (e.g. Page 9036, Paragraph beginning with “Choosing the correct …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of converting to a common coordinate system in the system of Zhang, in order to execute successful navigation of the vehicle (see at least Page 9036, Paragraph beginning with “Choosing the correct …” of Zhang).
Claim 4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable Ditty, in view of Xiong, as applied to Claims 1 and 7 above, respectively, and further in view of “Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks”, to Zhang, et al (hereinafter referred to as Zhang; previously of record), in view of U.S. Patent Publication No. 2020/0209883 A1, to Kim, et al (hereinafter referred to as Kim; previously of record).
As per Claim 4, and similarly for Claim 9, Ditty, in view of Xiong, teaches the features Claims 1 and 7, respectively, but the combination of Ditty, in view of Xiong, fails to teach every feature of wherein in response to there being no fault occurring in on-board ranging sensors of the vehicle, the vehicle identified as a host vehicle, and the collaborative vehicle identified as a neighboring vehicle, relative distance information obtained by the on-board ranging sensor of the host vehicle, relative distance information obtained by the on-board ranging sensor of the neighboring vehicle, and relative distance information obtained by vehicle-to-vehicle (V2V) communication are combined to obtain the estimated value of the relative distance between the target vehicle and the collaborative vehicle; in response to there being fault occurring in the on-board ranging sensors of the vehicle identified as the host vehicle, and the collaborative vehicle identified as the neighboring vehicle, when the on- board ranging sensor of the host vehicle fails, the relative distance information obtained by the on- board ranging sensor of the neighboring vehicle and the relative distance information obtained by V2V communication are fused to obtain the estimated value of the relative distance between the target vehicle and the collaborative vehicle.
However, Zhang, in a similar field of endeavor, teaches the features of wherein in response to there being no fault occurring in on-board ranging sensors of the vehicle, the vehicle identified as a host vehicle, and the collaborative vehicle identified as a neighboring vehicle, relative distance information obtained by the on-board ranging sensor of the host vehicle, relative distance information obtained by the on-board ranging sensor of the neighboring vehicle, and relative distance information obtained by vehicle-to-vehicle (V2V) communication are combined to obtain the estimated value of the relative distance between the target vehicle and the collaborative vehicle.
Zhang teaches a method for improving vehicle position information through cooperative driving, where each vehicle is modeled as a node, and sends its location and distance information to a server or to each other through a V2V network, and each vehicle determines its own GPS information and distance information (i.e., no fault with sensing), and observations are obtained from vehicle-to-everything or vehicle-to-vehicle frameworks, and the results are fused together to increase localization performance (e.g. Page 9035, Paragraph beginning with “In order to enhance …”).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of fusing all the data together in the system of Zhang, in order to enhance accuracy of vehicle positioning (see at least Page 9035, Paragraph beginning with “In order to enhance …” of Zhang).
Kim, in a similar field of endeavor, further teaches the features of in response to there being fault occurring in the on-board ranging sensors of the vehicle identified as the host vehicle, and the collaborative vehicle identified as the neighboring vehicle, when the on- board ranging sensor of the host vehicle fails, the relative distance information obtained by the on- board ranging sensor of the neighboring vehicle and the relative distance information obtained by V2V communication are fused to obtain the estimated value of the relative distance between the target vehicle and the collaborative vehicle.
Kim teaches a method for controlling an autonomous vehicle on the basis of another vehicle’s received information, where internal location data may be obtained using vehicle sensors, and external location data refers to location-related data that is externally obtained by surrounding vehicles, which may involve obtaining relative distance and direction information of other vehicles through communication links, such as vehicle to vehicle (v2v) (e.g. see Paragraphs [0057-[0059], [0061]), and the controller may compare an error between the internal absolute location and the external absolute location, and when the error does not exist, or is less than a threshold, the internal absolute location data is selected (i.e., no fault, or a minimal error with the host vehicle sensing), and when the error is equal to or greater than a threshold, the controller selects the external absolute location calculated by the external location estimator as the current location of the vehicle (i.e., the host vehicle has a large sensing error, and uses the data acquired from other vehicles) (e.g. Paragraphs [0070]-[0072], [0084]).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong and Zhang, with the feature of detecting and discarding sensor fault data in the system of Kim, in order to maintain continuous and safe autonomous driving in the event of a sensor failure (see at least Paragraph [0011] of Kim).
Claim 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ditty, in view of Xiong, as applied to Claims 1 and 7 above, and further in view of U.S. Patent Publication No. 2021/0263166 A1, to Zheng, et al (hereinafter referred to as Zheng; previously of record).
As per Claim 5, Ditty, in view of Xiong, teaches the features of Claim 1, and Ditty further discloses the features of wherein ‘…’ the data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle are obtained from an inertial measurement unit (IMU) chip (e.g. Paragraphs [0124], [0667], [0699]; where an inertial sensor or inertial measurement unit monitors the movement of the vehicle body, including determining velocity, pitch, yaw, roll, acceleration and rotation of the vehicle).
The combination of Ditty, in view of Xiong, fails to teach every feature of wherein the information longitude, latitude and altitude of the current position of the target vehicle are obtained from a global navigation satellite system (GNSS) sensor.
However, Zheng, in a similar field of endeavor, teaches a method for determining a relative vehicle location, where GNSS is used to determine vehicle position, by determining longitude, latitude, and altitude of the vehicle, and the estimate of the location of the vehicle may include latitude and longitude and an altitude component (e.g. Paragraph [0112]; Table 1).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of using GNSS, in order determine accurate relative positions between two receiving antennas (see at least Paragraph [0029] of Zheng).
As per Claim 10, Ditty, in view of Xiong, teaches the features of Claim 7, and Ditty further discloses the features of wherein the data collection module comprises ‘…’ an IMU module, ‘…’ the IMU module is configured to obtain the data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the target vehicle (e.g. Paragraphs [0124], [0667], [0699]; where an inertial sensor or inertial measurement unit monitors the movement of the vehicle body, including determining velocity, pitch, yaw, roll, acceleration and rotation of the vehicle).
The combination of Ditty, in view of Xiong, fails to teach every feature of wherein the data collection module comprises a GNSS receiving module and ‘…’ the GNSS receiving module is configured to obtain the information of longitude, latitude and altitude of the current position of the target vehicle.
However, Zheng, in a similar field of endeavor, teaches a method for determining a relative vehicle location, where GNSS is used to determine vehicle position, by determining longitude, latitude, and altitude of the vehicle, and the estimate of the location of the vehicle may include latitude and longitude and an altitude component (e.g. Paragraph [0112]; Table 1).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to further modify the system for improving autonomous driving of Ditty, in view of Xiong, with the feature of using GNSS, in order determine accurate relative positions between two receiving antennas (see at least Paragraph [0029] of Zheng).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/MERRITT LEVY/Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
