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 .
Claims 1-17 are pending in the application.
Claim Objections
Claim 8 2nd line “the at least one feature” has no antecedent basis.
Claim 11 recites “generating the projection signal”. It is not clear which projection signal it refers to since claim 8 recites “a runtime projection signal” and “a training projection signal”. Further it is suggested to replace “the projection data” with “the runtime projection data”.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 and 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (US 7171036 B1, hereafter Liu).
As per claim 1, Liu teaches a method (Abstract; FIG. 2) of generating training results using a vision system (FIG. 3), comprising:
selecting at least one training image comprising at least one representative feature (FIG. 1; FIG. 2 #200; FIG. 4 #450/#480 “side features”);
operating a line-finding tool that searches the at least one training image for the at least one representative feature using at least one caliper (FIG. 4 #430-450);
generating a projection signal from projection data associated with the at least one caliper (col. 5 line 61-col. 6 line 3 “Starting with a first side region, a plurality of caliper vision tools is applied to the first side region … A caliper window, or region of interest, is provided to the tool as a two-dimensional image. The tool then projects the image into a one-dimensional image--a process that can be likened to collapsing the entire window into a single line”);
generating a filter signal from the projection signal (col. 6 line 3-6 “The tool applies an edge filter to the one-dimensional image returning the location, polarity, and contrast (or difference in grey level value on either side of the edge) for each edge feature in the caliper window”); and
generating an index value by finding an edge of the at least one caliper nearest to an expected feature (FIG. 4 #450 and #480; col. 6 line 3-6 “The tool applies an edge filter to the one-dimensional image returning the location, polarity, and contrast (or difference in grey level value on either side of the edge) for each edge feature in the caliper window”; The “location” corresponds to an index; The feature “nearest to an expected feature” is inherent when an edge filter is applied).
As per claim 2, dependent upon claim 1, Liu further teaches:
configuring at least one line-finding parameter of the line-finding tool prior to operating the line-finding tool (col. 5 line 66-67 “A caliper window, or region of interest, is provided to the tool as a two-dimensional image”; The “caliper window” at least teaches projection width).
As per claim 3, dependent upon claim 2, Liu further teaches wherein the at least one line-finding parameter comprises at least one of: search direction; search length; projection width (See rejections applied to claim 2); or polarity.
As per claim 4, dependent upon claim 1, Liu further teaches normalizing the projection data prior to generating the projection signal (col. 6 line 25-29).
As per claim 5, dependent upon claim 1, Liu teaches wherein the line-finding tool comprises at least one caliper (FIG. 4 #430; col. 5 line 61-66).
As per claim 7, dependent upon claim 1, Liu teaches wherein the at least one representative feature comprises at least one line segment (FIG. 5; col. 5 line 53-59).
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.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 7171036 B1, hereafter Liu), as applied above to claim 1, in view of David et al. (WO 9818117 A2, hereafter David).
As per claim 6, dependent upon claim 1, Liu teaches applying an edge filter to the one-dimensional image (col. 6 lines 3-6), but does not specify that the filter signal comprises a first derivative of the projection signal.
David in the same field of endeavor teaches a line detection method using a very similar approach (Abstract; FIG. 4 #112). Specifically, two-dimensional image of a target is projected into a one-dimensional image, and first derivatives of the projections are calculated to identify edges (page 12 line 25-28 “The projection tool, which maps the two-dimensional image of the target into a one-dimensional image, is applied along the axes defined by the edges in the image. As those skilled in the art will appreciate, the location of the edges can be discerned from by finding the peaks in the first derivatives of each of those projections”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Liu to incorporate the teaching of David to apply a first derivative of the projection signal to generate a filter signal. Doing so would allow detection of edges by identifying most rapid changes in the first derivative values (David page 46 top 2 lines; FIG. 106).
Allowable Subject Matter
Claims 8-17 are allowed.
The following is Examiner’s reasons for identification of allowable subject matter.
As per claim 8, the closest prior art includes Wang et al. (US 20210233250 A1, hereafter Wang), Liu et al. (US 7171036 B1, hereafter Liu), and David et al. (WO 9818117 A2, hereafter David).
Liu teaches generating training projection signal, training filter signal and training parameters as analyzed above in rejection to claim 1. David teaches generating training projection signal, training filter signal and training parameters in a similar manner (FIG. 3-4; page 12 line 23-31). Wang discloses a similar method of generating training results using a similar vision system (FIG. 1-3; para. [0034]-[0035]). Wang further teaches that when lines are identified, the user can train the system to associate predetermined (e.g. text) labels with respect to such lines. These labels can be used to define neural net classifiers. The neural net operates at runtime to identify and score lines in a runtime image that are found using a line-finding process. The found lines can be displayed to the user with labels and an associated probability score map based upon the neural net results (Abstract; FIG. 19-21; para. [0061]-[0068]). Prior art, either applied alone or in combination with, fails to teach or suggest “determining a best path by warping and mapping a training projection signal relative to a runtime projection signal and a training filter signal relative to a runtime filter signal; generating a runtime projection index and a runtime filter index using the determined best path and at least one training parameter; determining a confidence index based upon the runtime projection index and the runtime filter index”, in combination with other features in the claim.
Claim 17 recites similar limitations as claim 8.
Conclusion
Additional prior art Hsu et al. (US 10152780 B2) discloses a method for finding multiple line features in an image. Two related steps are used to identify line features. First, the process computes x and y-components of the gradient field at each image location, projects the gradient field over a plurality subregions, and detects a plurality of gradient extrema, yielding a plurality of edge points with position and gradient. Next, the process iteratively chooses two edge points, fits a model line to them, and if edge point gradients are consistent with the model, computes the full set of inlier points whose position and gradient are consistent with that model. The candidate line with greatest inlier count is retained and the set of remaining outlier points is derived. See Abstract, FIG. 2-3, col. 4 line 38 -col. 5 line 40).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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