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 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses a processor to perform some steps in claims 1-8 (i.e. a manufacture); a computer implemented method in claims 9-16 (i.e., a process); a system in claims 17-20 (i.e., a machine). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significantly more. The instant invention is broadly directed to comparing similarity between objects based on a signature data.
Claim 1 recites the following (with emphasis added):
At least one processor, comprising: one or more circuits to:
compute a covariance matrix for an object represented within a three-dimensional (3D) scene;
compute, using the covariance matrix, a signature for the object;
determine a similar object based on the signature for the object; and
maintain a single representation in memory to use for rendering both the similar object and the object.
Claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-8.
Claim 1 recites the steps for process of computing data, comparing data, which is directed to the mathematical relationships and calculations, a mathematical concept.
Claim 9 recites the following (with emphasis added):
A computer-implemented method, comprising:
computing a signature for an object, represented in a three-dimensional (3D) scene, based on input properties corresponding to an appearance of the object;
comparing the signature to a plurality of additional signatures for a group of additional objects represented in the 3D scene;
determining one of the additional signatures, for a respective additional object, is similar to the signature according to at least one similarity metric; and
replacing at least one of the object or the respective additional object with a reference object.
Claim 9 encompass the abstract idea, which is also encompassed by the dependent claims 10-16.
Claim 9 recites the steps for process of computing data, comparing data and determining similarity and representing data, which is directed to the mathematical relationships and calculations, a mathematical concept.
Claim 17 recites the following (with emphasis added):
A system, comprising: one or more processing units to
determine two or more objects within a three-dimensional (3D) scene are within a threshold similarity based on respective signatures corresponding to the two or more objects and to cause the two or more objects to be stored as a common reference representation.
Claim 17 encompass the abstract idea, which is also encompassed by the dependent claims 18-20.
Claim 17 recites the steps for process of comparing data and determining similarity and representing data, which is directed to the mathematical relationships and calculations, a mathematical concept.
Prong Two
Claim 1 recites using a processor, circuits and memory to perform the abstract idea; Claim 17 recites suing processing unit to perform the abstract idea. This judicial exception is not integrated into a practical application because mere instruction to implement on a computer or a computer model, or merely using a computer or computer model as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment or field of use is not considered integration into a practical application. Dependent claim 7 recites using training data to train a neural network model. Using training data to train a neural network model is a generic feature of neural network, which does not represent a technological improvement. Dependent claim 8 and 20 recites a generic example system. The using of the computer, a generic system and the neural network model does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. Claims 2-7, 10-16, 18-19 are about mathematical relationships and calculations, which are abstract idea. Claims 4, 12, 15, 16 recites saving data. The claims do not include additional elements that are sufficient to enable the abstract idea to integrate into a practical application. The conventional computers over generic network as presented are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the mental processes using collected data to predict a result.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a computer (e.g. processor and memory). These additional elements are merely conventional computer. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing generic data handling using mathematical concepts). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are generally linked to implement an abstract idea on a computer. When looked at individually and as a whole, the claim limitations are determined to be an abstract idea without "significantly more," and thus 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) 1-6, 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2009/0204636) in view of Cirujeda et al. (“MCOV: acovariance descriptor for fusion of texture and shape features in 3D point clouds” from IDS).
Regarding claim 1, Li teaches:
At least one processor, comprising: one or more circuits to: ([0069])
determine a similar object based on the signature for the object; ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system.”) and
maintain a single representation in memory to use for rendering both the similar object and the object. ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system. If a second object is identified that has a signature equal to the signature of the object, then the exemplary method 80 branches at 88 and involves indexing 90 the object in the object index as a reference to the second object.”)
However, Li does not, but Cirujeda teaches:
Generating a signature for an object using:
compute a covariance matrix for an object represented within a three-dimensional (3D) scene; compute, using the covariance matrix, a signature for the object; (page 553, upper left: “
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Li teaches reducing memory saving redundancy by deciding if objects are similar to one another by comparing objects signatures. Cirujeda teaches a specific method of generating signatures of objects.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Li with the specific signature generation method of Cirujeda to accurately decide similarities among objects.
Regarding claim 2, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the one or more circuits are further to: determine a rigid rotation for the object.( Cirujeda, page 552, upper right: “
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The combination rationale of claim 1 is incorporated here.
Regarding claim 3, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the covariance matrix is computed from one or more 3D vectors for the object. (Cirujeda page 553, upper left: “
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The combination rationale of claim 1 is incorporated here.
Regarding claim 4, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the one or more circuits are further to: store the signature within a data representation; (Li [0041], “After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system. If a second object is identified that has a signature equal to the signature of the object, then the exemplary method 80 branches at 88 and involves indexing 90 the object in the object index as a reference to the second object. However, if the computer system fails to identify a second object having a signature equal to the signature of the object, the exemplary method 80 branches at 88 and involves storing 92 the object in the object system and indexing 94 the object in the object index as a reference to the object”)
determine a distance between the similar object and the object; and determine, based on the distance, that the similar object and the object are similar objects.( Cirujeda page 553, left bottom: “
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Li teaches determining if one object is redundant by comparing the signature similarity. Cirujeda teaches using covariance matrices to represent object signature and deciding objects similarity based on matrix distance.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Li with the specific signature generation method of Cirujeda to accurately decide similarities among objects.
Regarding claim 5, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the distance is determined by at least one of a squared vector normal or a cosine similarity function.( Cirujeda “
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The combination rationale of claim 1 is incorporated here.
Regarding claim 6, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein at least a portion of the covariance matrix is based on at least one of object vertex positions, object vertex normals, color, or vertex texture coordinates.( Cirujeda page 552, section A: “. In the context of a descriptor definition, the observed random variables are related to the set of observable features which can be extracted from points and their close localities in the scene, e.g. pixel color values, 3D coordinates, first or second order derivatives,”)
The combination rationale of claim 1 is incorporated here.
Regarding claim 8, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the processor is comprised in at least one of:…a system implemented at least partially in a data center….(Li FIG. 2. [0001], “Many computing scenarios involve the storage of objects in an object system according to physical locations on various memory devices, and the exposure of such objects to a user according to logical organization schemes. For example, a computer system may logically represent a collection of files as grouped together in a hierarchical file system, but the files may be physically stored as one or more segments in various sectors of a platter of a hard disk drive. The computer system may opaquely manage the storage of the objects on the physical media, and may provide hardware and software management routines to handle related technical issues (e.g., object fragmentation, media defragmentation, error detection and correction for media failures, accessor procedures for reduced access latency and improved streaming consistency, RAID schemes, hardware-level encryption and decryption, etc.) in the background while maintaining the logical organization of the objects.”)
Regarding claim 9, Li teaches:
A computer-implemented method, comprising:
computing a signature for an object, ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object.”)
comparing the signature to a plurality of additional signatures for a group of additional objects represented in the 3D scene; ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system.”)
determining one of the additional signatures, for a respective additional object, is similar to the signature according to at least one similarity metric; ([0041], “After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system.”) and
replacing at least one of the object or the respective additional object with a reference object. ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system. If a second object is identified that has a signature equal to the signature of the object, then the exemplary method 80 branches at 88 and involves indexing 90 the object in the object index as a reference to the second object.”)
computing a signature for an object, represented in a three-dimensional (3D) scene, based on input properties corresponding to an appearance of the object; additional objects represented in the 3D scene (page 552, Section A: “The statistical notation of covariance is a measure of how several random variables change together and captures the intrinsic correlation between sampling distributions of the involved cues. In the context of a descriptor definition, the observed random variables are related to the set of observable features which can be extracted from points and their close localities in the scene, e.g. pixel color values, 3D coordinates, first or second order derivatives, etc….
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Li teaches reducing memory saving redundancy by deciding if objects are similar to one another by comparing objects signatures. Cirujeda teaches a specific method of generating signatures of objects.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Li with the specific signature generation method of Cirujeda to accurately decide similarities among objects.
Regarding claim 10, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, wherein the signature is a 3D vector. (Cirujeda page 553, upper left: “
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The combination rationale of claim 9 is incorporated here.
Regarding claim 11, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, wherein the signature is based on at least one of one or more object vertex positions, one or more object vertex normals, color, or one or more vertex texture coordinates. (Cirujeda page 552, section A: “The statistical notation of covariance is a measure of how several random variables change together and captures the intrinsic correlation between sampling distributions of the involved cues. In the context of a descriptor definition, the observed random variables are related to the set of observable features which can be extracted from points and their close localities in the scene, e.g. pixel color values, 3D coordinates, first or second order derivatives,” The combination rationale of claim 9 is incorporated here.)
Regarding claim 12, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, further comprising: storing the signature and the plurality of additional signatures in a space-partitioning data structure; (Li [0041], “After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system. If a second object is identified that has a signature equal to the signature of the object, then the exemplary method 80 branches at 88 and involves indexing 90 the object in the object index as a reference to the second object. However, if the computer system fails to identify a second object having a signature equal to the signature of the object, the exemplary method 80 branches at 88 and involves storing 92 the object in the object system and indexing 94 the object in the object index as a reference to the object” FIG. 2)
and computing a distance between the signature and individual additional signatures of the plurality of additional signatures. ( Cirujeda page 553, left bottom: “
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Li teaches determining if one object is redundant by comparing the signature similarity. Cirujeda teaches using covariance matrices to represent object signature and deciding objects similarity based on matrix distance.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Li with the specific signature generation method of Cirujeda to accurately decide similarities among objects.
Regarding claim 13, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, wherein the object is represented by a mesh or a point cloud. (Cirujeda, Abstract: “—In this paper we propose MCOV, a covariance based descriptor for the fusion of shape and color information of 3D surfaces with associated texture aiming at a robust” The combination rationale of claim 9 is incorporated here.)
Regarding claim 14, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, wherein the signature is computed by a singular value decomposition.( Cirujeda page 552, right: “
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The combination rationale of claim 9 is incorporated here.)
Regarding claim 15, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, further comprising: determining a rotation for the object; (Cirujeda, page 552, upper right: “
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and storing the rotation for rendering the object within the 3D scene. (Li teaches storing signature information in memory. [0041])
The combination rationale of claim 9 is incorporated here.
Regarding claim 16, Li in view of Cirujeda teaches:
The computer-implemented method of claim 9, further comprising: and storing the centroid for rendering the object within the 3D scene. (Li teaches storing signature information in memory.[0041]) determining a centroid for the object; (Cirujeda “page 552, right: “
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The combination rationale of claim 9 is incorporated here.
Regarding claim 17, Li teaches:
A system, comprising: one or more processing units ([0069])
to determine two or more objects … are within a threshold similarity based on respective signatures corresponding to the two or more objects ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system.”) and to cause the two or more objects to be stored as a common reference representation. ([0041], “The exemplary method 80 of FIG. 4 begins at 82 and involves generating 84 a signature of the object. The signature comprises a value indicating the contents of the object, and may be compared with the signature of another object to determine whether the objects are identical. After generating 84 the signature of the object, the exemplary method 80 involves comparing 86 the signature of the object with the signatures of other objects in the object system. If a second object is identified that has a signature equal to the signature of the object, then the exemplary method 80 branches at 88 and involves indexing 90 the object in the object index as a reference to the second object.”)
However, Li does not, but Cirujeda teaches:
two or more objects within a three-dimensional (3D) scene (page 552, left: “The method proposed in the present paper is focused on the combination of the visible and the 3D information in an implicit fusion and correlation analysis way, which is provided by means of the statistical concept of covarianc”)
Li teaches determining if one object is redundant by comparing the signature similarity. Cirujeda teaches using covariance matrices to represent object signature for 3D objects.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have applied the method of Li to the 3D objects of Cirujeda and combined with the signature generation method of Cirujeda to accurately decide similarities among 3D objects.
Claim 18, 20 recites similar limitations of claim 11 and 8 respectively, thus is rejected accordingly.
Regarding claim 19, Li in view of Cirujeda teaches:
The system of claim 17, wherein the threshold similarity is based on a distance between respective vectors associated with the respective signatures. ( Cirujeda page 553, Section B: “
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Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Cirujeda and further in view of Brown et al. (US 2025/0218132 A1).
Regarding claim 7, Li in view of Cirujeda teaches:
The at least one processor of claim 1, wherein the one or more circuits are further to:
select the one or more features; and cause the covariance matrix to be determined using the one or more features.( Cirujeda page 552, section A: “
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However, Li in view of Cirujeda does not, but Brown teaches:
train one or more neural networks, based on a plurality of scenes including a plurality of objects, to identify one or more features representative of the object; ([0031], “In some embodiments, after the pre-processing and segmentation are complete, the feature extraction component 215 may apply trained machine learning (ML) models to process the data and extract relevant features 220 for creating a mapping of the pet. In some embodiments, the ML models may include convolutional neural networks that are trained to identify and extract features of an object (e.g., a pet or a pet-related item) from its associated visual data (e.g., images, videos). The extracted features 220 may represent the distinctive characteristics of the pet as illustrated in the visual data 205, including but not limited to the pet's physical attributes (e.g., color, weight, height, texture of fur or skin),”)
Li in view of Cirujeda teaches identifying object features. Brown teaches using a trained model to do that.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Li in view of Cirujeda with the specific teachings of Brown to efficiently and accurately identifying object features.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YANNA WU whose telephone number is (571)270-0725. The examiner can normally be reached Monday-Thursday 8:00-5:30 ET.
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/YANNA WU/Primary Examiner, Art Unit 2615
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