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 Objections
Claim 3 is objected to because of the following informalities: the claim ends in a semi-colon and should end with a period. Appropriate correction is required.
Examiner’s Note: The Examiner would like to point out that a plurality of calls were made to Applicant’s representative to try and put the case in condition for allowance, however, the number provided (626-685-4991) rings twice and then drops saying the person on the other line is busy and to call another time.
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.
Claim(s) 1-3, 5, 6, 8, 11-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mingqiang et al., “Shape Matching and Object Recognition Using Chord Contexts” (Mingqiang).
Regarding claim 1, Mingqiang teaches a system (retrieval system) (p. 63; Section 1., 1st paragraph) for performing object recognition (shape matching and object recognition using chord contexts) (p. 63; Title) based on an acquired image and a reference image (determining the correspondence between shapes) (p. 65; Section 2.2., 1st paragraph), the system (retrieval system) (p. 63; Section 1., 1st paragraph) comprising:
a) detect a plurality of edge pixels in the acquired image (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7); and
b) detect a plurality of edge pixels in the reference image (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7);
a plurality of transforms, each transform associated with an input chord length and an output chord length (wherein the each matrix for the query and target images is based on the set of chords in every direction) (pages 63-64; Section 2.1.), wherein each transform is characterized by a scale based on a ratio of the associated input and output chord lengths (in order to reduce the size of the matrix M and, at the same time, make the feature extracted invariant with scale transforms, we normalize this matrix M) (pages 63-64; Section 2.1.);
to generate:
a) a first plurality of chords, each chord characterized by a length between two of the plurality of edge pixels in the acquired image (wherein each chord is a length between two edge pixels in the query image) (pages 63-64; Section 2.1., Fig. 1); wherein each of the first plurality of chords is coupled to at least one of the plurality of transforms based on the associated input chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5); and
b) a second plurality of chords, each chord characterized by a length between two of the plurality of edge pixels in the reference image (wherein each chord is a length between two edge pixels in the matching image) (pages 63-64; Section 2.1., Fig. 1);
wherein each of the second plurality of chords is coupled to at least one of the plurality of transforms based on the associated output chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5); and
a) for each of a plurality of scales (scale and rotation transforms) (p. 67; Section 3.1), determine a number of chords from the second plurality of chords that have a counterpart chord among the first plurality of chords (finding the similarity between the query image and the matches) (pages 65-66; Section 2.2., Fig. 7, and p. 67; Section 3.1); and
b) output a recognition signal if and when the number exceeds a predetermined threshold for one of the plurality of scales (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.).
Although Mingqiang does not explicitly teach “an edge detector”, “a chord generator”, or “a summing circuit”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that these are all processing modules that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 2, Mingqiang teaches further comprising a scale selector module configured to :a) sweep through the plurality of scales (wherein a plurality of scales can be selected) (p. 67; Section 3.1); and b) while sweeping through the plurality of scales, activate one or more of the plurality of transforms based on the associated scale (wherein each scale is transformed based on normalization) (p. 67; Section 3.1).
Regarding claim 3, Mingqiang teaches connecting each of the first plurality of chords with at least one of the plurality of transforms based on the associated input chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5); and connecting each of the second plurality of chords with at least one of the plurality of transforms based on the associated output chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5).
Although Mingqiang does not explicitly teach “a data bus”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that these are all processing modules that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 5, Mingqiang teaches a system (retrieval system) (p. 63; Section 1., 1st paragraph) for performing object recognition (shape matching and object recognition using chord contexts) (p. 63; Title) based on an acquired image comprising a plurality of edge pixels (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7) and at least one reference image (determining the correspondence between shapes) (p. 65; Section 2.2., 1st paragraph) comprising a plurality of edge pixels (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7), the system comprising:
a plurality of transforms, each transform associated with an input chord length and an output chord length (wherein the each matrix for the query and target images is based on the set of chords in every direction) (pages 63-64; Section 2.1.), wherein each transform is characterized by a scale based on a ratio of the associated input and output chord lengths (in order to reduce the size of the matrix M and, at the same time, make the feature extracted invariant with scale transforms, we normalize this matrix M) (pages 63-64; Section 2.1.);
a) a first set of features consisting of a first plurality of chords, each of the first plurality of chords characterized by a length between two edge pixels in the acquired image (wherein each chord is a length between two edge pixels in the query image) (pages 63-64; Section 2.1., Fig. 1);
wherein each of the first plurality of chords is coupled to at least one of the plurality of transforms based on the associated first chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5); and
b) a second set of features consisting of a second plurality of chords, each of the second plurality of chords characterized by a length between two edge pixels in the reference image (wherein each chord is a length between two edge pixels in the matching image) (pages 63-64; Section 2.1., Fig. 1);
wherein each of the second plurality of chords is coupled to at least one of the plurality of transforms based on the associated second chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5);
to output a number of the first plurality of chords that map to one of the second plurality of chords (finding the similarity between the query image and the matches) (pages 65-66; Section 2.2., Fig. 7, and p. 67; Section 3.1) (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.) via one or more of the plurality of transforms that are characterized by a current scale (scale and rotation transforms) (p. 67; Section 3.1).
Although Mingqiang does not explicitly teach “a feature detector”, or “a summing circuit”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that these are all processing modules that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 6, Mingqiang teaches further comprising a scale selector module configured to: select the current scale from a sequence comprising a plurality of scales (going through a plurality of scales) (p. 67; Section 3.1); activate any of the plurality of transforms characterized by a scale equal to the current scale (wherein a normalization is used for each scale) (p. 67; Section 3.1); wherein to output a number of input chords that map to an output chord for each scale of the plurality of scales (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.).
Although Mingqiang does not explicitly teach “a summing circuit”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that it’s a processing module that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 8, Mingqiang teaches wherein counting signals from the second plurality of chords, wherein the signals correspond to the number of first plurality of chords that map to one of the second plurality of chords (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.) via one or more of the plurality of transforms that are characterized by a current scale (scale and rotation transforms) (p. 67; Section 3.1).
Although Mingqiang does not explicitly teach “a pulse counter”, or “a summing circuit”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that these are all processing modules that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 11, Mingqiang teaches further configured to connect each of the second plurality of chords with at least one of the plurality of transforms (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5).
Although Mingqiang does not explicitly teach “a data bus”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that it’s a processing module that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps.
Regarding claim 12, Mingqiang teaches wherein configured to connect to the second plurality of chords (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5); wherein the second plurality of chords correspond to a plurality of reference images (wherein the chords can correspond to the matching reference images) (pages 65-66; Section 2.2., and Fig. 7), whereby a plurality of objects can be searched in parallel (wherein a plurality of shapes can be searched at the same time) (pages 65-66; Section 2.2., and Fig. 7).
Although Mingqiang does not explicitly teach “a data bus”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that it’s a processing module that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps
Regarding claim 13, Mingqiang teaches further configured to connect each of the first plurality of chords with at least one of the plurality of transforms (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5).
Although Mingqiang does not explicitly teach “a data bus”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that it’s a processing module that would obviously be part of the retrieval system (p. 63; Section 1., 1st paragraph) to process the image processing steps
Regarding claim 17, Mingqiang teaches a method of performing object recognition (shape matching and object recognition using chord contexts) (p. 63; Title) based on an acquired image comprising a plurality of edge pixels (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7) and at least one reference image (determining the correspondence between shapes) (p. 65; Section 2.2., 1st paragraph) comprising a plurality of edge pixels (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7), the method comprising:
generating a first plurality of chords, each of the first plurality of chords characterized by a length between two edge pixels in the acquired image (wherein each chord is a length between two edge pixels in the query image) (pages 63-64; Section 2.1., Fig. 1);
wherein each of the first plurality of chords is coupled to at least one of the plurality of transforms based on the associated chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5);
generating a second plurality of chords, each of the second plurality of chords characterized by a length between two edge pixels in the reference image (wherein each chord is a length between two edge pixels in the matching image) (pages 63-64; Section 2.1., Fig. 1);
wherein each of the second plurality of chords is coupled to at least one of the plurality of transforms based on the associated chord length (wherein each chord of the plurality of chords is coupled to the normalization) (pages 63-64; Section 2.1., Fig. 1, and Fig. 5);
generating an estimated scale for a plurality of chord pairs (scale and rotation transforms; normalization) (p. 67; Section 3.1), each chord pair comprising one of the first plurality of chords and one of the second plurality of chords (finding the similarity between the query image and the matches) (pages 65-66; Section 2.2., Fig. 7, and p. 67; Section 3.1);
selecting one of a plurality of scales (scale and rotation transforms; normalization) (p. 67; Section 3.1);
determining a number of chord pairs correspond to said one of the plurality of scale (finding the similarity between the query image and the matches) (pages 65-66; Section 2.2., Fig. 7, and p. 67; Section 3.1);
if and when the number exceeds a predetermined threshold (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.).
Although Mingqiang does not explicitly teach to “output a recognition signal”, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that that outputted matches (pages 65-66; Section 2.2., and Fig. 7) are an obvious outputted recognition signal.
Regarding claim 18, Mingqiang teaches a) extracting a plurality of edge pixels from the acquired image (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7); and b) extracting a plurality of edge pixels from the reference image (detect edge pixels based on the chords that connect between to edge pixels; this is done for the 1st image that is compared to the 2nd image; query versus matches) (pages 63-64; Section 2.1., Fig. 1, and p. 66; Fig. 7).
Regarding claim 19, Mingqiang teaches generating an estimated rotation angle for each of the plurality of chord pairs (wherein generating an estimated angle that increases from 0 to 179 degrees) (p. 64, right column, 1st paragraph); and determining a number of chord pairs corresponding to said one of the plurality of scales (wherein a match is considered when the minimum value of the distance set at the similarity distance; i.e. towards convergence and thus a larger number of chords match) (pages 65-66; Section 2.2., and Fig. 7) (such as if shape 1 is more similar to shape 2 than shape 3 then the value between shape 1 and shape 2 will have the smallest value) (pages 65-66; Section 2.2.) and one of the plurality of estimated rotation angles (because all the viewpoint directions, considered with a certain angle interval, are chosen to produce the chord length histogram) (p. 63; Section 1, 3rd paragraph).
Allowable Subject Matter
Claims 4, 7, 9, 10, and 14-16 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Prior art Toshev et al., “Shape-Based Object Detection via Boundary Structure Segmentation” teaches: shape representation, called a chordiogram, which is based on geometric relationships of object boundary edges and can be using for object recognition (p. 123; Abstract).
Contact
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/MICHAEL J VANCHY JR/Primary Examiner, Art Unit 2666 Michael.Vanchy@uspto.gov