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 .
Response to Arguments
Applicant’s arguments, filed on 03/26/2026, with respect to claims 12-17, have been fully considered and are persuasive. The rejection of said claims has been withdrawn and claims are indicated allowable over the prior art of the record..
Applicant's arguments filed with respect to independent claims 1 and 18 (and their respective dependent claims) have been fully considered and are persuasive partially. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wang (CN 108549873 A, an English translation of the CN is attached herewith, hereinafter Wang).
Further, Regarding claims 1 and 18 (and their respective dependent claims) Applicant argues that “The cited portions of Dal do not teach or suggest "calculating a first vector distance between the geometric asset feature of the candidate 3D object and the geometric asset feature of the reference 3D object" or "calculating a second vector distance between the semantic feature vector of the candidate 3D object and the semantic feature vector of the reference 3D object" per amended claim 1.” (please see Remarks, page 11, first paragraph).
Examiner respectfully disagrees, as Dal in paragraph 147, discloses “all of the objects in the training set may be supplied to the first stage CNN.sub.1 to generate a set of known descriptors {F.sub.ds(m)}, where the index m indicates a particular labeled shape in the training data. A similarity metric is defined to measure the distance between any two given descriptors (vectors) F and F.sub.ds(m). Some simple examples of similarity metrics are a Euclidean vector distance and a Mahalanobis vector distance. …A metric learning algorithm may learn a linear or non-linear transformation of feature vector space that minimizes the average distance between vector pairs belonging to the same class (as measured from examples in the training data) and maximizes the average distance between vector pairs belonging to different classes”. Hence as can be seen from the passage that Dal discloses calculating a first vector distance between the geometric asset feature of the candidate 3D object and the geometric asset feature of the reference 3D object" i.e., known descriptors {F.sub.ds(m)}, where the index m indicates a particular labeled shape and "calculating a second vector distance between the semantic feature vector of the candidate 3D object and the semantic feature vector of the reference 3D object i.e., known descriptors {F.sub.ds(m)}, where the index m indicates a particular labeled shape and similarity metrics being calculated where similarity metric is defined to measure the distance between any two given descriptors (vectors) F and F.sub.ds(m), corresponds to claimed first vector distance and second vector distance. Therefore Dal discloses the argued limitation as presented by the Applicant.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 9-11, and 18-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1:
Claim 1 is directed to idea of itself (abstract idea) without significantly more for the following reason(s):
Step 1: Claim 1 recites a computer-implemented method, comprising: determining a geometric asset feature of a candidate 3D object based on a plurality of images of the candidate 3D object; determining a semantic feature vector of the candidate 3D object; determining a degree of similarity between the candidate 3D object and a reference 3D object by calculating a first vector distance between the geometric asset feature of the candidate 3D object and a geometric asset feature of the reference 3D object; calculating a second vector distance between the semantic feature vector of the candidate 3D object and a semantic feature vector of the reference 3D object; and generating a fused vector distance by combining the first vector distance and the second vector distance; and classifying the candidate 3D object based on the degree of similarity between the candidate 3D object and the reference 3D object. Thus, the claim is directed to method, which is one of the statutory categories of the invention.
Step 2A prong 1, the claimed determining geometric asset feature (mental process grouping), determining a semantic feature vector (mental process grouping), and determining a degree of similarity (mental process grouping), are directed to abstract idea for the reason that these steps are processes found by the courts to be abstract ideas in that related to mental processing grouping, i.e., concepts that are capable of being performed in human mind or with pen and paper. Further, calculating a first vector distance and second vector distance steps and then generating a fused vector steps are directed to abstract idea for the reason that these steps are processes found by the courts to be abstract ideas in that related to “mathematical concepts grouping”, more specifically, mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. Thus, these steps are an abstract idea in the “mathematical concepts grouping”. Accordingly, the claim recites an abstract idea.
Step 2A prong 2, The Judicial exception is not integrated into a practical application. Treating claim 1 as a whole, the claim limitations do not show inventive concept in applying the judicial exception. From the claim scope, the claim fail to address any improvement because merely determining, calculating and classifying object, is not enough to tie the claim towards the technical improvement and can be performed mentally and/or mathematically. There are no further additional elements in the claim that can be integrated as an exception into practical application of the exception. Accordingly, the claim recites an abstract idea.
Step 2B, The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites determining geometric asset feature (mental process grouping), determining a semantic feature vector (mental process grouping), and determining a degree of similarity (mental process grouping), calculating a first vector and second vector (mathematical concept grouping) and generating a fused vector (mathematical concept grouping), calculating and classifying object, is not enough to tie the claim towards the technical improvement and can be performed mentally and/or mathematically. The claim do not go beyond general data manipulation based on mathematical algorithms and/or mental processing. Thus, claimed method falls with the judicial exception to patent eligible subject matter of an abstract idea without significantly more.
Regarding dependent claims 2-7, and 9-11.
Dependent claims 2-7, and 9-11, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further none of the dependent claims show inventive concept in applying the judicial exception. Therefore, the claims are not patent eligible.
Regarding claim 18.
Claim 18, please see the analysis of claim 1, further claim 18, recites additional elements “a non-transitory computer readable medium” and “a processing device”, the claimed “a non-transitory computer readable medium” and “a processing device” are well-understood, routine activities, a claim reciting a generic computer component performing a generic computer function is necessarily ineligible. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking) component cannot provide an inventive concept. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. The Court also discussed this concept in an earlier case, Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), where the claim recited a process for converting binary-coded-decimal (BCD) numerals into pure binary numbers. The Court found that the claimed process had no meaningful practical application except in connection with a computer. Benson, 409 U.S. at 71-72, 175 USPQ at 676. The Federal Circuit described the claims as directed to an abstract idea because they were “squarely about creating a contractual relationship--a ‘transaction performance guaranty’.” 765 F.3d at 1355, 112 USPQ2d at 1096. The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that “apply it” in a computer. Therefore, the claim is not patent eligible.
Regarding dependent claims 19-20.
Dependent claims 19-20, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further none of the dependent claims show inventive concept in applying the judicial exception. Therefore, the claims are not patent eligible.
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.
Claim(s) 1-2, 5-6, 8, 10-11, and 18, is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal (US PGPUB 2019/0108396 A1) and further in view of Wang (CN 108549873 A, an English translation of the CN is attached herewith, hereinafter Wang).
As per claim 1, Dal discloses a computer-implemented method, comprising:
determining a geometric asset feature of a candidate 3D object based on a plurality of images of the candidate 3D object (Dal, paragraphs 84 and 197, discloses “the properties that can be inspected fall into two categories: variables and attributes. The term “variables,” as used herein in the context of the analysis of the 3-D model, refers to measurable physical quantities, e.g., the size of the minimum bounding box containing the object itself, the volume occupied by the object, and the size of a shoe, and the like. The term “attributes,” as used herein in the context of the analysis of the 3-D model, refers to aspects that are not necessarily numeric values, such as the presence or absence of pronounced wrinkles on a leather component or the presence or absence of stains on fabric components (e.g., in excess of some threshold tolerance). In other words, a variable may be characterized by a specific numeric value, which can be uniquely measured, whereas an attribute may relate to the presence or absence of a characteristic (e.g., a binary value) in accordance with a subjective or qualitative analysis (e.g., two different people may provide different inspection results for an “attribute” or a qualitative characteristic));
determining a semantic feature vector of the candidate 3D object (Dal, paragraphs 156, 197, and 222, discloses the trained CNN may be applied to extract a feature vector from a scan of an object under inspection. The feature vector may include color, texture, and shape detected in the scan of the object. The classifier may assign a classification to the object, where the classifications may include being defect-free (or “clean”) or having one or more defects);
determining a degree of similarity between the candidate 3D object and a reference 3D object (Dal, paragraphs 147 and 195) by:
calculating a first vector distance between the geometric asset feature of the candidate 3D object and the geometric asset feature of the reference 3D object (Dal, paragraph 147, discloses A similarity metric is defined to measure the distance between any two given descriptors (vectors) F and F.sub.ds(m). Some simple examples of similarity metrics are a Euclidean vector distance and a Mahalanobis vector distance. In other embodiments of the present invention a similarity metric is learned using a metric learning algorithm);
calculating a second vector distance between the semantic feature vector of the candidate 3D object and the semantic feature vector of the reference 3D object (Dal, paragraph 147, discloses In more detail, all of the objects in the training set may be supplied to the first stage CNN.sub.1 to generate a set of known descriptors {F.sub.ds(m)}, where the index m indicates a particular labeled shape in the training data. A similarity metric is defined to measure the distance between any two given descriptors (vectors) F and F.sub.ds(m). Some simple examples of similarity metrics are a Euclidean vector distance and a Mahalanobis vector distance….A metric learning algorithm may learn a linear or non-linear transformation of feature vector space that minimizes the average distance between vector pairs belonging to the same class (as measured from examples in the training data) and maximizes the average distance between vector pairs belonging to different classes); and
classifying the candidate 3D object based on the degree of similarity between the candidate 3D object and the reference 3D object (Dal, paragraphs 133-134, discloses the task of classifying a shape s into one of a set C of given classes (also called categories or labels) is distinguished from the task of retrieving from a database the shape that is most similar (under a specific metric) to a given shape).
Dal does not explicitly disclose generating a fused vector distance by combining the first vector distance and the second vector distance.
Wang discloses generating a fused vector distance by combining the first vector distance and the second vector distance (Wang, Fig. 4:S160, and page 13, discloses the step S160 comprises: calculating the Euclidean distance and the cosine distance between the characteristic vector to be identified and the preset characteristic vector in the human face characteristic database, and weighting and combining the Euclidean distance and the cosine distance to obtain the distance to be identified;).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dal teachings by determining distance between vectors as taught by Wang.
The motivation would be to provide an improved system for three-dimensional object recognition with accuracy (paragraph 2), as taught by Wang.
As per claim 2, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein the first vector distance is a Euclidean vector distance and the second vector distance is a cosine similarity (Wang, page 13).
As per claim 5, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein combining the first vector distance and the second vector distance further comprises applying a respective transformation function to the first vector distance and the second vector distance (Dal, paragraph 147).
As per claim 6, Dal in view of Wang further discloses the computer-implemented method of claim 1, further comprising:
constructing a fused vector distance matrix that includes respective fused vector distances between a plurality of 3D objects that includes the candidate 3D object (Dal, paragraphs 207 and 253); and
applying multidimensional scaling (MDS) to the fused vector distance matrix to determine a plurality of updated feature vectors for each of the plurality of 3D objects (Dal, paragraph 225), wherein each updated feature vector represents a fused vector distance of the corresponding 3D object to other 3D objects of the plurality of 3D objects, and wherein a dimension of each updated feature vector is lower than a dimension of the corresponding geometric asset feature and the corresponding semantic feature vector (Dal, paragraph 225, discloses in some embodiments methods similar to multi-dimensional scaling (MDS) are used. Multi-dimensional scaling is a form of non-linear dimensionality reduction, and, in some embodiments, all or a portion of the 3-D surface of the scanned model of the object is converted (e.g., mapped) onto a two-dimensional (2-D) representation. In this mapping, the geodesic distances among the 3-D surface points (that may include surface defects) are substantially maintained in the 2-D representation. Representing all, or a portion, of the 3-D surface using a 2-D encoding allows the use of conventional convolutional neural network (CNN) techniques that are designed to be performed on 2-D images. Because the 2-D representation substantially maintains the 3-D distances between the points, the defects that are categorized by actual real-world sizes can also be detected).
As per claim 8, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein determining the semantic feature vector of the candidate 3D object comprises:
obtaining one or more images of the candidate 3D object (Dal, paragraph 84); and
analyzing the one or more images with a pre-trained machine learning model to obtain the semantic feature vector of the candidate 3D object (Dal, paragraph 138, discloses trained neural network model), wherein the machine learning model is trained via contrastive learning based on predicting matching pairs of image and associated text from a training dataset (Dal, paragraphs 138 and 257), wherein the semantic feature vector is a high-dimensional vector, and wherein each dimension of the high-dimensional vector encodes respective semantic information (Dal, paragraphs 138 and 257).
As per claim 10, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein prior to determining the degree of similarity between the candidate 3D object and the reference 3D object, the method further comprises:
obtaining a first plurality of semantically similar reference 3D objects (Dal, paragraph 206, discloses the defect detection relates to detecting “abnormal” or “outlier” instances of the objects from among the collection of objects. In more detail, in some embodiments of the present invention, a group of 3-D scans of a representative sample of objects (instances) of the same class is collected. For the sake of convenience, it will be assumed that each of these instances is substantially identical in the idea case (e.g., they are all shoes of the model and have the same size) but that the actual instances vary in size and shape due, for example, to variability in the materials, variability in the consistency of the tools used to shape the materials, differences in skill of humans working on the assembly line );
obtaining a second plurality of geometrically similar reference 3D objects (Dal, paragraph 206, discloses under the assumption that the defect rate is relatively low (e.g., that most instances fall within a particular range), an “average” model can be computed from all of the “training” 3-D models of the representative sample of objects. For example, in one embodiment, the 3-D models of the representative samples are aligned (e.g., using iterative closest point). The aligned 3-D models can then be averaged. For example, for each point p.sub.1 on a first model, a closest point p.sub.2 of a second model is identified (e.g., by computing the distances to all points of the second model within a ball around point p.sub.1). The first point p.sub.1 and the second point p.sub.2 will be referred to herein as “corresponding” points of the 3-D models. All of the corresponding points across all of the training 3-D models may be identified and, for each set of corresponding points, an average (e.g., a mean) is computed and the collection of average points may constitute the “average” 3-D model);
forming a combined pool of geometrically similar and semantically similar reference 3D objects based on the first plurality of semantically similar reference 3D objects and the second plurality of geometrically similar reference 3D objects (Dal, paragraph 206); and
selecting the reference 3D object from the combined pool of geometrically similar and semantically similar reference 3D objects (Dal, paragraphs 146-147, discloses the classifier CNN.sub.2 classifies the target object 10 by using the descriptor F of the target object to retrieve a most similar shape in a data set).
As per claim 11, Dal in view of Wang further discloses the computer-implemented method of claim 1, further comprising generating the plurality of images of the candidate 3D object, wherein each image of the plurality of images of the candidate 3D object is from a respective camera position of two or more camera positions (Dal, paragraph 245).
As per claim 18, Dal in view of Wang discloses a non-transitory computer-readable medium with instructions stored thereon that, responsive to execution by a processing device, cause the processing device to perform operations (Dal, paragraphs 20-21) comprising:
For rest of claim limitations please see the analysis of claim 1.
Claim(s) 7, and 9, is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal (US PGPUB 2019/0108396 A1) and further in view of Wang (CN 108549873 A, an English translation of the CN is attached herewith, hereinafter Wang) and further in view of An (US PGPUB 2015/0154229 A1).
As per claim 7, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein determining the geometric asset feature of the candidate 3D object comprises:
Dal in view of Wang does not explicitly disclose determining one or more histogram of oriented gradients (HOG) vectors for each image of the plurality of images of the candidate 3D object; and calculating the geometric asset feature of the candidate 3D object based on the one or more HOG vectors for each of the plurality of images of the candidate 3D object.
An discloses determining one or more histogram of oriented gradients (HOG) vectors for each image of the plurality of images of the candidate 3D object (An, paragraph 59); and calculating the geometric asset feature of the candidate 3D object based on the one or more HOG vectors for each of the plurality of images of the candidate 3D object (An, paragraphs 59 and 129).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dal in view of Wang teachings by implementing feature extraction technique to the system, as taught by An.
The motivation would be to provide a system to improve the similarity measure (paragraph 112), as taught by An.
As per claim 9, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein classifying the candidate 3D object comprises determining a uniqueness of the candidate 3D object, and wherein determining the uniqueness of the candidate 3D object (Dal, paragraphs 207 and 253) comprises:
determining a corresponding fused vector distance between the candidate 3D object and each of a plurality of reference 3D objects (Dal, paragraphs 207 and 253);
determining a plurality of neighboring 3D objects to the candidate 3D object (Dal, paragraph 134);
and determining a uniqueness score based on the local density for the candidate 3D object and a maximum local density of the plurality of reference 3D objects (Dal, paragraph 215).
Dal in view of Wang does not explicitly disclose determining a local density for the candidate 3D object by calculating an average fused vector distance between the candidate 3D object and the plurality of neighboring 3D objects;
An discloses determining a local density for the candidate 3D object by calculating an average fused vector distance between the candidate 3D object and the plurality of neighboring 3D objects (An, paragraph 150);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dal in view of Wang teachings by implementing feature extraction technique to the system, as taught by An.
The motivation would be to provide a system to improve the similarity measure (paragraph 112), as taught by An.
Claim(s) 3-4, and 20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal (US PGPUB 2019/0108396 A1) and further in view of Wang (CN 108549873 A, an English translation of the CN is attached herewith, hereinafter Wang) and further in view of Edwards (US PGPUB 2021/0350346 A1)
As per claim 3, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein classifying the candidate 3D object comprises:
determining if the fused vector distance meets an inauthentic object threshold (Wang, page 2, discloses comparing the distance to be identified with a preset distance threshold, and implementing three-dimensional face recognition according to the comparison result, hence obvious variation);
Dal in view of Wang does not explicitly disclose if the fused vector distance meets the inauthentic object threshold, classifying the candidate 3D object as an inauthentic object; and
if the fused vector distance does not meet the inauthentic object threshold, classifying the candidate 3D object as an authentic object.
Edwards discloses if the fused vector distance meets the inauthentic object threshold, classifying the candidate 3D object as an inauthentic object (Edwards, paragraphs 42, 55, 68 and 70); and
if the fused vector distance does not meet the inauthentic object threshold, classifying the candidate 3D object as an authentic object (Edwards, paragraphs 42, 55, 68 and 70).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dal in view of Wang teachings by implementing threshold conditioning to the system, as taught by Wang.
The motivation would be to provide an improved system for facial recognition classification (paragraph 42), as taught by Edwards.
As per claim 4, Dal in view of Wang further discloses the computer-implemented method of claim 1, wherein classifying the candidate 3D object comprises:
determining if the fused vector distance meets a similarity threshold (Wang, page 2, discloses comparing the distance to be identified with a preset distance threshold, and implementing three-dimensional face recognition according to the comparison result, hence obvious variation);
Dal in view of Wang does not explicitly disclose if the fused vector distance meets the similarity threshold, classifying the candidate 3D object as a similar object to the reference 3D object; and
if the fused vector distance does not meet the similarity threshold, classifying the candidate 3D object as a dissimilar object to the reference 3D object.
Edwards discloses if the fused vector distance meets the similarity threshold, classifying the candidate 3D object as a similar object to the reference 3D object (Edwards, paragraphs 42, 55, 68 and 70); and
if the fused vector distance does not meet the similarity threshold, classifying the candidate 3D object as a dissimilar object to the reference 3D object (Edwards, paragraphs 42, 55, 68 and 70).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dal in view of Wang teachings by implementing threshold conditioning to the system, as taught by Wang.
The motivation would be to provide an improved system for facial recognition classification (paragraph 42), as taught by Edwards.
As per claim 20, please see the analysis of claim 4.
Allowable Subject Matter
Claims 12-17, are allowed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED Z HAIDER whose telephone number is (571)270-5169. The examiner can normally be reached MONDAY-FRIDAY 9-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SAM K Ahn can be reached at 571-272-3044. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED HAIDER/Primary Examiner, Art Unit 2633
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