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 .
DETAILED ACTION
Status of Claims
Claims 1-10 are currently pending in this application.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
FP 7.30.05
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
FP 7.30.06
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “Column 2 of Table 1” in claim “Column 5 of Table 1” with generic placeholder “Column 3 of Table 1”.
Claim limitation
Generic placeholder
Functional language
Claim number
1
an image converting
module
configured to analyze a face of an image …
1
2
an image generating
module
configured to save a lookup table and …
1
3
an image combining
module
configured to combine the real-time 3D face model …
5
Table 1
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 of this title, 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2(c) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gope et al. (11,908,238) in view of Kim et al. (2006/0210168) and further in view of Wang et al. (2017/0316598).
Regarding claim 1, Gope teaches an image generating system having high face positioning precision (e.g., selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching. Gope: c. 3 L.16-18. Therefore, image generated by selecting the pre-stored image with a match score above the pre-defined threshold), comprising:
an image converting module configured to analyze a face of an image via an artificial intelligence model to generate a plurality of feature points (e.g., The image input unit 202 is configured to receive data from at least one of the real-time streaming system 104, the video/image archive 106, and the computer system 108. The data primarily comprises of at least one image/frame captured in real-time by the video/image capturing devices 104b. In an embodiment of the invention, the data corresponds to at least one image/frame previously stored in the video/image archive 106 or the computer system 108. Gope: c.5 L.37-44. The face feature points detection unit 206 is configured to detect a set of feature points in a face of the one or more faces detected by the face detection unit 204. Examples of the feature points include, but are not limited to eyes, nose, lips, eyebrows, mouth, lips, ears and the like. Gope: c.6 L.4-8. In an embodiment of the invention, the initial positions of feature points/parts of the face such as eyes, nose and lips are estimated using a constrained deformable part-based model (CDPBM). This model uses HoGs as part-based filters and is trained by latent Support Vector Machines (SVM) where the location and geometry of the parts form the latent variables. The detected face is rescaled to a fixed size and processed at single scale. Gope: c.6 L.17-24 The feature extraction system further comprises of a filtering unit, a mapping unit, a region selection unit, and a feature description unit. The filtering unit is configured to generate a set of Gabor Magnitude Images (GMIs) for the image using multi-orientation (p) and multi-scale (q) Gabor filters. The set of GMIs comprises of p*q GMIs. The values for each of the p and q are selected based on at least one of an inter class feature distance and intra class feature distance computed for a set of training images. Gope: c.1 L.62-67 and c.2 L.1-4. The combination of all the feature descriptions corresponding to the one or more feature points detected in the input image is referred to as the signature of the input image. Gope: c.10 L.18-21. Therefore, the system is based on a machine training (artificial intelligence) model), wherein each of feature points has a feature point coordinate (see 1_1 below); and
an image generating module configured to save a lookup table and a default grid model having a plurality of grid points, wherein a number of the feature points is equal to a number of the grid points (e.g., a data storage may be configured to store a set of training images (pre-stored images) that are used for facial recognition. Along with these training images, the data storage may be configured to store related information such as feature descriptors of the training images i.e., training feature descriptors and additional information for the training images. For example, when a training image includes a face, the additional information includes gender, race, age, measurements, etc. Gope: c.10 L.22-30. The number of training feature descriptors may be extremely large for millions of training images, therefore, there is a need to limit the number of training feature descriptors. One way to limit the number of the training feature descriptors is to use vector quantization techniques such as bag-of-words technique. To match the input image with the set of training images in the data storage, a closest match for each feature description of the input image is identified against the training feature descriptors. Gope: c.10 L.31-39. The feature points of the training images are interpreted the grid points. As the feature description unit processes both the input image and training image, the same number of feature points are extracted for both images. See 1_2 below),
wherein the lookup table records a grid point coordinate of the grid point corresponding to the feature point coordinate of each of the feature points; wherein the image generating module is configured to find out the feature points matching the grid points, and align at least a portion of the grid points with the feature points corresponding thereto so as to combine the default grid model with the face and generate a real-time three-dimensional (3D) face model (e.g., a method of facial recognition comprising the steps of: extracting a feature descriptor from a detected feature point of a detected face in an input image frame 602; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching 604. The user identity verified by way of the more robust matching technique may be coupled to an on-site provisioning system (gate-keeping/payment transaction). Gope: c.10 L.48-60. In continuing reference back to FIG. 5, the index mapping unit 502 creates an index mapping in order to speed up identifying a matching image among the set of training images for the input image. The index mapping unit 502 creates the index mapping based on the training feature descriptors. The index mapping is created in at least one format of an array, a hash table, a lookup table and a k-dimensional (k-d) tree. Gope: c.10 L.61-67 and c.11 L.1-4. The face alignment and normalization unit 208 is configured to align and normalize the feature points detected for the face. The feature points are mapped to a pre-defined 3D face model. This provides a mapping for the face from 2D to 3D. Once the feature points are mapped, this 3D face model is back projected to obtain an aligned 2D face. Gope: c.6 L.29-34. See 1_1 and 1_2 also).
While Gope does not explicitly teach, Kim teaches:
(1_1). wherein each of feature points has a feature point coordinate (e.g., The feature points of respective face images are arranged in different local coordinate systems, according to the characteristics of an input face image. That is, according
to the conditions when photographing the face, each face image has a different size (scale (s)), a different rotation degree ( direction ([Symbol font/0x71] ), and a different position of the center point (translation (tx, ty)). Accordingly, while keeping the shape characteristics of the face, feature point data extracted from this face image needs to be aligned approximately in a common reference coordinate system. Kim: [0031] L.1-10. Therefore, with a common reference coordinate system, the relationships between different features (points) can be calculated);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine teaching of Kim into the teaching of Gope so that size, scale, directions and positions between different feature points can be obtained.
While the combined teaching of Gope and Kim does not explicitly teach, Wang teaches:
(1_2). an image generating module (e.g., A 3D human face reconstruction apparatus includes an image feature point determining unit configured to obtain a 2D human face image for 3D human face reconstruction, and determine feature points on the 2D human face image, where the feature points are representative of a human face contour. The apparatus further includes a posture adjusting unit configured to determine posture parameters of a human face by using the feature points, and adjust, based on the posture parameters, a posture of a general 3D human face model obtained in advance. The apparatus further includes a feature point matching unit configured to determine points on the general 3D human face model corresponding to the feature points, and adjust corresponding points in a blocking state to obtain a preliminary 3D human face model. The apparatus further includes a model deforming unit configured to perform deformation adjusting on the preliminary 3D human face model to obtain a deformed 3D human face model, so that a positional relationship among the corresponding points on the deformed 3D human face model is consistent with a positional relationship among the feature points on the 2D human face image. The apparatus further includes a texture mapping unit configured to perform texture mapping on the deformed 3D human face model to obtain a 3D human face. Wang: [0008]. Therefore, a 3D human face is generated from the feature points identified by the feature points of pre-defined 3D face model and deformed in consistent with feature points on the 2D human face image);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wang into the combined teaching of Gope and Kim so that a 3D human face model can be obtained with the features points and deformed the 3D human face in consistent with the positional relationship among the feature points on the 2D human face image
Regarding claim 2, the combined teaching of Gope, Kim and Wang teaches the image generating system having high face positioning precision as claimed in claim 1, wherein the image generating module is configured to align at least a portion of the grid points with the feature points corresponding thereto without breaking or distorting the default grid model (e.g., The face alignment and normalization unit 208 is configured to align and normalize the feature points detected for the face. The feature points are mapped to a pre-defined 3D face model. This provides a mapping for the face from 2D to 3D. Once the feature points are mapped, this 3D face model is back projected to obtain an aligned 2D face. Gope: c.6 L.29-34).
Regarding claim 3, the combined teaching of Gope, Kim and Wang teaches the image generating system having high face positioning precision as claimed in claim 1, wherein the image generating module is configured to arrange the grid points not matching the feature points according to a shape of the default grid model (e.g., a method of facial recognition comprising the steps of: extracting a feature descriptor from a detected feature point of a detected face in an input image frame 602; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching 604. The user identity verified by way of the more robust matching technique may be coupled to an on-site provisioning system (gate-keeping/payment transaction). Gope: c.10 L.48-60).
Regarding claim 4, the combined teaching of Gope, Kim and Wang teaches the image generating system having high face positioning precision as claimed in claim 3, wherein the image generating module is configured to arrange the feature points unable to be identified by the image generating module according to the shape of the default grid model (e.g., To reduce distracting influence of elements such as glasses, and hair on the forehead and ears. Gope: c.6 L.53-54. It is obvious that face with glasses could hide feature points of eyes, eyebrows of a real-time 2D face. The pre-stored facial image, assumed without glasses, would have feature points of eyes and eyebrows and these feature points are unable to be identified in the 2D face with glasses).
Regarding claim 5, the combined teaching of Gope, Kim and Wang teaches the image generating system having high face positioning precision as claimed in claim 1, further comprising an image combining module configured to combine the real-time 3D face model with a decorative image to generate a combined image (e.g., In an embodiment, upper and lower edges of glasses are detected and then, special filters are applied to remove the effect of glasses on the face. This improves feature quality in the area around eyes leading to improved accuracy. This is particularly useful in matching the face of a person wearing glasses with an image of the same person without glasses. In addition to that, hair is detected on the face and weightages corresponding to the areas with hair are reduced. Gope: c.6 L.55-64. Therefore, the glasses is interpreted as a decoration put on the face).
Regarding claims 6-10, the claims are method claims of system claims 1-5 respectively. The claims are similar in scope to claims 1-5 respectively and they are rejected under similar rationale as claims 1-5 respectively.
Gope teaches that “The present invention generally relates to the field of face recognition, and in particular, the disclosure relates to methods and systems for double matching of extracted feature descriptors of an image for enabling a Point-of-Recognition (POR) provisioning.” (Gope: [0001]).
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure:
a). Kinoshita (7,925,048) teaches that “A device and method for detecting feature points of an object from an image. A three-dimensional model is created in which a plurality of nodes corresponding to feature points in a learning image are defined. The model is projected onto an input image and a feature value is derived from a plurality of sampling points around a projection point of each node. An error estimated amount is computed based on the displacement of a feature point between a correct model and an error model. The three dimensional position of each feature point in the input image is estimated based on the error estimated amount and a three dimensional model.” (Kinoshita: Abstract).
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/SING-WAI WU/Primary Examiner, Art Unit 2611