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 1-20 are pending for examination in the instant application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1, 8 and 15 rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, 7 and 13 of U.S. Patent No. 12469316 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because see the table below:
Instant Application: 19246671 U.S. Patent No.: 12469316 B1
1. A method for authentication of a physical object based on microstructural features extracted from patches of a surface of the physical object, the method comprising:
processing a plurality of input surfaces of an object by a feature extractor using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera;
outputting by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces;
dividing the input surface of the object into a plurality of micro-surfaces;
training the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
applying the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
storing a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
using the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
1. A method for authentication of a physical object based on microstructural features extracted from patches of a surface of the physical object, the method comprising:
processing a plurality of input surfaces of an object by a feature extractor using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera;
outputting by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces wherein the feature extractor describes the input surface as a one-dimensional feature vector;
dividing the input surface of the object into a plurality of micro-surfaces;
training the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
applying the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
storing a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
using the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
8. A system for authentication of a physical object based on microstructural features extracted from patches of a surface of the physical object, the system comprises:
a backend module including machine learning (ML) models configured to process a plurality of input surfaces of an object by a feature extractor of the ML models using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera, and the backend module is configured to:
receive by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces;
divide the input surface of the object into a plurality of micro-surfaces;
train the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
apply the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
store a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
use the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
7. A system for authentication of a physical object based on microstructural features extracted from patches of a surface of the physical object, the system comprises:
a backend module including machine learning (ML) models configured to process a plurality of input surfaces of an object by a feature extractor of the ML models using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera, and the backend module is configured to:
receive by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces wherein the feature extractor describes the input surface as a one-dimensional feature vector;
divide the input surface of the object into a plurality of micro-surfaces;
train the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
apply the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
store a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
use the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
15. A computer-program product tangibly embodied in a non-transitory machine- readable storage medium, including instructions configured to cause a data processing apparatus to perform operations, for authenticating a physical object based on microstructural features extracted from patches of a surface of the physical object, the operations comprising:
processing a plurality of input surfaces of an object by a feature extractor using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera;
outputting by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces;
dividing the input surface of the object into a plurality of micro-surfaces;
training the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
applying the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
storing a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
using the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
13. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations, for authenticating a physical object based on microstructural features extracted from patches of a surface of the physical object, the operations comprising:
processing a plurality of input surfaces of an object by a feature extractor using a deep neural network, wherein the plurality of input surfaces varies in dimensions, and the plurality of input surfaces are processed by the deep neural network from a plurality of images of the object captured by a device camera;
outputting by the feature extractor, an input surface from the plurality of input surfaces which is uniformly sized with respect to sizes of the plurality of input surfaces wherein the feature extractor describes the input surface as a one-dimensional feature vector;
dividing the input surface of the object into a plurality of micro-surfaces;
training the feature extractor on the plurality of micro-surfaces to identify a plurality of microstructural features of the input surface of the object;
applying the trained feature extractor on a plurality of surfaces corresponding to a plurality of objects, the plurality of surfaces have different dimensions from the plurality of input surfaces;
storing a set of microstructural features of the plurality of surfaces corresponding to the plurality of objects in a database; and
using the trained feature extractor for authenticating a plurality of query objects based on the plurality of microstructural features of the input surface corresponding to each query object, wherein the authentication includes matching the plurality of microstructural features obtained from images of a query object with the set of microstructural features of the plurality of objects stored in the database.
The difference being that the instant claims are broader than the conflicting claims and the conflicting claims contain the limitation e.g., the feature extractor describes the input as a one-dimensional feature vector. Hence the conflicting claims are a species of a genus of the instant claims.
The instant claims merely broaden the scope of the conflicting claims. It is well settled that broadening the scope of claims would have been obvious to one of ordinary skill in the art in view of the narrower issued claims. In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982) and In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
The dependent claims carry the deficiencies from the base claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892.
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/Tauqir Hussain/Primary Examiner, Art Unit 2449
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