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 .
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method and system for estimating navigation time.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019).).
Claim Rejections - 35 USC § 101
Step 1: Does the Claim Fall within a Statutory Cateqory?
Yes, with respect to claims 1-20, which recite a method, system or medium that include at least one step. The system is therefore directed to the statutory class of machine or manufacture. Paragraph 0001 states that “he subject matter described herein relates, in general, to estimating gaps and applying adjustments involving vehicle signaling, and, more particularly, to estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals..”
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite an abstract idea. The system and methods shown uses Mathematical concepts to determine many of the limitations, including using mathematical relationships. Formulas or equations and calculations. The claims are directed to a method, system or apparatus for determining to estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [0001].
Step 2A, Prong Two: Is the Abstract Idea Inteqrated into a Practical Application?
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components are recited at a high level of generality and are merely invoked as a tool to implement the steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept?
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claim, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claim does not provide an inventive concept.
As such, the claims are not patent eligible.
Examiners Note: To overcome the 101 the Applicant must include an active step. A step that controls the vehicle in some way as described in the Specification ¶ 0040; 0041; 0042; 0057; 0058; 0059; 0065; or 0068, to name a few sections that actively operate the vehicle or systems within a vehicle.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 3, 6, 7, 10 -14, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Green et al. [US20210171079, now Green] with Sun et al. [US20240322555, now Sun].
Claim 1
Green discloses an estimation system comprising: a memory storing instructions that, when executed by a processor [see at least Green, Fig. 8; ¶ 0031 (“In some embodiments, a map is a diagrammatic representation of a guideway network in terms of nodes (e.g., platforms, beacon-coverage areas or the like) and edges (e.g., tracks, guideways or the like) connecting the nodes. Additionally or alternatively, the map is map stored in memory (FIG. 8 804) as a database. In some embodiments, a digital map (e.g., a map stored in memory (804) and presented on a user interface (FIG. 8 842 by processing circuitry (FIG. 8 802)) includes locations of guideway beacons 108 that have digital identifiers (ID). Additionally or alternatively, each guideway beacon 108 has a unique digital ID that is reported in real-time (e.g., part of a message transmitted by guideway beacon 108). In some embodiments, the map contains an association between the beacon's ID and it location in terms of earth-centered, earth-fixed (ECEF) coordinates and/or edge/offset as well. Additionally or alternatively, a position of the vehicle's reference point is determined in terms of the edge identification (ID) and the offset along the edge both in a positive and negative direction of travel (e.g., where is the vehicle on a guideway, what is its orientation and direction of motion and the like).”)], cause the processor to:
compute a positioning speed-signal and an odometry speed-signal temporally by a vehicle from positioning data and odometry data, the positioning data and the odometry data generated at different frequencies [see at least Green, Abstract; ¶ 0002 (“Position and speed determination of a rail vehicle can be performed by a system that includes a checked-redundant vehicle onboard controller (VOBC) connected to a set of sensors. The sensors can consist of a radio frequency identification (RFID) tag reader, a tachometer/speed sensor, cameras, LIDΔR, UWB technology, radar (radio detection and ranging) and accelerometer with RFID tags installed along a guideway. The speed and positioning functions are typically part of the VOBC.”); 0023 (“In some embodiments, a positioning and odometry system (PAOS) determines vehicle position and speed using a beacon and map system. Additionally or alternatively, the PAOS also determines a vehicle stationary state and vehicle cold motion detection (e.g., detection of vehicle motion occurring while processing circuitry is powered off) in beacon coverage areas.”)];
Green does not specifically disclose but Sun teaches calculate a cost matrix for the positioning speed-signal and the odometry speed-signal using dynamic time warping (DTW) [see at least Sun, Abstract; ¶ 0092 (“he dynamic time warping algorithm is used to compute the similarity of two time series X and Y, if the heads and tails of two sequences are positionally matched, and no cross-match and no left out among two sequences. Assume the sizes of X and Y are N and M. An element of D.sub.ij corresponding to row i and column j of D represents the cost of between two arrays with length i and j which equals the distance between the tails, X.sub.i and Y.sub.j plus the minimum of cost in arrays with length (i−1) and j, i and (j−1), and (i−1) and (j−1):
Dij=cost(Xi,Yj)+min{D(i-1)j,Di(j-1),D(i-1)(j-1)},(3)
where cost(X.sub.i,Y.sub.j) is a distance function between two points, X.sub.i,Y.sub.j, such as Euclidean distance. The dynamic time warping based distance between X and Y is the last element of the cost matrix D, D.sub.NM.”)]; and
extract a time gap using the cost matrix and align the positioning speed-signal and the odometry speed-signal by correcting a lag with the time gap [see at least Sun, Abstract; ¶ 0011 (“Some embodiments of the present invention provide a hybrid method to determine the fault location after the presence of fault is detected. In the method, correlations between time-series current components between two consecutive IEDs is first utilized to locate the suspicious fault locations, and then the final fault location is confirmed by identifying the branch containing fault using time-series K-nearest neighbors (KNN) model with neighbor distance measured by dynamic time warping (DTW). After properly tuned hyper-parameters of correlation threshold and number of nearest neighbors, near perfect accuracy can be achieved for localization of weak signal faults, since the disclosed hybrid model emphasizes on the reduction of false positive cases.”); 0092 (“To further narrow down the suspicious locations, a supervised K-nearest neighbors (KNN)-based method is developed incorporating power consumption profile. The k-nearest neighbors (KNN) model is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. For classification problems, a class label is assigned on the basis of a majority vote—i.e. the label that is most frequently represented around a given data point is used. The metric to determine KNNs model is critical for an accurate classification. Euclidean distance is generally used as a metric for finding the nearest neighbors among the input dataset, in which the one-to-one distance between two points is calculated at the same time from two different datasets. For this reason, Euclidean distance does not provide accurate distance information among two time-series datasets if the datasets are not perfectly aligned to each-other in time domain. To compute the nearest neighbors for time-series input datasets in KNNs model, dynamic time warping (DTW) can be used for a more accurate distance measurement between two time-series dataset with different time-alignment. Thus, DTW is employed as a metric of the KNN model considering that power consumption profile through the branches of the microgrid is a time-series data.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun. Providing a more effective [Green, 0049; Sun ¶ 0098]; efficient, and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 2
Green and Sun disclose/teach the system of Claim 1.
Green does not specifically disclose but Sun teaches the instructions to calculate the cost matrix further include instructions to: estimate costs for changing values from the positioning speed-signal to the odometry speed- signal [see at least Sun, ¶ 0093].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun. Providing a more effective [Green, 0049; Sun ¶ 0098]; efficient, and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 3
Green and Sun disclose/teach the system of Claim 1.
Green further discloses the instructions to extract the time gap further include instructions to: derive the time gap from plotted areas of the positioning speed-signal and the odometry speed-signal, and the plotted areas are one of a horizontal flat-line and a vertical flat-line [see at least Green, ¶ 0023 – 0024 (“[0023] In some embodiments, a positioning and odometry system (PAOS) determines vehicle position and speed using a beacon and map system. Additionally or alternatively, the PAOS also determines a vehicle stationary state and vehicle cold motion detection (e.g., detection of vehicle motion occurring while processing circuitry is powered off) in beacon coverage areas. [0024] In some embodiments, the PAOS includes a beacon range measurement system with vehicle beacons installed on-board the vehicle measuring the range to guideway beacons installed trackside to determine a position of the vehicle. Additionally or alternatively, frequency modulated continuous wave (FMCW) radar, from the vehicle beacons, determines the Doppler speed (e.g., radial relative speed) together with a range and angular position (azimuth) to the guideway beacons within the vehicle beacon's field of view (FOV). In some embodiments, a six degree of freedom (DOF) inertial measurement unit (IMU) measures three dimensional (3-D) acceleration and angular speed with respect to a local coordinate system. In some embodiments, positioning and odometry algorithms maintain a high safety integrity stationary state determination and cold motion detection in beacon coverage areas. Additionally or alternatively, positioning and odometry algorithms provide safety integrity level (SIL) 4 positioning and odometry functions, stationary state determination and cold motion detection on a guideway in beacon coverage areas. In some embodiments, the SIL-4 is based on international electrotechnical commission's (IEC) standard IEC 61508, or CENELEC 50126 and 50129, herein incorporated by reference in their entirety. Additionally or alternatively, SIL-4 refers to a probability of system failure per hour ranging from 10.sup.−8 to 10.sup.−9.”); 0030 (“)].
Claim 6
Green and Sun disclose/teach the system of Claim 1.
Green does not disclose but Sun teaches the instructions to compute the positioning speed- signal and the odometry speed-signal temporally further include instructions to: interpolate the positioning speed-signal that includes a first zero-centering operation; and sample the odometry speed-signal that includes a normalization operation and a second zero- centering operation [see at least Sun, ¶ 0059 (“(normal status)”); 0071 (“VMD is an adaptive and quasi-orthogonal decomposition algorithm, aiming at separating the noisy signal f into κ discrete frequency modes, which are compact around (estimated) center frequency of each mode ω.sub.k. VMD is first to obtain unilateral frequency spectrum by applying Hilbert transform to each mode (estimated) u.sub.k, then shift the frequency spectrum of each mode to baseband by mixing an exponential component centered around estimated mode frequency, and the final output is a set of k intrinsic mode function (IMF) components, and each of which represents a harmonic component u.sub.k at frequency ω.sub.k. An IMF represents a generally simple oscillatory mode as a counterpart to the simple harmonic function. By definition, the IMF is any function with the same number of extrema and zero crossings, whose envelopes are symmetric with respect to zero.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun. Providing a more effective [Green, 0049; Sun ¶ 0098]; efficient, and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 7
Green and Sun disclose/teach the system of Claim 1.
Green further discloses assemble information snippets for a trip by the vehicle using the positioning speed-signal and the odometry speed-signal; and generate a map from temporally stitching the information snippets together [see at least Green, ¶ 0110 (“In some embodiments, a system of one or more computers are configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs are configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. In some embodiments, a positioning and odometry system includes two or more vehicle beacons installed on an end of a vehicle and configured to communicate with one or more guideway beacons, the one or more guideway beacons installed along a guideway. The positioning and odometry system also includes processing circuitry configured to communicate with the one or more vehicle beacons, the processing circuitry configured to perform at least one of: determine, before the processing circuitry enters a sleep state, a first vehicle position on the guideway using range measurements between the vehicle beacons and the guideway beacons; determine, after the processing circuitry wakes from the sleep state, a second vehicle position on the guideway using range measurements between the vehicle beacons and the guideway beacons; determine, after the processing circuitry wakes from the sleep state, any difference between the first vehicle position on the guideway and the second vehicle position on the guideway; determine a third vehicle position on the guideway using range measurements between the vehicle and the guideway beacons taken at configurable time intervals; and determine a vehicle speed using range measurements between a single vehicle beacon and a single guideway beacon where speed is measured as a change in the third vehicle position over time. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.”)]..
Claim 10
Claim 10 has similar limitations to claim 1, therefore claim 10 is rejected with the same rationale as claim 1.
Claim 11
Claim 11 has similar limitations to claim 2, therefore claim 11 is rejected with the same rationale as claim 2.
Claim 12
Claim 12 has similar limitations to claim 1, therefore claim 12 is rejected with the same rationale as claim 1.
Claim 13
Claim 13 has similar limitations to claim 2, therefore claim 13 is rejected with the same rationale as claim 2.
Claim 14
Claim 14 has similar limitations to claim 3, therefore claim 14 is rejected with the same rationale as claim 3.
Claim 17
Claim 17 has similar limitations to claim 6, therefore claim 17 is rejected with the same rationale as claim6.
Claim 18
Claim 18 has similar limitations to claim 7, therefore claim 18 is rejected with the same rationale as claim 7.
Claims 8 -9 and 19 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Green et al. [US20210171079, now Green] with Sun et al. [US20240322555, now Sun], further with Ditty et al. [US20190258251, now Ditty].
Claim 8
Green and Sun disclose/teach the system of Claim 1.
Green discloses acquire, by hardware on the vehicle, the positioning data and the odometry data using clock rates that are different, wherein the positioning data is acquired raw from a satellite-based system and the odometry data is raw structure from motion (SfM) data generated with information from a camera associated with the vehicle [see at least Green, ¶ 0002 (“camera”]; 0101 (“FIG. 8 is a high-level functional block diagram of a processor-based system 800, in accordance with some embodiments. In some embodiments, positioning and odometry system processing circuitry 800 is a general purpose computing device including a hardware processor 802 and a non-transitory, computer-readable storage medium 804. Storage medium 804, amongst other things, is encoded with, i.e., stores, computer program instructions 806, i.e., a set of executable instructions such as a positioning and odometry algorithm. Execution of instructions 806 by hardware processor 802 represents (at least in part) a positioning and odometry tool which implements a portion or all of the methods described herein in accordance with one or more embodiments (hereinafter, the noted processes and/or methods).”)]; and
assemble, by the vehicle, the positioning data and the odometry data into blocks when the vehicle is stopped [see at least Green, ¶ 0024 (“In some embodiments, the PAOS includes a beacon range measurement system with vehicle beacons installed on-board the vehicle measuring the range to guideway beacons installed trackside to determine a position of the vehicle. Additionally or alternatively, frequency modulated continuous wave (FMCW) radar, from the vehicle beacons, determines the Doppler speed (e.g., radial relative speed) together with a range and angular position (azimuth) to the guideway beacons within the vehicle beacon's field of view (FOV). In some embodiments, a six degree of freedom (DOF) inertial measurement unit (IMU) measures three dimensional (3-D) acceleration and angular speed with respect to a local coordinate system. In some embodiments, positioning and odometry algorithms maintain a high safety integrity stationary state determination and cold motion detection in beacon coverage areas. Additionally or alternatively, positioning and odometry algorithms provide safety integrity level (SIL) 4 positioning and odometry functions, stationary state determination and cold motion detection on a guideway in beacon coverage areas. In some embodiments, the SIL-4 is based on international electrotechnical commission's (IEC) standard IEC 61508, or CENELEC 50126 and 50129, herein incorporated by reference in their entirety. Additionally or alternatively, SIL-4 refers to a probability of system failure per hour ranging from 10.sup.−8 to 10.sup.−9.”)].
Ditty more specifically teaches acquire, by hardware on the vehicle, the positioning data and the odometry data using clock rates that are different, wherein the positioning data is acquired raw from a satellite-based system and the odometry data is raw structure from motion (SfM) data generated with information from a camera associated with the vehicle [see at least Ditty, ¶ 0012 (“The success of Level 1 and Level 2 ADAS products, coupled with the promise of dramatic increases in traffic safety and convenience, have driven investments in self-driving vehicle technology. Yet despite that immense investment, no vehicle is available today that provides Level 4 or Level 5 functionality and meets industry safety standards, and autonomous driving remains one of the world's most challenging computational problems. Very large amounts of data from cameras, RADAR, LIDAR, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. Ensuring that cars can react correctly in a fraction of a second to constant- and rapidly-changing circumstances requires interpreting the torrent of data rushing at it from a vast range of sensors, such as cameras, RADAR, LIDAR and ultrasonic sensors. First and foremost, this requires a massive amount of computational horsepower. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms.”); 0122 (“Controller (100) sends command signals to operate vehicle brakes (60) via one or more braking actuators (61), operate steering mechanism (58) via a steering actuator (62), and operate propulsion unit (56) which also receives an accelerator/throttle actuation signal (64). Actuation is performed by methods known to persons of ordinary skill in the art, with signals typically sent via the Controller Area Network data interface (“CAN bus”)—a network inside modern cars used to control brakes, acceleration, steering, windshield wipers, etc. The CAN bus can be configured to have dozens of nodes, each with its own unique identifier (CAN ID). In a preferred embodiment, the CAN network can comprise more than a hundred different CAN node IDs. The bus can be read to find steering wheel angle, ground speed, engine RPM, button positions, and other vehicle status indicators. The functional safety level for a CAN bus interface is typically ASIL B. Other protocols may be used for communicating within a vehicle, including FlexRay and Ethernet.”); 0163 (“A variety of different IMU sensors may be used, without limiting the technology. For example, embodiments may include six-axis applications (accelerometers and gyroscopes) and nine-axis applications (accelerometers, gyroscopes, and magnetometers). Alternatively, rather than use magnetometers, the IMU (82) may be implemented as a miniature, high performance GPS-Aided Inertial Navigation System (GPS/INS) that combines MEMS inertial sensors, a high-sensitivity GPS receiver, and advanced Kalman filtering algorithms to provide optimal estimates of position, velocity, and attitude. Example implementations are capable of estimating heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from the GPS to the inertial sensors. Alternatively, IMU (82) and GPS (76) may be combined in a single integrated unit.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun, further with the advanced Algorithms and precise calculations of Ditty . Providing a more effective [Green, 0049; Sun ¶ 0098; Ditty, ¶ 0181]; efficient [Ditty, ¶ 0141], and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003; Ditty, ¶ 0001], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 9
Green and Sun disclose/teach the system of Claim 1.
Green further discloses the positioning speed-signal and the odometry speed- signal are uncorrelated [see at least Green, ¶ 0048].
Ditty also teaches this limitation [see at least Ditty, ¶ 0163].
Note: If some of the data is left out and the results only show one aspect or the other of the data collected, then they are not correlated with each other. Also if can correct ate can do the opposite (uncorrelated).
Claim 19
Claim 19 has similar limitations to claim 8, therefore claim 19 is rejected with the same rationale as claim 8.
Claim 20
Claim 20 has similar limitations to claim 9, therefore claim 20 is rejected with the same rationale as claim 9.
Claims 4, 5, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Green et al. [US20210171079, now Green] with Sun et al. [US20240322555, now Sun], with Ditty et al. [US20190258251, now Ditty], further with Zhang et al. [US20230288209, now Zhang].
Claim 4
Green and Sun disclose/teach the system of Claim 3.
Neither Green or Sun specially disclose/teach but Ditty teaches the instructions to extract the time gap further include instructions to: find an optimal path between plotted values of the positioning speed-signal and the odometry speed-signal by minimizing a total cost within potential warping paths [see at least Ditty, ¶0163; 0638 (“The likelihood consists of how likely all the drives are given the graph. This is like a generative likelihood of picking a path through the graph arbitrarily with some arbitrary lane changes possible between close parallel segments. To score a drive, embodiments implement a procedure that finds the most likely paths through the graph to explain the drive. This procedure may be useful for two things, both to assign a path through an existing lane graph for a drive, and to adjust the continuous parameters of the splines. This procedure proceeds through dynamic programming. For each pose, the embodiments find the spline paths that are reasonably close and in the appropriate direction and measure the likelihood for each. This means that each pose gets a short list of possible edge point assignments. Then the embodiments add the likelihood of certain edge transitions, which results in a dynamic programming problem for the best path through the graph in the sense of a set of edge point assignments for each pose in the drive. The embodiments can then compute the derivatives of the cost of the best path with respect to the node parameters. If the embodiments already have the graph topology and approximate layout, they can thereby fine tune the node parameters of one tile (more precisely one connected component of a tile) using nonlinear refinement.”)]; and
search the optimal path for a flat region parallel with a coordinate axis, wherein the flat region is the lag for aligning the positioning speed-signal and the odometry speed-signal [see at least Ditty, ¶ 0638; 0671 (axis); 0700 (axis)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun, further with the advanced Algorithms and precise calculations of Ditty . Providing a more effective [Green, 0049; Sun ¶ 0098; Ditty, ¶ 0181]; efficient [Ditty, ¶ 0141], and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003; Ditty, ¶ 0001], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Zhang more specifically teaches “coordinate axis” [see at least Zhang, 0086 - 0089 (“The coordinate systems may be defined as follows: [0087] the camera coordinate system {C} may originate at the camera optical center, in which the x-axis points to the left, the y-axis points upward, and the z-axis points forward coinciding with the camera principal axis; [0088] the IMU coordinate system {I} may originate at the IMU measurement center, in which the x-, y-, and z-axes are parallel to {C} and pointing in the same directions; and [0089] the world coordinate system {W} may be the coordinate system coinciding with {C} at the starting pose.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun, with the advanced Algorithms and precise calculations of Ditty, further with the axis coordination capabilities of Zhang . Providing a more effective [Green, 0049; Sun ¶ 0098; Ditty, ¶ 0181]; efficient [Ditty, ¶ 0141; Zhang, ¶ 0318], and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003; Ditty, ¶ 0001; Zhang, ¶ 0260], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 5
Green, Sun, Ditty and Zhang disclose/teach the system of Claim 4.
Green further teaches derive warping paths for the subsequences individually; and combine the warping paths into the optimal path [see at least Green ¶ 0069 (“In some embodiments, the speed is derived from two (2) positions where: (1) the difference in the along guideways distance (e.g., the arc length of centerline 414) between the two (2) positions is greater than 10 times the positioning error; (2) the difference in the along guideways distance (e.g., arc length of centerline 414) between the two (2) positions is less than 100 times the positioning error. Additionally or alternatively, when the arc length of centerline 414 between first position 422 and second position 424 is not less than 10 times the positioning error (e.g., potentially causing the derived speed to be noisy (e.g., affected by the positioning error)) and not greater than 100 times the arc length of centerline 414 between first position 422 and second position 424 to prevent inaccurate measurements the most accurate speed is derived by the odometry algorithm. Additionally or alternatively, the speed error can be expressed as V.sub.err=2P.sub.err/Δt+2ΔPt.sub.err/Δt.sup.2. In some embodiments, in order to avoid noisy speed, a larger Δt is preferred that typically is related with a larger ΔP too. Additionally or alternatively, the speed is calculated as a derivative of the position. In some embodiments, the derivative is noisy; therefore relaxing (e.g., lengthening) the At reduces the derivative noise. In some embodiments, this means that the derivative is not calculated based on consecutive measurements. Additionally or alternatively, a measurement is taken (e.g., P1 at t1) then the next measurement used for ΔP and At should be Pn, to not P2, t2.”); 0079; 0096].
Sun also teaches the general concept of the same limitation [see at least Sun,¶ 0011 (“Some embodiments of the present invention provide a hybrid method to determine the fault location after the presence of fault is detected. In the method, correlations between time-series current components between two consecutive IEDs is first utilized to locate the suspicious fault locations, and then the final fault location is confirmed by identifying the branch containing fault using time-series K-nearest neighbors (KNN) model with neighbor distance measured by dynamic time warping (DTW). After properly tuned hyper-parameters of correlation threshold and number of nearest neighbors, near perfect accuracy can be achieved for localization of weak signal faults, since the disclosed hybrid model emphasizes on the reduction of false positive cases.”); 0036; 0069].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun. Providing a more effective [Green, 0049; Sun ¶ 0098]; efficient, and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Neither Green or Sun (or Ditty) specifically disclose/teach but Zhang does teach truncate the plotted values of the positioning speed-signal and the odometry speed-signal into subsequences that are separate [see at least Zhang, ¶ 134-235 (”[0134] Let {circumflex over (θ)}.sub.m and {circumflex over (t)}.sub.m be the 6-DOF pose corresponding to {circumflex over (T)}.sub.m, and let Σ.sub.m be a relative covariance matrix. The constraints are, Σ.sub.m[({circumflex over (θ)}.sub.m−θ.sub.m).sup.T,({circumflex over (t)}.sub.m−t.sub.m).sup.T].sup.T=0. Eq. 14. [0135] Eq. 14 refers to the case that the prior motion is from the visual-inertial odometry, assuming the camera is functional. Otherwise, the constraints are from the IMU prediction. {circumflex over (θ)}′.sub.m and {circumflex over (t)}′.sub.m(θ.sub.m) may be used to denote the same terms by IMU mechanization. {circumflex over (t)}′.sub.m(θ.sub.m) is a function of θ.sub.m because integration of accelerations is dependent on the orientation (same with {circumflex over (t)}.sub.l.sup.c (θ.sub.l.sup.c) in Eq. 11). The IMU pose constraints are, Σ′.sub.m[({circumflex over (θ)}′.sub.m−θ.sub.m).sup.T,({circumflex over (t)}′.sub.m(θ.sub.m)−t.sub.m).sup.T].sup.T=0, Eq. 15 where Σ′.sub.m is the corresponding relative covariance matrix. In the optimization problem, Eqs. 14 and 15 are linearly combined into one set of constraints. The linear combination is determined by working mode of the visual-inertial odometry. The optimization problem refines θ.sub.m and t.sub.m, which is solved by the Newton gradient-descent method adapted to a robust fitting framework.”); 171-172 (“Each module in the system contributes to the overall accuracy. FIG. 11 depicts estimated trajectories in an accuracy test. A first trajectory plot 1102 of the trajectory of a mobile sensor generated by the visual-inertial odometry system uses the IMU module 122 and the visual-inertial odometry module 126 (see FIG. 2). The configuration used in the first trajectory plot 1102 is similar to that depicted in FIG. 9B. A second trajectory plot 1104 is based on directly forwarding the IMU prediction from the IMU module 122 to the scan matching module, 132 (see FIG. 2) bypassing the visual-inertial odometry. This configuration is similar to that depicted in FIG. 9A. A third trajectory plot 1108 of the complete pipeline is based on the combination of the IMU module 122, the visual inertial odometry module 126, and the scan matching module 132 (see FIG. 2) has the least amount of drift. The position errors of the first two configurations, trajectory plot 1102 and 1104, are about four and two times larger, respectively. [0172] The first trajectory plot 1102 and the second trajectory plot 1104 can be viewed as the expected system performance when encountering individual sensor degradation. If scan matching is degraded (see FIG. 9B), the system reduces to a mode indicated by the first trajectory plot 1102. If vision is degraded, (see FIG. 9A), the system reduces to a mode indicated by the second trajectory plot 1104. If none of the sensors is degraded, (see FIG. 2) the system incorporates all of the optimization functions resulting in the trajectory plot 1108. In another example, the system may take the IMU prediction as the initial guess and but run at the laser frequency (5 Hz). The system produces a fourth trajectory plot 1106. The resulting accuracy is only little better in comparison to the second trajectory plot 1104 which uses the IMU directly coupled with the laser, passing the visual-inertial odometry. The result indicates that functionality of the camera is not sufficiently explored if solving the problem with all constraints stacked together.”); 0177 (“map is truncated”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the positioning and odometry system of Green with the “weak signal fault identification of inverter-based microgrids [0001] of Sun, with the advanced Algorithms and precise calculations of Ditty, further with the axis coordination capabilities of Zhang . Providing a more effective [Green, 0049; Sun ¶ 0098; Ditty, ¶ 0181]; efficient [Ditty, ¶ 0141; Zhang, ¶ 0318], and safer [Green, ¶ 0024 (“safety integrity”); Sun 0003; Ditty, ¶ 0001; Zhang, ¶ 0260], for “estimating a gap between positioning and odometry speed-signals through time warping for aligning the speed-signals [instant Specification, ¶ 0001]”.
Claim 15
Claim 15 has similar limitations to claim 4, therefore claim 15 is rejected with the same rationale as claim 4.
Claim 16
Claim 16 has similar limitations to claim 5, therefore claim 16 is rejected with the same rationale as claim5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hossein Mousazadeh, A technical review on navigation systems of agricultural autonomous off-road vehicles Journal of Terramechanics, Volume 50, Issue 3, June 2013, Pages 211-232.
Xiaotong Shen, Hans Andersen, Wei Kang Leong, Hai Xun Kong, Marcelo H. Ang Jr., Daniela Rus, A General Framework for Multi-vehicle Cooperative Localization Using Pose Graph, arXiv:1704.01252, [Submitted on 5 Apr 2017].
Michael Hoy, Methods for Collision-Free Navigation of Multiple Mobile Robots in Unknown Cluttered Environments, arXiv: 1401.6775, [Submitted on 27 Jan 2014].
*F. Sarholz, J. Mehnert, J. Klappstein, J. Dickmann and B. Radig, "Evaluation of different approaches for road course estimation using imaging radar," 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 2011, pp. 4587-4592.
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(571) 270-7952