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 .
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.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to non-statutory subject matter. The claim does fall within at least one of the four categories of patent eligible subject matter because the claim describes a computing device-readable recording medium.
Further, Applicant's specification, at paragraphs 30, 73 and 76, fails to explicitly define the scope of a computing device-readable recording medium. Thus, in giving the term its plain meaning (see MPEP 2111.01), the claimed a computing device-readable recording medium is considered to include data signals per se. Data signals per se are not statutory as they fail to fall into one of the four statutory categories of invention.
As an additional note, a non-transitory computer readable medium having executable programming instructions stored thereon is considered statutory as non-transitory computer readable media excludes transitory data signals.
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.
Claims 1, 12 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Demir et al (US 20210010814 A1).
Regarding claim 1, Demir discloses a method of localizing an autonomous driving vehicle using a low-capacity normal distribution transform (NDT) map, which is performed by a computing device (Demir [0011], “a method for robust localization … a vehicle”; [0012], “the map tile point cloud data based on a normal distribution transform (NDT)”; [0043], “the map tile point cloud data based on a normal distribution transform (NDT), which may be associated with a memory-efficient representation (low-capacity)”), the method comprising:
collecting a 3D point cloud for a predetermined region (Demir [0039], “a sensor scan point cloud output by transforming the set of sensor data from each sensor”; [0043], “sensor scan point cloud output (e.g., sensor point cloud)”; [0047], “sensor points corresponding to dynamic objects (e.g., obstacles) may be obtained by first projecting 3D points”); and
performing localization on an autonomous driving vehicle using a previously generated low-capacity NDT map corresponding to the predetermined region and the collected 3D point cloud (Demir [0036], “Exemplary vehicle systems include an autonomous driving system”; [0041], “The scan matcher 104 may receive map tile point cloud data from a map tile server 112 in addition to the sensor scan point cloud … The map tile point cloud data may be indicative of transformed point cloud data associated with a coarse vehicle location and be built using a reference set of sensors 110.”; [0043], “the scan matcher 104 may determine whether a match between the map tile point cloud data from a map tile server 112 (a previously generated low-capacity NDT map, on a server, corresponding to a predetermined region) and the sensor scan point cloud output exists. The scan matcher 104 may determine the match between the sensor scan point cloud output and the map tile point cloud data based on a normal distribution transform (NDT), which may be associated with a memory-efficient representation”).
Demir does not expressly disclose a low-capacity normal distribution transform (NDT) map.
However, Demir suggests a low-capacity normal distribution transform (NDT) map (Demir [0043], “the map tile point cloud data based on a normal distribution transform (NDT), which may be associated with a memory-efficient (interpreted as reading on a low-capacity NDT) representation of the scan surface”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize a memory-efficient NDT map. This would have been done to perform mapping related operations in an efficient manner. See, for example, Demir [0043], “The scan matcher 104 may provide a reliable registration of the point cloud data, be memory efficient”.
Regarding claim 12, Demir discloses the method of claim 1, wherein the performing of the localization on the autonomous driving vehicle includes calculating a localization value for the autonomous driving vehicle by matching the 3D point cloud collected in real time from the autonomous driving vehicle located within the predetermined region with the previously generated low-capacity NDT map (Demir [0036], “vehicle systems include an autonomous driving system”; [0042], “map tiles 114 may be ‘tiles’ around the vehicle which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment).”).
Claim 16 recites a computing device which corresponds to the function performed by the method of claim 1. As such, the mapping and rejection of claim 1 above is considered applicable to the computing device of claim 16.
Additionally, Demir discloses a computing device [0070], “computing devices include personal computers, server computers, hand-held or laptop devices, mobile devices”
Claim 17 recites a computing device-readable recording medium which corresponds to the function performed by the method of claim 1. As such, the mapping and rejection of claim 1 above is considered applicable to the computing device-readable recording medium of claim 17.
Additionally, Demir discloses a computing device-readable recording medium (Demir [0067], “a computer-readable medium including processor-executable instructions configured to implement one aspect of the techniques presented herein”).
Allowable Subject Matter
Claims 2-11 and 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, Zhu et al (US 20230291886 A1), discloses generating a plurality of grids by gridding the 3D point cloud (Zhu [0025], “M×N array of data for each of K modulation frequencies, resulting in M×N grids of intraframe depth data 302a-c, wherein each pixel 304 in each grid represents a measurement acquired at a corresponding illumination light modulation frequency k of K modulation frequencies.”)
modeling points one-to-one corresponding to the plurality of generated grids as a normal distribution and generating a 2D NDT map as a low-capacity NDT map (Zhu [0041], “IS-NDT matching may convert the 6-degrees of freedom (DOF) scan-matching problem into a 2-DOF translation problem. IS-NDT is a grid-based approach and may help provide efficient computation when applied to 2D data”).
However, none of the prior art of record, alone or in combination, disclose claim 2 as recited as a whole.
Claims 3-7 are allowable for depending from claim 2.
Regarding claim 8, Zhu discloses a plurality of 2D NDT maps for the predetermined region (Zhu [0045], “providing feature maps to the matching algorithm may provide improved performance and/or computational efficiency compared to omitting feature maps.”).
However, none of the prior art of record, alone or in combination, disclose claim 8 as recited as a whole.
Claims 9-11 are allowable for depending from claim 8.
Regarding claim 13, Demir discloses classifying a plurality of points included in the 3D point cloud collected in real time from the autonomous driving vehicle (Demir [0036], “vehicle systems include an autonomous driving system”; [0042], “map tiles 114 may be ‘tiles’ around the vehicle which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment).”).
However, none of the prior art of record, alone or in combination, disclose claim 13 as recited as a whole.
Regarding claim 14, none of the prior art of record, alone or in combination, disclose the claim as recited as a whole.
Regarding claim 15, none of the prior art of record, alone or in combination, disclose the claim as recited as a whole.
Conclusion
See the notice of references cited (PTO-892) for prior art made of record, including art that is not relied upon but considered pertinent to applicant's disclosure.
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/JITESH PATEL/Primary Examiner, Art Unit 2612
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