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 .
Examiner’s Note
To help the reader, examiner notes in this detailed action claim language is in bold, strikethrough limitations are not explicitly taught and language added to explain a reference mapping are isolated from quotations via square brackets.
Election/Restrictions
Applicant’s election without traverse of claims 1-19 in the reply filed on 06/11/2025 is acknowledged.
Response to Arguments
Applicant's arguments filed 11/14/2025 have been fully considered but they are not persuasive. An explanation is provided below.
Applicant alleges on p.1:
The examiner has made no attempt to explain why the ordinary artisan, having discovered that a robot, and in particular, a robot with many arms, is useful for improving "diagnosis," would have concluded that there exists some equivalence between a "robot" and illumination by "ground-penetrating radar" as claimed. Hence, the rationale offered is "conclusory" within the meaning of KSR.
The Examiner respectfully disagrees.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Lavy and Liu deal with radar data, training data sets, and using neural networks. Both Lavy and Liu discuss known techniques applied to similar systems, are analogous art and may be used by one of ordinary skill in the art to make a global map of subsurface data to improve inspection efficiency (Liu 0091).
Applicant alleges on p.7:
D1's 1|40 discloses sending "radar" data. However, the claim requires sending "training" and "target" data. Thus, one must infer that the examiner considers it impossible for "radar data" to not include both "training data" and "target data." The examiner has not established that "radar data" necessarily comprises both "training data" and "target data." Accordingly, should the rejection be maintained on this basis, it is requested that the examiner provide evidence tending to show that one cannot have "radar data" that does not include both "training data" and "target data."
The Examiner respectfully disagrees. Applicant alleges the claim ‘requires sending "training" and "target" data’, however no where in the claim is it required to ‘send’ training and target data. At best, the claim claims ‘storing training and target data in a radar database’. Lavy teaches “[0038] Image server 200 processes the captured images that it receives, either directly from camera 110 or from smartphone 120, and stores the processed images on a storage device 300.”; “[0048] training server 400 generates maps using bounding box or other methods for detecting objects in an image”; “[0040] sending lidar or radar data to image server”. As such, the map tiles are subdivisions of an overall map image data. As such, Applicant’s arguments are unpersuasive.
Applicant alleges on p.8:
Applicant recognizes that all claim terms must be given their broadest reasonable interpretation. However, it is not altogether clear why structures that are on the surface would have broadly and reasonably be construed as being "subsurface." Should the rejection be maintained, it is requested that the broadest reasonable interpretation of "subsurface structures" be made of record sot that it can be understood how such features as "lanes" and "road crossings" could be considered subsurface."
The Examiner respectfully disagrees. Applicant requests the BRI of ‘subsurface structures’ and alleges the cited prior art does not teach subsurface structures but rather misconstrues surface structures as substructures. Applicant furthermore requests the Examiner to explain the BRI cited claimed limitations throughout the remarks. Examiner notes the BRI of claimed limitations are taken within the context of the claims and in view of the specification. Liu teaches in the Abstract “Overall automatic inspection can be implemented both on the surface and inside of the tunnel lining.”. As such, subsurface structures are taught and Applicant’s arguments are unpersuasive.
Applicant alleges on p.9:
It is believed that "cropping" does not amount to "dividing. into subdivisions." In fact, "cropping" would be the equivalent to "truncating." For example, when one "crops" an image, one removes a portion of the image and discards it. Therefore, the act of "cropping" the map tile is not "dividing" for the same reason that truncating a number is not equivalent to dividing that that number. Should the rejection be maintained, it is requested that the examiner articulate on the record the broadest reasonable interpretation of "dividing" together with evidence relied upon to arrive at that broadest reasonable interpretation . . . According to 86, "cropping" is an operation with an "input" and an "output." The "input" is the map tile. The "output" is one 256×256-pixel square, i.e., the alleged "subdivision." Thus, D1's operation of "cropping" does not yield plural "subdivisions" as claimed.
The Examiner respectfully disagrees. The claim requires dividing training data into subdivisions. Merriam-Webster defines ‘subdivide’ as “to divide the parts of into more parts.” Lavy teaches “[0079] a first map tile from a road map image . . . Image server 200 includes an image concatenator 220 that concatenates captured image # N and captured image #(N+1) to generate a first concatenated image, and also concatenates the map tiles of the road image and the aerial image to generate a second concatenated image”. As such, the map tiles are subdivisions of an overall map image data. As such, Applicant’s arguments are unpersuasive.
Regarding claims 2-20, the rejections are maintained in view of the Response to Arguments above and rejection below.
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 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.
Claim(s) 1-3, 6-10, 16-19, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu).
Regarding claim 1, Lavy teaches A method comprising storing training data and target data (Abstract “A system for training simultaneous localization and mapping (SLAM) models”) in a radar database (0040 “In accordance with embodiments of the present invention, the system of FIG. 1 includes lidar or radar transmitters embedded in vehicle 100, sending lidar or radar data to image server 200, wherein the lidar or radar data are treated as visual data and are uploaded to image server 200.”),
,
dividing said training data into subdivisions (0086 “Referring back to FIG. 5, map tiles 270 and 280 are cropped to be of size 256×256 pixels, and concatenated with a mask.”),
using an annotation interface (0067 “In accordance with embodiments of the present invention, the method of FIG. 2 also includes generating a full geographic area of compressed vector/pixel level segmentation of the map tile, including annotating the map tile”),
receiving annotations that associate features in said subdivisions with types of subsurface structures (0067 “annotating the map tile using segmentation and vector annotation of lanes, road borders, road features and road crossings, and training the SLAM model on the annotated data.”),
using said annotated subdivisions and said training data to develop a trained classifier (0188 “Goal: fine-grain classification for real world localization.”),
dividing said target data into local maps (0189 “Solution: map every corner of the street in a database, and retrieve the corners from the database from a photo of the street localized in a range of 10 m using image similarity.”),
using said trained classifier to associate features in said local maps with types of subsurface structures (0048 “et further, objects changing over time include appearance; e.g., clarification of a road sign or a road marking. Moreover, each object type presence is a class, and is only tracked when it starts to be detected and when it stops, to generate a naïve simple map. Additionally, the map is updated and change detection is propagated to vehicle-to-vehicle (V2V) networks and municipalities, to allow vehicles and municipalities to react to the change; e.g., a traffic light that was previously detected is now missing.”),
aligning said local maps to form a global map showing said subsurface structures, and providing said global map to an end user (0036 “Embodiments of the subject invention provide methods that combine GPS, driving view and top view to yield high accuracy, map aligned global positioning in real time.”; 0048 “locations of the objects relative to vehicle 100 are determined, and positioned globally using self-localization of vehicle 100”; fig 12).
Lavy does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Liu teaches
said training data and said target data both resulting from illumination by ground-penetrating radar (0159 “Step S1: Establish a simulated training data set, where the simulated training data set includes a plurality of ground penetrating radar sectional view-dielectric constant distribution diagram data pairs.”)
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 2, the cited prior art teach The method of claim 1, wherein said local maps comprise first and second local maps, wherein said method further comprises identifying a subsurface structure (Liu 0075 “The linear array camera array 12 is used for surface defect inspection. The near-infrared binocular stereo imaging camera 11 is configured to position and frame a tunnel environment with uneven illumination”) that comprises a first segment in said first local map and a second segment in said second local map, and wherein aligning said subdivisions comprises establishing continuity between said first and second segments (Lavy 0004 “In some embodiments, the present invention provides “snap-to-satellite (Snap2Sat)” capability, i.e., mapping a motor vehicle route to a corresponding portion of a satellite image.”; 0067 “Additionally, the compressed vector/pixel level segmentation of the map tile is continually updated, from up-to-date dashboard camera 110 road images.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 3, the cited prior art teach The method of claim 2, wherein establishing said continuity comprises aligning said first local map with said second local map such that said first segment and said second segment connect to each other (Lavy fig 4 “concatenate satellite and roadmap image portions”).
Regarding claim 6, the cited prior art teach The method of claim 2, wherein establishing said continuity comprises aligning said first local map with said second local map based on coordinates of said first and second segments and directions in which said first segment and said second segment extend (Lavy Abstract “labels the uploaded images with a GPS location and a timestamp”; 0013 “FIG. 6 is a simplified drawing showing a vehicle local moving coordinate system, including course direction and sideway direction”).
Regarding claim 7, the cited prior art teach The method of claim 1, further comprising generating a navigation plan that comprises navigation paths that are to be traversed while carrying out said illumination by ground-penetrating radar (Liu 0012 “Further, the wideband air-coupled ground penetrating radar”), wherein said navigation paths comprise first and second navigation paths that are along a first street (Lavy fig 12).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 8, the cited prior art teach The method of claim 1, further comprising generating a navigation plan that comprises navigation paths that are to be traversed while carrying out said illumination by ground-penetrating radar (Liu 0079 “The wideband air-coupled ground penetrating radar 15 is disposed on the sliding gimbal 25.”), wherein said navigation paths comprise first and second navigation paths that correspond to first and second lanes of a first street (Lavy 0295 “Navigation with lane level awareness may allow notification of passing to correct lane or detection of potential highway exit miss before it actually happened.”).
Regarding claim 9, the cited prior art teach The method of claim 1, wherein said annotations associate features in said training data with types of man-made infrastructure (Lavy 0052 “training server 400 performs localization fix prior to aggregation. Yet further, classes include lanes, lane separation types, sidewalks, parking lots, buildings, crossings, tree centers, road markings, road edges, and other features that assist localization and visibility from above, such as bridges and highway signs”; 0037 “Vehicle 100 also includes an annotation tool 140 for annotating road images, as described hereinbelow.”).
Regarding claim 10, the cited prior art teach The method of claim 1, wherein said annotations associate features in said training data with types of natural features (Lavy 0052 “training server 400 performs localization fix prior to aggregation. Yet further, classes include lanes, lane separation types, sidewalks, parking lots, buildings, crossings, tree centers, road markings, road edges, and other features that assist localization and visibility from above, such as bridges and highway signs”; 0037 “Vehicle 100 also includes an annotation tool 140 for annotating road images, as described hereinbelow.”).
Regarding claim 16, the cited prior art teach The method of claim 1, wherein aligning said local maps comprises implementing a machine-learning process that includes creating a synthetic labeled training dataset, training the machine learning model using the synthetic labeled training set thus created (Liu 0039 “obtaining a ground penetrating radar inversion deep learning network model according to the simulated training data set”), and using the trained classifier to process pairs of linear subsurface structures identified in said local maps (Liu 0169 “The plurality of layers of convolution structures are used to enhance ground penetrating radar single-channel data by using neighborhood information. The plurality of layers of perceptron structures are used to compress and restructure each channel of enhanced ground penetrating radar single-channel data, and splice the data in order, to achieve sufficient neighborhood information extraction and correspondence of spatial feature information between data pairs.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 17, the cited prior art teach The method of claim 1, wherein receiving annotations comprises receiving annotations that rely on historical maps (Lavy 0075 “the method of FIG. 2 is used in combination with an historic probability map based on vast GPS raw data obtained on a given geographic area.”) and test pits (Liu 0005 “Existing inspection robots used in subway tunnels and other tunnels with tracks are mostly equipped with line scan”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 18, the cited prior art teach The method of claim 1, wherein said training data and said target data result from illumination of sides of a tunnel and a ceiling of said tunnel (Liu Abstract “A multi-arm robot used for tunnel lining inspection and defect diagnosis . . . Overall automatic inspection can be implemented both on the surface and inside of the tunnel lining.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 19, the cited prior art teach The method of claim 1, wherein said global map identifies locations of subsurface defects (Liu 0051 “The moving robot of the present disclosure can travel autonomously, and in the process of traveling, the moving robot performs lining defect detection based on artificial intelligence.”) and locations of buried infrastructure (Lavy 0042 “Yet further, sensor(s) 130 data is collected and mapped to road conditions.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 21, the cited prior art teaches The method of claim 1, further comprising receiving said global map and using said global map to carry out directional drilling to install, replace, or maintain underground utilities (Liu 0075 “The linear array camera array 12 is used for surface defect inspection. The near-infrared binocular stereo imaging camera 11 is configured to position and frame a tunnel environment with uneven illumination”), using said global map to detect subsurface voids or cracks under or around existing infrastructure (Liu 0003 “a large number of tunnel structures in an operation period have structural concealed defects such as lining cracks”), and using said global map to mark a pavement's surface with markings that show the locations of underground infrastructure (Liu 0146 “S503. Defect recognition and marking is separately performed on the visible light sampling image and the infrared sampling image by using a semantic segmentation neural network.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the multi-arm robot used for tunnel lining inspection of Liu with the teachings of Lavy. One would have been motivated to do so in order to advantageously improve diagnosis (Liu 0006). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Liu merely teaches that it is well-known to incorporate the particular GPR features. Since both Lavy and Liu disclose similar radar technology used in feature maps and training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of El-Sheimy et al. (US 20170227361 hereinafter El-Sheimy).
Regarding claim 4, the cited prior art teach The method of claim 2, wherein establishing said continuity comprises aligning said first local map with said second local map such that a line extending along an axis of said first segment is colinear with a line that extends along an axis of said second segment.
While Lavy discloses that roads are represented by lines, the combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, El-Sheimy teaches wherein establishing said continuity comprises aligning said first local map with said second local map such that a line extending along an axis of said first segment is colinear with a line that extends along an axis of said second segment (0051 “Using two or more images and collinearity equations, the 3D coordinates of interest points in mapping frame can be calculated as shown in FIG. 3.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the mapping system and method of El-Sheimy with the cited prior art. One would have been motivated to do so in order to advantageously improve system accuracy (El-Sheimy 0040). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, El-Sheimy merely teaches that it is well-known to incorporate the particular mapping features. Since both the cited prior art and El-Sheimy disclose similar navigation systems, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of Ondruska et al. (US 20210407101 hereinafter Ondruska).
Regarding claim 5, the cited prior art teach The method of claim 2,
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Ondruska teaches wherein establishing said continuity comprises aligning said first local map with said second local map such that a portion of said first segment overlaps a portion of said second segment (0075 “Following the creation and potential testing of the multiple submaps 110 for the map segments 106, a process of global alignment 114 is performed to align each of the submaps 110 for the overlapping map segments 106 by using the overlapping portions of the submaps 110.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the navigation system and method of Ondruska with the cited prior art. One would have been motivated to do so in order to advantageously improve quality of the maps (Ondruska 0001). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Ondruska merely teaches that it is well-known to incorporate the particular map overlapping features. Since both the cited prior art and Ondruska disclose similar navigation systems, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of Tsuchida et al. (US 20110167027 hereinafter Tsuchida).
Regarding claim 11, the cited prior art teach The method of claim 1,
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Tsuchida teaches wherein dividing said training data into subdivisions comprises selecting sizes of said subdivisions based on feature densities of said subdivisions (0104 “The density estimation model learning unit 91 may divide the training data generated by the training text generation unit 90 into two parts, use one part of the training data to learn a density estimation model, and using the density estimation model, estimate the densities of units of training included in the other part of the training data.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the classification system and method of Tsuchida with the cited prior art. One would have been motivated to do so in order to advantageously improve classification accuracy (Tsuchida 0010). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Tsuchida merely teaches that it is well-known to incorporate the particular training data features. Since both the cited prior art and Tsuchida disclose similar classification systems and use of training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of Kitagawa et al. (US 20200234136 hereinafter Kitagawa).
Regarding claim 12, the cited prior art teach The method of claim 1,
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Kitagawa teaches wherein dividing said training data into subdivisions comprises choosing an area of a first subdivision having a first feature density to minimize a difference between a product of said area of said first subdivision and said first feature density and a product of a feature density of a second subdivision and an area of said second subdivision (0123 “If a not yet learned area [Xa, Yb] is set, the routine proceeds to step 306. At step 306, first, the training data density D in the not yet learned area [Xa, Yb] in which the new input values x1, x2 fall is calculated. This training data density D (=number of training data/[Xa, Yb]) shows the value obtained by dividing the number of training data by the area of the not yet learned area [Xa, Yb], that is, the product of the preset ranges of the values of the types of the operating parameters . . . if the variance S.sup.2 of the training data is smaller than the preset variance S.sup.2.sub.0, the processing cycle is completed.”; 0108 “In this way, in this second embodiment, if the data density obtained by dividing the number of training data”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the machine learning system and method of Kitagawa with the cited prior art. One would have been motivated to do so in order to advantageously improve classification accuracy (Kitagawa 0010). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Kitagawa merely teaches that it is well-known to incorporate the particular training data features. Since both the cited prior art and Kitagawa disclose similar classification systems and use of training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Regarding claim 13, the cited prior art teach The method of claim 1,
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Kitagawa teaches wherein dividing said target data into local maps comprises choosing a ratio of an area of a first local map to a second local map to be as close as possible to a ratio of a feature density of said second local map to said first local map (0114 “in FIG. 17, the preset ranges Rx, Ry of the values of the types of operating parameters are divided into a plurality of sections and a plurality of divided areas [Xn, Ym] (n=1, 2 . . . n, m=1, 2 . . . m) defined by combinations of divided ranges in which the values of the types of operating parameters are divided are preset.”; 0126 “In this regard, when two divided areas [Xn, Ym] are close, the relationships of the change of training data with respect to a change of an input value are similar between these divided areas [Xn, Ym]. Therefore, when the two divided areas [Xn, Ym] are close, it is possible to use the same number as the number of nodes Knm of the hidden layer.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the machine learning system and method of Kitagawa with the cited prior art. One would have been motivated to do so in order to advantageously improve classification accuracy (Kitagawa 0010). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Kitagawa merely teaches that it is well-known to incorporate the particular training data features. Since both the cited prior art and Kitagawa disclose similar classification systems and use of training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of Carney et al. (US PAT 3557674 hereinafter Carney).
Regarding claim 14, the cited prior art teach The method of claim 1,
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Carney teaches wherein said subsurface structures comprise a water main and a sewer pipe dividing said training data into subdivisions results in first and second subdivisions that at least partially overlap. (1:1-7 “This invention relates to a novel photographic device for inspecting the interiors of pipes such as water mains, sewer lines, oil wells, and the like, and, more particularly, to a device of this kind arranged to provide a signal at a remote location indicating its proper operation.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the system of Carney with the cited prior art. One would have been motivated to do so in order to advantageously maintain proper operation (Carney 1:1-20). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Carney merely teaches that it is well-known to incorporate the particular water main/sewer pipes. Since both the cited prior art and Carney disclose similar systems for monitoring of pipes, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavy et al. (US 20200309541 hereinafter Lavy) in view of Liu et al. (US 20210389257 hereinafter Liu) as applied claim 1, and further in view of Felemban et al. (US 20170017846 hereinafter Felemban).
Regarding claim 15, the cited prior art teach The method of claim 1
The combination does not explicitly teach the strikethrough limitations. However, in a related field of endeavor, Felemban teaches wherein dividing said training data into subdivisions results in first and second subdivisions that are separated by a gap that is outside of any subdivision (0048 “Thus, the SVM model represents the examples (i.e., the training data) as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, at the time of filing of the instant application, to include the monitoring system and method of Felemban with the cited prior art. One would have been motivated to do so in order to advantageously improve traffic estimate (Felemban Abstract). Further still, the Supreme Court in KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007) provides that combining prior art elements according to known methods to yield predictable results may render a claimed invention obvious over such combination. Here, Felemban merely teaches that it is well-known to incorporate the particular subdivisions. Since both the cited prior art and Felemban disclose similar monitoring/traffic technology and use of training data, one of ordinary skill in the art would recognize that the combination of elements here has previously been executed according to known methods, thereby evidencing that such combination would yield predictable results.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to application’s disclosure:
Weinzaepfel (US 20200364509) discloses “A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment (See abstract)”
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/ISMAAEEL A. SIDDIQUEE/
Examiner, Art Unit 3648
/William Kelleher/ Supervisory Patent Examiner, Art Unit 3648