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 .
This action is in response to communications filed on 06/10/2026.
Claim 19 has been canceled.
Claim 21 had been added.
Claims 1-18 and 20-21 are pending and have been examined.
Claim Objections
Claims 6, 9, and 13 are objected to because of the following informalities:
As per claim 6, it appears that the word “and” should be inserted after “space,” in line 3. This similarly applies to claims 9 and 13.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 and 20-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 is amended to recite “the distance threshold… is determined prior to any determination of distance values involving the evaluation embedding space”. However, the specification does not support the above features. For example, paragraph 40 states that “First, the sub-flow 551 that determines the distance threshold will be described. Specifically, in Figure 4, a plurality of views of the reference embedding space are obtained… the distance threshold is determined using the plurality of views of the reference embedding space” and paragraph 42 states “Next, the sub-flow 552 that determines the distance (as one example, an energy distance) between the embedding spaces will be described. Referring to Figure 4, a distance is determined between the evaluation embedding space and the reference embedding space”, but this merely describes the order of the describing of sub-flows, not the actual order of operation of the sub-flows. As can be seen in figure 4, each of the determinations are shown in parallel with determination of distance values including the evaluation embedding space labeled 403 while determining the distance threshold is labeled 405. As can be seen in figure 5, each of the determinations are shown in parallel. As such, it is not described or clear which occurs before the other or if they happen at the same time. As such, the claim lacks written description. Independent claims 8 and 20 also recite the same limitations and therefore have the same problem. Due at least to their dependency upon claims 1, 8, or 20, dependent claims 2-7, 9-18, and 21 also lack written description.
Further claim 21 recites “wherein determining the distance threshold further comprises: identifying a perturbation level associated with a threshold condition by evaluating performance values across multiple perturbation levels applied to the reference embedding space; generating a plurality of perturbed versions of the reference embedding space using the identified perturbation level”. However, the specification does not support the above features. For example, the specification describes “use the view(s) with highest perturbation level that meet the performance threshold to determine maximum distance that does not indicate a distribution shift. The distance from the view(s) with highest perturbation level still meeting performance criteria to the reference embedding space is computed” (e.g. in paragraph 42). It is silent as to “evaluating performance values across multiple perturbation levels”. It is also silent as to “generating a plurality of perturbed versions of the reference embedding space using the identified perturbation level” that is generated by “evaluating performance values across multiple perturbation levels applied to the reference embedding space”. As such, the claim lacks written description.
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 1-18 and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a system, method, and product to access, obtain, determine, obtain, determine, compare, and determine.
The limitations “access … obtain… determine… obtain… determine… compare… determine…” as recited in claim 1 is a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “one or more processors” and “hardware storage devices”, “access a reference embedding space generated by applying an embedding model to a reference dataset, wherein…” in the context of the claim encompasses the user making calculations to arrive at a result. “obtain a plurality of views of the reference embedding space” in the context of the claim encompasses the user manipulating information. “determine a distance threshold for a distance metric using the plurality of views of the reference embedding space, wherein:…” in the context of the claim encompasses the user making determinations and calculations. “obtain an evaluation embedding space generated by applying the embedding model to an evaluation dataset, wherein…” in the context of the claim encompasses the user making calculations. “determine a distance value representing a distance between the evaluation embedding space and the reference embedding space” in the context of the claim encompasses the user making calculations. “compare the distance value with the distance threshold” in the context of the claim encompasses the user making an observation. “based on the comparison, determine a level of fitness of the embedding model for the evaluation dataset” in the context of the claim encompasses the user making a judgement. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements. The claim recites “one or more processors” and “hardware storage devices” as noted above. However, this is recited at a high level of generality such that it amounts to no more than merely instructions to apply the exception to a generic computer component (see MPEP 2106.05(f)). It is noted that “embedding model” broadly includes a function(s) or equation(s), which can be applied by mental steps and/or pen and paper. At best, it merely amounts to no more than merely instructions to apply the exception to a generic computer component (see MPEP 2106.05(f)) and/or amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than applying the judicial exception to a generic computer component and/or field of use. Therefore, the claims are not patent eligible.
Claims 8 and 20 also recite similar claim language as claim 1, and thus have the same issues. With respect to claim 8, the claim does not include any additional elements. With respect to claim 20, the claim further includes “one or more hardware storage devices”. Similar to the above, the “one or more hardware storage devices” is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements also do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to more than the judicial exception.
Regarding claim 2, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes views, which are part of the mental steps and do not include any additional elements. This similarly applies to claim 9.
Regarding claim 3, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the distance value, which is part of the mental steps and do not include any additional elements. This similarly applies to claim 10.
Regarding claim 4, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes determining of the distance threshold, which is part of the mental steps and do not include any additional elements. This similarly applies to claim 11.
Regarding claim 5, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the performance criteria, which is part of the mental steps and do not include any additional elements. This similarly applies to claim 12.
Regarding claim 6, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes views, which are part of the mental steps and do not include any additional elements. This similarly applies to claim 13.
Regarding claim 7, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the reference dataset, which is part of the mental steps and do not include any additional elements. This similarly applies to claim 14.
Regarding claim 15, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the level of fitness, which is part of the mental steps and do not include any additional elements.
Regarding claim 16, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the obtaining, which is part of the mental steps, and describes performing by the computing system which is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). This similarly applies to claim 17.
Regarding claim 18, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the spaces, which is part of the mental steps and do not include any additional elements.
Regarding claim 21, the claim does not include any additional elements that integrate the abstract idea into a practical application or that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes identifying, generating computing and determining, which are mental steps (encompassing a user performing evaluations and calculations) and do not include any additional elements.
Response to Arguments
Previous objections to the claims not included in this action have been withdrawn in view of amendments.
Previous rejections under 35 USC 112 have been withdrawn in view of amendments.
Applicant’s arguments with respect to the prior art have been considered but are moot in view of new grounds of rejection. See Nagato et al. (US 20180322364 A1) below.
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.
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.
Claims 1, 6-8, 13-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 20200311557 A1) in view of Nagato et al. (US 20180322364 A1), and Ratnesh Kumar et al. (US 20220392234 A1).
As per independent claim 1, Jin teaches a computing system that evaluates a fit of an embedding model for an evaluation dataset (e.g. in paragraph 3, “a target data acceptability component that determines whether application of the target neural network model to the target data set will generate results with an acceptable level of accuracy based on the degree of correspondence”), said computing system comprising:
one or more processors and hardware storage devices that store instructions that are executable by the one or more processors to cause the computing system to (e.g. in paragraph 3, “a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory”):
access a reference embedding space generated by applying an embedding model to a reference dataset (e.g. in paragraphs 23, 28, 42, 50, and 56, “training data set (also referred to herein as the reference data)… subset of layers of the DNN model (e.g., one or more layers of the DNN model [i.e. embedding model] excluding the final output layer) can be applied to the training data set to generate a first set of model-based features based on the training data set……extracted as second training data features… first training data features 304 (e.g., feature vectors) can be extracted from the training data set 124”, i.e. reference embedding space);
obtain a plurality of views of the reference embedding space (e.g. in paragraphs 23, 28, 42, 50, and 56, “generate a first set of model-based features based on the training data… a subset of the first set of model-based features can be extracted as second training data features… Although the visualization exemplified in FIG. 2 depicts…two nodes Z are used to represent the feature vectors from eight input images, it should be appreciated that the dimensionality of the input data set and the resulting extracted feature vectors can vary… first training data features 304 (e.g., feature vectors) can be extracted from the training data set”, i.e. views, and figure 2);
obtain an evaluation embedding space generated by applying the embedding model to an evaluation dataset (e.g. in paragraphs 28, 42, 50, and 57, “The same subset of layers of the DNN model can also be applied to the target data set to generate a second set of model-based features. This second set of model-based features or a subset of the second set of model-based features can be extracted as second target data feature… first target data features 308 (e.g., feature vectors) can also be extracted from the target data set 126”, i.e. evaluation embedding space, used to determine acceptability as seen below);
determine a distance value representing a distance between the evaluation embedding space and the reference embedding space (e.g. in paragraphs 28, 38, 51 and 58-59, “generate a first set of model-based features based on the training data… a subset of the first set of model-based features can be extracted as second training data features [i.e. view]… set of features or feature vectors extracted from the training data set 124 are referred to herein as first training data features or first training data feature vectors [i.e. view]… first degree of correspondence can then be determined between the first training data features 304 and the first target data features 308…employ one or more statistical and/or machine learning based approaches (e.g.,…Mahalanobis distance analysis on multi-dimensions, t-SNE analysis on lower dimensions to get similarity distances…) to…determine a degree of correspondence between the first training data features 304 and the first target data features… determine a degree of correspondence between the second training data features/feature vectors and the second target data features/feature vectors… distance measurement”);
compare the distance value with a distance threshold and based on the comparison, determine a level of fitness of the embedding model for the evaluation dataset (e.g. in paragraphs 28-29 52-53, 58, and 62, “within the scope of the DNN model… outside the scope of DNN model… determine whether a measurement value representative of the degree of correspondence meets an acceptability criterion (e.g., a minimum threshold… determined to be acceptable… determined to be unacceptable… in some implementations, the confidence score can be a binary value, representative of acceptable (e.g., within the scope of the training data set 124) or unacceptable (e.g., outside the scope of the training data set 124). In another embodiment, the confidence score can correspond to the degree of correspondence, such that the higher the degree of correspondence, the higher the confidence score… results generated based on application of the DNN model to the target data set can be associated with a high degree of accuracy (e.g., in accordance with a predefined accuracy scale) that reflects the first and/or second degree of correspondence”),
but does not specifically teach determine a distance threshold for a distance metric using the plurality of views of the reference embedding space, wherein: the plurality of views include one or more perturbed versions of the reference embedding space, the distance threshold is based on distances between the reference embedding space and the one or more perturbed versions of the reference embedding space, and the distance threshold is defined independently of the evaluation embedding space and is based on distances between the reference embedding space and the one or more perturbed versions of the reference embedding space, and is determined prior to any determination of distance values involving the evaluation embedding space; and wherein the embedding model is configured to structure the reference embedding space to have a first number of dimensions, wherein the embedding model is further configured to structure the evaluation embedding space to have a second number of dimensions, the second number of dimensions being the same as the first number of dimensions such that the reference embedding space and the evaluation embedding space are structured, by the embedding model, to have a same dimension size, and wherein dimensions of the evaluation embedding space are structured, by the embedding model, to correspond to dimensions of the reference embedding space.
However, Nagato teaches determining a distance threshold for a distance metric using a plurality of views of a reference embedding space, wherein the plurality of views include one or more perturbed versions of the reference embedding space and the distance threshold is based on distances between a reference embedding space and one or more perturbed versions of the reference embedding space and the distance threshold is defined independently of an evaluation embedding space and is based on distances between the reference embedding space and the one or more perturbed versions of the reference embedding space and is determined prior to any determination of distance values involving the evaluation embedding space (e.g. in paragraphs 30, 33, 40, and 116-117, “in the case where node feature amounts are calculated by different methods for respective kinds of partial programs [i.e. one or more perturbed versions] which are incorporated in intermediate nodes as illustrated in FIG. 6… the image processing program 2 has to be regenerated at the time when it is determined that image processing precision is low based on a final processing result… calculates a numeral value representing a variation degree of n sets of feature amounts as the threshold value TH… reference information calculation unit 123 calculates a distance between each of the n sets of feature amounts and the initial feature amount F0, for example” and figures 11, 13, and 16-17 showing independent and prior to evaluation). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Jin to include the teachings of Nagato because one of ordinary skill in the art would have recognized the benefit of enabling precision assessment,
but does not specifically teach wherein the embedding model is configured to structure the reference embedding space to have a first number of dimensions, wherein the embedding model is further configured to structure the evaluation embedding space to have a second number of dimensions, the second number of dimensions being the same as the first number of dimensions such that the reference embedding space and the evaluation embedding space are structured, by the embedding model, to have a same dimension size, and wherein dimensions of the evaluation embedding space are structured, by the embedding model, to correspond to dimensions of the reference embedding space.
However, Ratnesh Kumar teaches an embedding model being configured to structure an embedding space to have a first number of dimensions, wherein the embedding model is further configured to structure another embedding space to have a second number of dimensions, the second number of dimensions being the same as the first number of dimensions such that the embedding space and the another embedding space are structured, by the embedding model, to have a same dimension size, and wherein dimensions of the another embedding space are structured, by the embedding model, to correspond to dimensions of the embedding space (e.g. in paragraphs 24, 38-39, 46-47, and 61, “an instantiation of the DNN… a width, W, a height, H, and color channels, C… and… a batch size, B… DNN 108 may be trained to compute the embeddings 110 with an embedding dimension… the DNN 108 may be trained to compute the embeddings 110 with an embedding dimension of 128 units while producing accurate and efficient results… generate the embedding 110A… generate the embedding 110B”, i.e. embedding model comprising DNN 108 generates multiple embedding spaces with corresponding dimensions at size of 128). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Ratnesh Kumar because one of ordinary skill in the art would have recognized the benefit of producing accurate and efficient results.
As per claim 6, the rejection of claim 1 is incorporated and the combination further teaches a first view of the plurality of views of the reference embedding space being a first subsample of the reference embedding space, a second view of the plurality of views of the reference embedding space representing a second subsample of the reference embedding space (e.g. Jin, in paragraph 28, “generate a first set of model-based features based on the training data… a subset of the first set of model-based features can be extracted as second training data features”; Ratnesh Kumar, in paragraphs 7, 29, and 49-50, “determine the meaningful sample(s)… determine a subset of the samples from each batch that are to be used by an optimizer to increase the ability of the DNN to learn effectively and converge more quickly to an acceptable or optimal accuracy… embeddings 110 computed by the DNN 108 for a batch of the image data 102 may be sampled using a batch sampling variant during batch sampling… sample from different views… various batch sampling variants may be used”).
As per claim 7, the rejection of claim 1 is incorporated and the combination further teaches the reference dataset comprising a training dataset (e.g. Jin, in paragraph 23, “training data set (also referred to herein as the reference data)”).
Claims 8 and 13-14 are the method claim corresponding to systems claim 1 and 6-7, and are rejected under the same reasons set forth.
As per claim 15, the rejection of claim 8 is incorporated and the combination further teaches the level of fitness comprising whether or not the embedding model is acceptable for use with the evaluation dataset (e.g. Jin, in paragraphs 4, 28-29, and 52-53, “determine whether the application of the target neural network model to the target data set will generate the results with the acceptable level of accuracy… within the scope of the DNN model… outside the scope of DNN model”).
As per claim 16, the rejection of claim 8 is incorporated and the combination further teaches wherein obtaining the evaluation embedding space is performed by the computing system applying the embedding model to the evaluation dataset (e.g. Jin, in paragraphs 28, 42, 50, and 57, “The same subset of layers of the DNN model can also be applied to the target data set to generate a second set of model-based features. This second set of model-based features or a subset of the second set of model-based features can be extracted as second target data feature… first target data features 308 (e.g., feature vectors) can also be extracted from the target data set 126”).
As per claim 17, the rejection of claim 8 is incorporated and the combination further teaches wherein obtaining the reference embedding space is performed by the computing system applying the embedding model to the reference dataset (e.g. Jin, in paragraphs 23, 28, 42, 50, and 56, “training data set (also referred to herein as the reference data)… subset of layers of the DNN model (e.g., one or more layers of the DNN model [i.e. embedding model] excluding the final output layer) can be applied to the training data set to generate a first set of model-based features based on the training data set”).
As per claim 18, the rejection of claim 8 is incorporated and the combination further teaches the reference embedding space and the evaluation embedding space each having greater than three dimensions (e.g. Jin, in paragraphs 23, 28 and 42, “a subset of layers of the DNN model (e.g., one or more layers of the DNN model excluding the final output layer) can be applied to the training data set to generate a first set of model-based features based on the training data set. This first set of model-based features or a subset of the first set of model-based features can be extracted as second training data features. The same subset of layers of the DNN model can also be applied to the target data set to generate a second set of model-based features. This second set of model-based features or a subset of the second set of model-based features can be extracted as second target data features… Although the visualization exemplified in FIG. 2 depicts…two nodes Z are used to represent the feature vectors from eight input images, it should be appreciated that the dimensionality of the input data set and the resulting extracted feature vectors can vary” and figure 2 showing layers with more than 3 nodes; Ratnesh Kumar, in paragraphs 38 and 47, “a width, W, a height, H, and color channels, C… and… a batch size, B… embedding dimension of 128 units”).
Claim 20 is the product claim corresponding to system claim 1, and is rejected under the same reasons set forth and the combination further teaches one or more hardware storage devices that store instructions that are executable by the one or more processors (e.g. Jin, in paragraph 3, “a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory”).
As per claim 21, the rejection of claim 20 is incorporated and the combination further teaches wherein determining the distance threshold further comprises: identifying a perturbation level associated with a threshold condition by evaluating performance values across multiple perturbation levels applied to the reference embedding space (e.g. Nagato, in paragraphs 74-84 and 130-132, “In the case where a partial program is a Sobel (primary differential) filter or a Laplace (secondary differential) filter, a node feature amount is calculated as the maximum value or the minimum value or both of these of luminance in an intermediate image… m pieces of node feature amounts are included in the feature amount F… calculates a distance D1 between the calculated feature amount F and the initial feature amount F0… distance D1 represents a performance evaluation value of an image processing program… compares the calculated distance D1 with the threshold value TH”); generating a plurality of perturbed versions of the reference embedding space using the identified perturbation level (e.g. Nagato, in paragraphs 74-84, “In the case where a partial program is a Sobel (primary differential) filter or a Laplace (secondary differential) filter, a node feature amount is calculated as the maximum value or the minimum value or both of these of luminance in an intermediate image”); computing a plurality of distances between the reference embedding space and the plurality of perturbed versions of the reference embedding space (e.g. Nagato, in paragraph 117, “calculates a distance between each of the n sets of feature amounts and the initial feature amount F0”); and determining the distance threshold as an aggregate statistical value of the plurality of distances (e.g. Nagato, in paragraph 117, “reference information calculation unit 123 calculates an average value AVE of n pieces of calculated distances and standard deviation σ”), wherein comparing the distance value with the distance threshold comprises determining whether the distance value is less than the distance threshold (e.g. Nagato, in paragraph 132, “in the case where the distance D1 is equal to or smaller than the threshold value TH, the determination processing unit 125 determines t”).
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 20200311557 A1) in view of Nagato et al. (US 20180322364 A1), and Ratnesh Kumar et al. (US 20220392234 A1) and further in view of Tan et al. (US 20200227030 A1).
As per claim 2, the rejection of claim 1 is incorporated, but the combination does not specifically teach, as a whole, a first view of the plurality of views of the reference embedding space being a sub sample of or an entirety of the reference embedding space, and a second view of the plurality of views of the reference embedding space representing a perturbation of the first view of the reference embedding space. However, Tan teaches a first view of a plurality of views of a reference embedding space being a sub sample of or an entirety of the reference embedding space and a second view of the plurality of views of the reference embedding space representing a perturbation of the first view of the reference embedding space (e.g. in paragraphs 25 and 38, “training data… perturbation… the original intent model (158) is augmented with synthetic data and subject to adversarial training [i.e. perturbation]… One or more thresholds may be applied to narrow the set of synthetic data. For example, in one embodiment, a first threshold is applied with respect to sampling synthetic data, and a second threshold is applied to a second subset within the sampling of the applied first threshold… optimizing the worst synthetic data within the sample”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Tan because one of ordinary skill in the art would have recognized the benefit of improving functionality of a model.
Claim 9 is the method claim corresponding to system claim 2, and is rejected under the same reasons set forth.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 20200311557 A1) in view of Nagato et al. (US 20180322364 A1), and Ratnesh Kumar et al. (US 20220392234 A1) and further in view of Gan et al. (US 20210089872 A1).
As per claim 3, the rejection of claim 1 is incorporated, but the combination does not specifically teach the distance value being a distribution shift value between the reference embedding space and the evaluation embedding space. However, Gan teaches a distance value being a distribution shift value between a reference embedding space and an evaluation embedding space (e.g. in paragraphs 53 and 126-127, “assumption that the data distributions of training datasets and deployment (e.g. testing) datasets are the same is not always valid… variability between training and operational (or test) data distributions [i.e. embedding spaces]… This shift in data distribution between training domains and testing/deployment domains is sometimes referred to as “domain shift”… Wasserstein Distance is used as a metric to compute the distances between two distributions”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Gan because one of ordinary skill in the art would have recognized the benefit of determining variability between spaces.
Claim 10 is the method claim corresponding to system claim 3, and is rejected under the same reasons set forth.
Claims 4-5 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Jin Jin et al. (US 20200311557 A1) in view of Nagato et al. (US 20180322364 A1), and Ratnesh Kumar et al. (US 20220392234 A1) and further in view of Banville et al. (US 20100014741 A1).
As per claim 4, the rejection of claim 1 is incorporated and the combination further teaches wherein determining the distance threshold is based on computing a value of an aggregate statistic of the distance metric for the plurality of views of the reference embedding space (e.g. Jin, in paragraphs 23, 28, 42, 50, and 56, “generate a first set of model-based features based on the training data…extracted as second training data features… Although the visualization exemplified in FIG. 2 depicts…two nodes Z are used to represent the feature vectors from eight input images, it should be appreciated that the dimensionality of the input data set and the resulting extracted feature vectors can vary… first training data features 304 (e.g., feature vectors) can be extracted from the training data set” and figure 2; Nagato, in paragraphs 116-117, “calculates a numeral value representing a variation degree of n sets of feature amounts as the threshold value TH… calculates an average value AVE of n pieces of calculated distances and standard deviation σ”), but does not specifically teach generated using a highest perturbation level that satisfies a user-specified performance criteria. However, Banville teaches generating a space using a highest perturbation level that satisfies a user-specified performance criteria (e.g. in paragraphs 23 and 61, “a gate boundary (and/or perturbations thereof) can be defined based on one or more limits… limits can be referred to as a boundary. In some embodiments, processing at the gating module can be performed, for example, based on one or more conditions (e.g., threshold values within a condition) and…based on one or more user preferences (e.g., a customizable user preference)”, i.e. highest). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Banville because one of ordinary skill in the art would have recognized the benefit of facilitating user preferences.
As per claim 5, the rejection of claim 4 is incorporated and the combination further teaches wherein the user-specified performance criteria is a value of a function that decreases as the distance metric increases (e.g. Jin, in paragraphs 51-52, “Mahalanobis distance analysis on multi-dimensions, t-SNE analysis on lower dimensions to get similarity distances… determine whether a measurement value representative of the degree of correspondence meets an acceptability criterion (e.g., a minimum threshold”; Luo, in paragraphs 38-39, “similarity… in general, are small and close to each other, which would be anticipated since they represent the same type object and appear relatively similar to the average shape. In contrast, the distances of shapes in the dissimilar shape group (84) present a large variation”, i.e. value of similarity function is smaller, i.e. decreases, when distance is larger, i.e. increases; Banville, in paragraphs 23 and 61, “based on one or more conditions (e.g., threshold values within a condition) and…based on one or more user preferences”).
Claims 11-12 are the method claims corresponding to system claims 4-5, and are rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Makadia et al. (US 20120109858 A1) teaches “acceptability of the training of the model may be evaluated by seeing how close the trained model comes to providing the correct ranking of the resources for the annotation pair… approach in the Weston paper involves training on an "embedding space" representation of arbitrary dimension, where distance between two items in the space denotes their similarity” (e.g. in paragraphs 42 and 51).
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.
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/W.W/Examiner, Art Unit 2144 08/26/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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