DETAILED ACTION
This Office Action is sent in response to the Applicant’s Communication received on 06/13/2023 for application number 18/209,024. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, IDS, and Claims.
Claims 1-20 are pending.
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 an abstract idea without significantly more.
Claims 1-10 are directed to a method. Claims 11-20 are directed to a non-transitory computer-readable media. Therefore, all claims are directed to one of the four categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1:
Claim 1 recites:
“A method comprising: detecting a plurality of distinct multidimensional points in an original plurality of multidimensional points that contains duplicates;” Detecting a plurality of distinct multidimensional points is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“increasing, based on duplicates in the original plurality of multidimensional points, a respective observed frequency of each distinct multidimensional point in the plurality of distinct multidimensional points;” Increasing a respective observed frequency is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“generating, based on a distinct multidimensional point of the plurality of distinct multidimensional points, a reconstruction of the distinct multidimensional point [by a reconstructive model];” Generating a reconstruction of the distinct multidimensional point is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“increasing, based on said increasing said observed frequency of the distinct multidimensional point, a scaled error of the reconstruction of the distinct multidimensional point;” Increasing a scaled error of the reconstruction is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the method is performed by one or more computers;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“by a reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
“increasing, based on the scaled error of the reconstruction of the distinct multidimensional point, accuracy of the reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the method is performed by one or more computers;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
“by a reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
“increasing, based on the scaled error of the reconstruction of the distinct multidimensional point, accuracy of the reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 2
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“training the reconstructive model with a training corpus that consists of the plurality of distinct multidimensional points;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“training the reconstructive model with a training corpus that consists of the plurality of distinct multidimensional points;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 2A Prong 1:
Claim 3 recites:
“generating a batch that represents more multidimensional points than the batch contains;” Generating a batch that represents more multidimensional points than the batch contains is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two and Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible.
Claim 4
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein each multidimensional point in the original plurality of multidimensional points represents a respective textual command;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein each multidimensional point in the original plurality of multidimensional points represents a respective textual command;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein said detecting the plurality of distinct multidimensional points comprises normalization of whitespace or decapitalization of letters;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein said detecting the plurality of distinct multidimensional points comprises normalization of whitespace or decapitalization of letters;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein said detecting the plurality of distinct multidimensional points comprises decreasing a numeric precision;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein said detecting the plurality of distinct multidimensional points comprises decreasing a numeric precision;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein: the textual commands represented by the original plurality of multidimensional points are database statements; said accuracy of the reconstructive model comprises anomaly detection accuracy;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein: the textual commands represented by the original plurality of multidimensional points are database statements; said accuracy of the reconstructive model comprises anomaly detection accuracy;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the original plurality of multidimensional points contains at least a hundred times as many multidimensional points as the plurality of distinct multidimensional points;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein the original plurality of multidimensional points contains at least a hundred times as many multidimensional points as the plurality of distinct multidimensional points;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein said increasing the accuracy of the reconstructive model comprises applying stochastic gradient descent to a denoising autoencoder;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“wherein said increasing the accuracy of the reconstructive model comprises applying stochastic gradient descent to a denoising autoencoder;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“the reconstructive model inferring without using a distance measurement;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“the reconstructive model inferring without using a distance measurement;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 11
Step 2A Prong 1:
Claim 11 recites:
“[One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors], cause: detecting a plurality of distinct multidimensional points in an original plurality of multidimensional points that contains duplicates;” Detecting a plurality of distinct multidimensional points is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“increasing, based on duplicates in the original plurality of multidimensional points, a respective observed frequency of each distinct multidimensional point in the plurality of distinct multidimensional points;” Increasing a respective observed frequency is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“generating, based on a distinct multidimensional point of the plurality of distinct multidimensional points, a reconstruction of the distinct multidimensional point [by a reconstructive model];” Generating a reconstruction of the distinct multidimensional point is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
“increasing, based on said increasing said observed frequency of the distinct multidimensional point, a scaled error of the reconstruction of the distinct multidimensional point;” Increasing a scaled error of the reconstruction is an action that can be performed mentally with the aid of pen and paper, and is therefore a mental process.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional elements are as follows:
“One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“by a reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
“increasing, based on the scaled error of the reconstruction of the distinct multidimensional point, accuracy of the reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
“One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors;” Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) which cannot provide an inventive concept.
“by a reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
“increasing, based on the scaled error of the reconstruction of the distinct multidimensional point, accuracy of the reconstructive model;” The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)) and cannot provide an inventive concept.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claims 12-20 are non-transitory computer-readable media claims that recites identical limitations to method claims 2-10. Therefore, claims 12-20 are rejected using the same rationale as claims 2-10.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (CN111694802A, see attached translation), hereinafter Yin, in view of Lin et al. (US 20210073644 A1), hereinafter Lin.
Regarding claim 1, Yin teaches,
A method comprising: detecting a plurality of distinct multidimensional points in an original plurality of multidimensional points that contains duplicates; increasing, based on duplicates in the original plurality of multidimensional points, a respective observed frequency (Para 0010, count the number of times each feature value appears in the n feature values in the deduplicated dataset) of each distinct multidimensional point in the plurality of distinct multidimensional points [Para 0009, The initial dataset is sampled to obtain a sampled dataset. The initial dataset includes N feature values belonging to the same attribute. The sampled dataset includes n feature values from the N feature values, where n is an integer less than N; Para 0010, Perform a deduplication operation on the n feature values to obtain a deduplicated dataset, and count the number of times each feature value appears in the n feature values in the deduplicated dataset];
generating, based on a distinct multidimensional point of the plurality of distinct multidimensional points, a reconstruction of the distinct multidimensional point by a reconstructive model (Para 0029, deduplication module) [Para 0012, a deduplication dataset is obtained by performing deduplication operations on the feature values in the sampled dataset; Para 0032, The deduplication module is used to perform deduplication on the n feature values to obtain a deduplicated dataset, and to count the number of times each feature value in the deduplicated dataset appears among the n feature values];
wherein the method is performed by one or more computers [Paras 0046-0049, this application provides an electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the steps of the deduplication information acquisition method provided in this application].
Yin does not teach increasing, based on increasing frequency of distinct point, a scaled error of reconstruction of the distinct point; increasing, based on the scaled error of the reconstruction of the distinct point, accuracy of the reconstructive model.
Lin teaches,
increasing, based on increasing frequency of distinct point, a scaled error (Para 0055, tunable weights) of reconstruction of the distinct point [Para 0055, each of the convolutional filters includes a certain number of tunable weights. As the number of channels of each layer or branch increases, the number of weights grows accordingly; Para 0060, Using a layer including a set of filters (over one or multiple channels) as an example, the local features of the set of filters can be preserved by generating a copy of the set of filters (referred to as a duplicate set of filters), and using the original set of filters as supervision (e.g., by keeping the original set of filters fixed) as the duplicate set of filters are updated and as one or more candidate filters are removed from the duplicate set of filters… the loss minimization engine 312 can minimize a loss function (e.g., including an error function, such as mean squared error, and the penalty applied by the penalty engine 314) to maintain the outputs of the original set of filters as weights, scaling factors, and/or other parameters of the duplicate filters are updated and as the candidate filters or parameters (e.g., weights) of the candidate filters are removed from the duplicate set of filters];
increasing, based on the scaled error of the reconstruction of the distinct point, accuracy of reconstructive model (Para 0084, in order to minimize (or optimize) the loss function) [Para 0060, The candidate filters can be determined and removed from the complex layers or branches of the trained neural network 302 in a way that preserves the local features of the trained neural network 302, ensuring that the compressed versions of the complex layers or branches output features that are similar to the features output from the original (uncompressed) layers or branches. Using a layer including a set of filters (over one or multiple channels) as an example, the local features of the set of filters can be preserved by generating a copy of the set of filters (referred to as a duplicate set of filters), and using the original set of filters as supervision (e.g., by keeping the original set of filters fixed) as the duplicate set of filters are updated and as one or more candidate filters are removed from the duplicate set of filters. For example, as described below, the loss minimization engine 312 can minimize a loss function (e.g., including an error function, such as mean squared error, and the penalty applied by the penalty engine 314) to maintain the outputs of the original set of filters as weights, scaling factors, and/or other parameters of the duplicate filters are updated and as the candidate filters or parameters (e.g., weights) of the candidate filters are removed from the duplicate set of filters; Para 0083, At block 610, the process 600 includes minimizing a loss function of an error between the output of the set of filters and the output of the duplicate set of filters and a penalty applied to one or more scaling factors associated with the duplicate set of filters; Para 0084, the process 600 can include minimizing the loss function by iteratively determining the error between the output of the set of filters and the output of the duplicate set of filters and the penalty applied to the one or more scaling factors associated with the duplicate set of filters… as described above with reference to Equation (3), various iterations of the loss function can be performed in order to minimize (or optimize) the loss function.].
Lin is analogous to the claimed invention as they both relate to data deduplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Lin and provide a scaled error in order to tune model weights for improved accuracy.
Regarding claim 2, Yin-Lin teach the limitations of claim 1.
Lin further teaches,
training reconstructive model with a training corpus that consists of plurality of distinct multidimensional points [Para 0060, duplicate filters are updated and as the candidate filters or parameters (e.g., weights) of the candidate filters are removed from the duplicate set of filters; Para 0084, the process 600 can include minimizing the loss function by iteratively determining the error between the output of the set of filters and the output of the duplicate set of filters and the penalty applied to the one or more scaling factors associated with the duplicate set of filters… as described above with reference to Equation (3), various iterations of the loss function can be performed in order to minimize (or optimize) the loss function].
Lin is analogous to the claimed invention as they both relate to data deduplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Lin and provide training reconstructive model [Lin, para 0032] in order to tune the model’s accuracy.
Regarding claim 11, Yin teaches,
One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors [Para 0050, this application provides a non-transitory computer-readable storage medium storing computer instructions, the computer instructions being used to cause the computer to execute the steps of the deduplication information acquisition method provided in this application; Para 0128, A processor can process instructions that execute within an electronic device, including instructions stored in or on memory], cause: detecting a plurality of distinct multidimensional points in an original plurality of multidimensional points that contains duplicates; increasing, based on duplicates in the original plurality of multidimensional points, a respective observed frequency (Para 0010, count the number of times each feature value appears in the n feature values in the deduplicated dataset) of each distinct multidimensional point in the plurality of distinct multidimensional points [Para 0009, The initial dataset is sampled to obtain a sampled dataset. The initial dataset includes N feature values belonging to the same attribute. The sampled dataset includes n feature values from the N feature values, where n is an integer less than N; Para 0010, Perform a deduplication operation on the n feature values to obtain a deduplicated dataset, and count the number of times each feature value appears in the n feature values in the deduplicated dataset];
generating, based on a distinct multidimensional point of the plurality of distinct multidimensional points, a reconstruction of the distinct multidimensional point by a reconstructive model (Para 0029, deduplication module) [Para 0012, a deduplication dataset is obtained by performing deduplication operations on the feature values in the sampled dataset; Para 0032, The deduplication module is used to perform deduplication on the n feature values to obtain a deduplicated dataset, and to count the number of times each feature value in the deduplicated dataset appears among the n feature values].
Yin does not teach increasing, based on increasing frequency of distinct point, a scaled error of reconstruction of the distinct point; increasing, based on the scaled error of the reconstruction of the distinct point, accuracy of the reconstructive model.
Lin teaches,
increasing, based on increasing frequency of distinct point, a scaled error (Para 0055, tunable weights) of reconstruction of the distinct point [Para 0055, each of the convolutional filters includes a certain number of tunable weights. As the number of channels of each layer or branch increases, the number of weights grows accordingly; Para 0060, Using a layer including a set of filters (over one or multiple channels) as an example, the local features of the set of filters can be preserved by generating a copy of the set of filters (referred to as a duplicate set of filters), and using the original set of filters as supervision (e.g., by keeping the original set of filters fixed) as the duplicate set of filters are updated and as one or more candidate filters are removed from the duplicate set of filters… the loss minimization engine 312 can minimize a loss function (e.g., including an error function, such as mean squared error, and the penalty applied by the penalty engine 314) to maintain the outputs of the original set of filters as weights, scaling factors, and/or other parameters of the duplicate filters are updated and as the candidate filters or parameters (e.g., weights) of the candidate filters are removed from the duplicate set of filters];
increasing, based on the scaled error of the reconstruction of the distinct point, accuracy of reconstructive model (Para 0084, in order to minimize (or optimize) the loss function) [Para 0060, The candidate filters can be determined and removed from the complex layers or branches of the trained neural network 302 in a way that preserves the local features of the trained neural network 302, ensuring that the compressed versions of the complex layers or branches output features that are similar to the features output from the original (uncompressed) layers or branches. Using a layer including a set of filters (over one or multiple channels) as an example, the local features of the set of filters can be preserved by generating a copy of the set of filters (referred to as a duplicate set of filters), and using the original set of filters as supervision (e.g., by keeping the original set of filters fixed) as the duplicate set of filters are updated and as one or more candidate filters are removed from the duplicate set of filters. For example, as described below, the loss minimization engine 312 can minimize a loss function (e.g., including an error function, such as mean squared error, and the penalty applied by the penalty engine 314) to maintain the outputs of the original set of filters as weights, scaling factors, and/or other parameters of the duplicate filters are updated and as the candidate filters or parameters (e.g., weights) of the candidate filters are removed from the duplicate set of filters; Para 0083, At block 610, the process 600 includes minimizing a loss function of an error between the output of the set of filters and the output of the duplicate set of filters and a penalty applied to one or more scaling factors associated with the duplicate set of filters; Para 0084, the process 600 can include minimizing the loss function by iteratively determining the error between the output of the set of filters and the output of the duplicate set of filters and the penalty applied to the one or more scaling factors associated with the duplicate set of filters… as described above with reference to Equation (3), various iterations of the loss function can be performed in order to minimize (or optimize) the loss function.].
Lin is analogous to the claimed invention as they both relate to data deduplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Lin and provide a scaled error in order to tune model weights for improved accuracy.
Claim 12 is a non-transitory computer-readable media claim that recites identical limitations to method claim 2. Therefore, claim 12 is rejected using the same rationale as claim 2.
Claim(s) 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin, and in further view of Marti et al. (US 20130170762 A1), hereinafter Marti.
Regarding claim 3, Yin-Lin teach the limitations of claim 1 including multidimensional points (Yin, para 0009).
Yin-Lin do not teach generating a batch that represents more points than the batch contains.
Marti teaches,
generating a batch (Para 0073, improved reference data) that represents more points (Para 0073, reference data sets 64 can be increased) than the batch contains (Para 0073, batch 15 may be added to the reference data sets 64) [Para 0073, the data sets 1-11 in the batch 15 may be added to the reference data sets 64. In this way, the number of reference data sets 64 can be increased to give improved reference data for future correlation analysis processing].
Marti is analogous to the claimed invention as they both relate to data compression. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Marti and provide generating a batch with more data than the original batch contained [Marti, para 0073] in order to give improved reference data for future analysis processing.
Claim 13 is a non-transitory computer-readable media claim that recites identical limitations to method claim 3. Therefore, claim 13 is rejected using the same rationale as claim 3.
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin, and in further view of Zhang et al. (CN113408301A, see attached translation), hereinafter Zhang.
Regarding claim 4, Yin-Lin teach the limitations of claim 1 including multidimensional points and the original plurality of multidimensional points (Yin, para 0009).
Yin-Lin do not teach wherein each point in plurality of points represents a respective textual command.
Zhang teaches,
wherein each point in plurality of points represents a respective textual command [Para 0022, the query text in the initial training samples of the preset text matching model, i.e. the keywords input into the preset text matching model, is clustered. Then, the clustered query text is deduplicated and corrected according to the category and the corresponding sample timestamp. That is, multiple initial training samples generated within a certain period of time are deduplicated or the labels corresponding to the samples are corrected, and finally, target model training samples with higher sample data quality are obtained].
Zhang is analogous to the claimed invention as they both relate to data duplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Zhang and provide data points as textual commands [Zhang, para 0022] in order to obtain higher quality sample data in particular machine learning models such as text matching models.
Claim 14 is a non-transitory computer-readable media claim that recites identical limitations to method claim 4. Therefore, claim 14 is rejected using the same rationale as claim 4.
Claim(s) 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin and Zhang, and in further view of Del Monte (US 5704060 A), hereinafter Del Monte.
Regarding claim 5, Yin-Lin-Zhang teach the limitations of claim 4 including said detecting the plurality of distinct multidimensional points (claim 1: Yin, paras 0009 and 0010).
Yin-Lin-Zhang do not teach normalization of whitespace or decapitalization of letters.
Del Monte teaches,
normalization of whitespace or decapitalization of letters [Col 15, lines 5-8, a decapitalization function determines whether the term in buffer contains capital letters. If it does, the decapitalization function decapitalizes the term (i.e., converts it to all lowercase letters)].
Del Monte is analogous to the claimed invention as they both relate to textual data manipulation. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Del Monte and provide decapitalization in order to standardize data and enhance data efficiency.
Claim 15 is a non-transitory computer-readable media claim that recites identical limitations to method claim 5. Therefore, claim 15 is rejected using the same rationale as claim 5.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin and Zhang, and in further view of Armangau (US 20240028234 A1), hereinafter Armangau.
Regarding claim 6, Yin-Lin-Zhang teach the limitations of claim 4 including said detecting the plurality of distinct multidimensional points (claim 1: Yin, paras 0009 and 0010).
Yin-Lin-Zhang do not teach decreasing a numeric precision.
Armangau teaches,
decreasing a numeric precision [Para 0040, Optional function 316 modifies the hash values 314, e.g., by truncating such values (e.g., to 56 bits) to support more efficient computations].
Armangau is analogous to the claimed invention as they both relate to textual data manipulation. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Armangau and provide decreasing a numeric precision [Armangau, para 0040] in order to support more efficient computations.
Claim 16 is a non-transitory computer-readable media claim that recites identical limitations to method claim 6. Therefore, claim 16 is rejected using the same rationale as claim 6.
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin and Zhang, and in further view of Kascenas et al. (Denoising Autoencoders for Unsupervised Anomaly Detection in Brain MRI, published 2022), hereinafter Kascenas.
Regarding claim 7, Yin-Lin-Zhang teach the limitations of claim 4 including the original plurality of multidimensional points (claim 1: Yin, paras 0009 and 0010) and said accuracy of the reconstructive model (claim 1, Lin, para 0084).
Zhang further teaches,
the textual commands are database statements [Para 0032, Suppose a user enters a query text into a pre-defined text matching model such as a knowledge question-answering system, and the system returns 20 relevant texts.].
Zhang is analogous to the claimed invention as they both relate to data duplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Zhang and provide data points as textual commands are database statements [Zhang, para 0022] in order to obtain higher quality sample data in particular machine learning models such as text matching models.
Yin-Lin-Zhang teach do not teach anomaly detection accuracy.
Kascenas teaches,
anomaly detection accuracy [Figure 1, Our denoising autoencoder anomaly detection method. During training (top), noise is added to the foreground of the healthy image, and the network is trained to reconstruct the original image. At test time (bottom), the pixelwise post processed reconstruction error is used as the anomaly score].
Kascenas is analogous to the claimed invention as they both relate to autoencoders. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Kascenas and provide anomaly detection accuracy [Kascenas, Sect 1, para 4] in order to improve intensity threshold for machine learning systems.
Claim 17 is a non-transitory computer-readable media claim that recites identical limitations to method claim 7. Therefore, claim 17 is rejected using the same rationale as claim 7.
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin, and in further view of Zuluaga et al. (US 20190156442 A1), hereinafter Zuluaga.
Regarding claim 8, Yin-Lin teach the limitations of claim 1 including the original plurality of multidimensional points and the plurality of distinct multidimensional points (Yin, para 0032).
Yin-Lin do not teach wherein original points contains at least a hundred times as many points as plurality of distinct points.
Zuluaga teaches,
wherein original points contains at least a hundred times as many points as plurality of distinct points [Para 0024, Data deduplication is a data compression process which may match duplicate copies of repeated data such as duplicate web listings. In the deduplication process, web listings may be processed to identify web listings that are a match to one another. Often a stored web listing or master copy is compared to a newly received web listing. When a match occurs, the redundant web listing may be replaced with a small reference (e.g., bit value, pointer, URL, etc.) that points to the web listing, rather than storing a duplicate copy of the web listing and its images, description, reviews, etc. within a storage inventory (e.g., a file, a table, a data store, a database file, etc.) Because the same web listing may occur dozens or even hundreds of time, the amount of data that is stored and maintained may be greatly reduced by deduplication. When a subsequent search is performed, only a single web listing may be provided which is used to represent a group of duplicate web listings which can be found across multiple sites].
Zuluaga is analogous to the claimed invention as they both relate to deduplication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Zuluaga and provide original points containing at least a hundred times as many points as plurality of distinct points in order to provide a comprehensive training set to improve machine learning models.
Claim 18 is a non-transitory computer-readable media claim that recites identical limitations to method claim 8. Therefore, claim 18 is rejected using the same rationale as claim 8.
Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin, and in further view of Liang et al. (Training Stacked Denoising Autoencoders for Representation Learning, published 2021), hereinafter Liang.
Regarding claim 9, Yin-Lin teach the limitations of claim 1 including said increasing the accuracy of the reconstructive model (Lin, para 0084).
Yin-Lin do not teach applying stochastic gradient descent to a denoising autoencoder.
Liang teaches,
applying stochastic gradient descent to a denoising autoencoder [Abstract, We implement stacked denoising autoencoders, a class of neural networks that are capable of learning powerful representations of high dimensional data. We describe stochastic gradient descent for unsupervised training of autoencoders, as well as a novel genetic algorithm based approach that makes use of gradient information].
Liang is analogous to the claimed invention as they both relate to autoencoders. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Liang and provide anomaly applying stochastic gradient descent to a denoising autoencoder in order to enhance machine learning systems by implementing a processing methodology that improves efficiency and scalability with frequent updates.
Claim 19 is a non-transitory computer-readable media claim that recites identical limitations to method claim 9. Therefore, claim 19 is rejected using the same rationale as claim 9.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Lin, and in further view of Ren et al. (US 20230196597 A1), hereinafter Ren.
Regarding claim 10, Yin-Lin teach the limitations of claim 1 including the reconstructive model (Yin, para 0029).
Yin-Lin do not teach model inferring without using a distance measurement.
Ren teaches,
model inferring without using a distance measurement [Para 0076, At step 808, the system may generate a first three-dimensional model of the environment. In some embodiments, the three-dimensional model may be a three-dimensional point cloud, for example. The system may generate the first three-dimensional model based on the image sequence alone, i.e. without the set of distance measurements].
Ren is analogous to the claimed invention as they both relate to autoencoders. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yin’s teachings to incorporate the teachings of Ren and provide model inferring without using a distance measurement in order to improve model robustness when handling outliers.
Claim 20 is a non-transitory computer-readable media claim that recites identical limitations to method claim 10. Therefore, claim 20 is rejected using the same rationale as claim 10.
Conclusion
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/SYED RAYHAN AHMED/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126