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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 is/are directed to the abstract idea of a mathematical concept. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
The claim(s) recite(s) receiving data, and using math on the received data. The rejected dependent claims only supply additional steps (mathematical calculations) or further define variables that a processor must perform. All of these concepts relate to the abstract idea of certain methods of mathematical concepts. The concept described in claims 1-20 is/are not meaningfully different than those methods of mathematical concepts and mental processes found by the courts to be abstract ideas. As such, the description in claims 1-20 is an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim(s) recite(s) the additional limitations of "central computers” to execute code. The hardware is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components that perform the generic functions of [e.g. "transmitting information", "generating information"] common to electronics and computer systems does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves another technology or technical field. Their collective functions merely provide conventional computer implementation (i.e. mere instructions to implement the abstract idea on a generic computing system).
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et. al. (US Patent Publication 2023/0091252) in view of Trogh et. al. ("Map matching and lane detection based on Markovian behavior, GIS, and IMU data.")
Regarding claims 1, and 12, Wang discloses a method and a system for fusing two or more versions of map data together to create fused map data, the system comprising: (abstract)
one or more central computers storing the two or more versions of map data and ground truth map data where each version of the map data represents a predefined geofenced area, wherein the one or more central computers execute instructions to: (¶79)
receive road network data representing a road network for the predefined geofenced area, a first set of map data points, and a second set of map data points, wherein the road network data includes a discrete random curve that represents lane markings, and wherein the discrete random curve includes a plurality of state vectors that are each defined by a respective location and tangent angle; (¶3, 25, 33, 61, 84; “rich road element data information, which can help the vehicle predict complex road information such as slope, curvature” and “The traffic object includes, for example, a road, a lane line, a traffic light, a shopping mall, etc. The location information includes, for example, a coordinate information. The attribute includes, for example, an attribute of a road, an attribute of a lane line” and “The program codes may be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine as a stand-alone software package or entirely on a remote machine or server.”)
determine a signed distance, wherein the signed distance includes
estimate a position
the historical map data 302 may be compared with basic map data 301 to obtain second difference data 303 between the historical map data 302 and the basic map data 301, and then a label information corresponding to the second difference data 303 is determined as the initial change information 304.” and “The label information includes, for example, a location information of the traffic object, an attribute of the traffic object, a change type of the traffic object, etc.” and “Then, the adjusted incremental map data is compared with the historical map data to obtain the first difference data between the incremental map data and the historical map data, and the target change information is determined from the initial change information based on the first difference data.” and “the first comparison sub-module includes: an adjustment unit and a comparison unit. The adjustment unit is used to adjust a data type of the incremental map data based on a data type corresponding to the historical map data, so as to obtain adjusted incremental map data. The comparison unit is used to compare the adjusted incremental map data with the historical map data, so as to obtain the first difference data between the incremental map data and the historical map data.”)
Wang discloses a system for fusing two versions of map data together, by comparing the two versions but does not appear to be explicit as to how the comparison is made and thus appears to be silent as to a first perpendicular distance measured from a respective first map data point to the discrete random curve and a second perpendicular distance measured from a respective second map data point to the discrete random curve; estimate a position for the state vectors of the discrete random curve based on the signed distance and the tangent angle by minimizing a spatial Kalman filter cost function; and execute a Kalman smoothening function to estimate the position and the tangent angle for the state vectors that are part of the discrete random curve, wherein the state vectors each represent a map point of the fused map data.
Trogh however teaches determine a signed distance, wherein the signed distance includes a first perpendicular distance measured from a respective first map data point to the discrete random curve and a second perpendicular distance measured from a respective second map data point to the discrete random curve; estimate a position for the state vectors of the discrete random curve based on the signed distance and the tangent angle by minimizing a spatial Kalman filter cost function; and execute a Kalman smoothening function to estimate the position and the tangent angle for the state vectors that are part of the discrete random curve, wherein the state vectors each represent a map point of the fused map data. (Section II, A. Map Matching; and B. Lane Detection: “Advanced map matching algorithms are often based on the Hidden Markov Model (HMM) [1], [13], [14]. These systems can model the road infrastructure” “A conditional random field (CRF) is a type of undirected graphical model that is used to encode known relationships between observations, e.g., to segment and label sequential data” “A feature-based algorithm uses low-level features, e.g., the solid or dashed painted lines on public roads, and image segmentation [24], deep learning [24], [25], or sensor fusion [26] to detect the lanes. A model-based approach uses a few parameters to represent the lanes, e.g., straight lines or parabolic curves, these parameters can be estimated by a Hough transformation [27], [28] or a likelihood function”)
The Examiner notes that while Trogh appears silents as to specifically a Kalman filter, Trogh uses a Hidden Markov Model (HMM). Both are well known hidden state space models and further because the main difference between the models is that a HMM has discrete hidden state variables, while a kalman filter can be EITHER discrete or variable (see: machine learning: the difference between hidden markov models and particle filter and kalman filter) a person of ordinary skill in the art at the time of filing would have found a kalman filter an obvious substitute for the HMM)
It would have been obvious to one of ordinary skill in the art at the time of filing to provide the invention of Wang with determine a signed distance, wherein the signed distance includes a first perpendicular distance measured from a respective first map data point to the discrete random curve and a second perpendicular distance measured from a respective second map data point to the discrete random curve; estimate a position for the state vectors of the discrete random curve based on the signed distance and the tangent angle by minimizing a spatial Kalman filter cost function; and execute a Kalman smoothening function to estimate the position and the tangent angle for the state vectors that are part of the discrete random curve, wherein the state vectors each represent a map point of the fused map data as taught by Trogh with a reasonable expectation of success because the technique for improving a particular class of devices was part of the ordinary capabilities of a person of ordinary skill in the art, in view of the teaching of the technique for improvement in other situations, would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 2, Trogh teaches wherein the discrete random curve is one of the following: a polyline and a Markovian random curve. (Section II, A. Map Matching; and B. Lane Detection: “Advanced map matching algorithms are often based on the Hidden Markov Model (HMM) [1], [13], [14]. These systems can model the road infrastructure” “A conditional random field (CRF) is a type of undirected graphical model that is used to encode known relationships between observations, e.g., to segment and label sequential data” “A feature-based algorithm uses low-level features, e.g., the solid or dashed painted lines on public roads, and image segmentation [24], deep learning [24], [25], or sensor fusion [26] to detect the lanes. A model-based approach uses a few parameters to represent the lanes, e.g., straight lines or parabolic curves, these parameters can be estimated by a Hough transformation [27], [28] or a likelihood function”)
It would have been obvious to one of ordinary skill in the art at the time of filing to provide the invention of Wang with wherein the discrete random curve is one of the following: a polyline and a Markovian random curve as taught by Trogh with a reasonable expectation of success because the technique for improving a particular class of devices was part of the ordinary capabilities of a person of ordinary skill in the art, in view of the teaching of the technique for improvement in other situations, would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claims 3, and 13, Wang further discloses wherein the one or more central computers execute instructions to: detect a data point representing a new state vector that is introduced to the discrete random curve; and in response to detecting the data point, estimate an updated state vector, where the updated state vector indicates respective values for the position and a tangent angle of a new data point. (¶34)
Regarding claim 11, Wang further discloses wherein the two or more versions of map data are each based on one or more of the following: global positioning system (GPS) data, perception data, a high-speed vehicle telemetry (HSVT) source, satellite image data, and data collected from survey vehicles. (¶23)
Allowable Subject Matter
Claims 4-10, and 14-20 would be allowable if the claims are rewritten or amended to overcome the §101 rejection(s) set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: the Trogh reference was relied upon as best teaching the particular details of the math for map comparisons, but as Trogh is teaching map matching (matching a vehicle to a map path) rather than for finding and correcting map corrections the prior art fails to disclose or render obvious the indicated claims absent impermissible hindsight reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN D HUTCHINSON whose telephone number is (571)272-8413. The examiner can normally be reached 7-5 Mon-Thur.
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/ALAN D HUTCHINSON/Primary Examiner, Art Unit 3669