DETAILED ACTION
This Final communication is in response to application no. 17/564,02 filed on 12/28/2021. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 04/14/2026 has been entered which amends claims 1-3, 11-12, 14-15 and 20. Claims 1-20 are pending. The amendments overcome the 101 rejection.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C § 102 and 103 filed 01/07/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments with respect to 35 U.S.C § 103 (pages 12-14 of applicant’s arguments) filed 04/14/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art reference Dalli (US 2022/0172050 A1) does not teach or suggest generating a set of candidate training data including simulating a problem domain. The examiner respectfully disagrees. Dalli teaches a simulator model S (2960 in Fig. 19) which uses information from a simulator constraint model C (2970 in Fig. 19). Dalli mentions [0180] “C may implement a combination of pre-defined knowledge priors, physical world models such as physics simulation models”. Also Dalli mentions [0214] “In an exemplary embodiment, an XAED and/or XGAN system may be implemented together with an XRL agent to generate realistic environmental simulations and/or provide experience learning data samples for the XRL agent. It is further contemplated that such an exemplary embodiment may be utilized in the creation of virtual reality simulations, augmented reality simulations, virtual collaboration spaces, and metaverses.” Thus, Dalli does teach simulating a problem domain.
Applicant also argues that the prior art reference Ghorbani (NPL: ‘Data Shapley: Equitable Valuation of Data for Machine Learning (Published 2019)) does not teach determining importance factors by evaluating inferences generated during training relative to a reference dataset to identify an effect of candidate training data on outputs of a machine learning model. The examiner respectfully disagrees. Like the applicant mentions in their arguments Ghorbani does evaluate the contribution of individual data points based on changes in model performance across subsets of a training dataset. This performance is computed by the function V. This function V takes into account data from a separate reference dataset. On page 2 right column they discuss this function V. Specifically “We can define
V
k
=
-
l
k
to be the predictor’s performance on the k-th test point. Similarly
ϕ
i
(
V
k
)
quantifies the value of the i-th training point to the k-th test point.”.
Thus, the 103 rejection is maintained.
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.
Claims 1-2, 10-13, and 20 are rejected 35 U.S.C. 103 as being unpatentable by Dalli (Us 20220172050 A1) in view of Ghorbani (NPL: ‘Data Shapley: Equitable Valuation of Data for Machine Learning (Published 2019)).
Regarding claim 1, Dalli teaches the following:
A method, comprising (claim 1).
generating, by a training data generator, a set of candidate training data, including simulating a problem domain using a perception system comprising a first machine learning model and a second machine learning model ([0180] “G 2900 may include a function P 2950 that drives the simulator model S 2960… S also receives constraint updates and priors from a simulator constraint model C 2970… In another example, C may implement a combination of pre-defined knowledge priors, physical world models such as physics simulation models ”. Also [0214] “In an exemplary embodiment, an XAED and/or XGAN system may be implemented together with an XRL agent to generate realistic environmental simulations and/or provide experience learning data samples for the XRL agent. It is further contemplated that such an exemplary embodiment may be utilized in the creation of virtual reality simulations, augmented reality simulations, virtual collaboration spaces, and metaverses.”).
updating the training data generator by modifying parameters used for simulating the problem domain based on one or more distributions of properties ([0150] “A model explanation may include coefficients θ of the explainable architecture x that may be utilized to explain the feature importance of the input features for a given observation” and [0164] “The detected bias, feature attributions and partition related explanations may be utilized as a feedback input 2600 to an explainable generator XG or a black-box generator G to tune and construct more realistic samples.”).
Dalli does not teach:
training the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain
Determining, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and
However Ghorbani does:
training the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain (Section 4.2 Synthetic Data “For the first sets of data set we us a logistic regression model and for the second set we use both a logistic regression and a neural network with one hidden layer.”).
Determining, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and (Section 1. Introduction page 2 “We propose data Shapley value, leveraging powerful results from game theory, to quantify the contribution of individual data points to a learning task.” And section 4. Experiments & Applications “Moreover we conduct two experiments showing that data points that are noisy or have label corruption will be assigned low Shapley value. Lastly we demonstrate that Shapley values can also give informative scores for groups of individuals. Taken together, these experiments suggest that, in addition to its equitable properties, data Shapley provides meaningful values to quantify the importance of data and can inform downstream analysis.” Also on page 2 right column “We can define
V
k
=
-
l
k
to be the predictor’s performance on the k-th test point. Similarly
ϕ
i
(
V
k
)
quantifies the value of the i-th training point to the k-th test point.”. )
Dalli and Ghorbani are considered analogous art to the claimed invention because they are in the same field of endeavor being data/model evaluation. It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to combine the data generation system of Dalli the training and feedback of Ghorbani. One of ordinary skill in the art would have been motivated to do this as Ghorbani discusses using their system on synthetic data. (Section 4.2 Synthetic Data)
Regarding claim 2, Dalli in view of Ghorbani teaches claim 1 as outlined above. Ghorbani further teaches:
one or more portions of the set of candidate training data included in the set of importance factors had at least a threshold effect in training the first machine learning model (Section 1. Introduction page 2 “Moreover, our empirical studies demonstrate that data Shapley has several additional utilities: 1) it gives more insights into the importance of each data point than the common leave-one-out score; 2) it can identify outliers and corrupted data; 3) it can inform how to acquire future data to improve the predictor.”)
Regarding claim 10, Dalli in view of Ghorbani teaches claim 1 as outlined above. Dalli further teaches:
wherein the training data generator comprises one or more of a generative adversarial network or a variational autoencoder ([0020] “The GAN architecture may include a generator and discriminator, which are designed to compete against each other. The generator may generate samples that are then evaluated by the discriminator to determine whether the generated samples are from the training dataset or from the generator”).
Regarding claim 11, Dalli in view of Ghorbani teaches claim 1 as outlined above. Dalli further teaches:
wherein the set of candidate training data comprises synthetic training data ([0101] “It may be contemplated that the data synthesis capabilities of white-box models can be used to generate additional synthetic training dataset samples that augment the training dataset”).
Regarding claim 12, Dalli teaches the following:
An apparatus, comprising: a memory configured to store data; a processing device coupled to the memory, the processing device configured to ([0054] “Further, many of the embodiments described herein are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It should be recognized by those skilled in the art that the various sequences of actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs)) and/or by program instructions executed by at least one processor”).
generate, by a training data generator, a set of candidate training data, including simulating a problem domain using a perception system comprising a first machine learning model and a second machine learning model ([0180] “G 2900 may include a function P 2950 that drives the simulator model S 2960… S also receives constraint updates and priors from a simulator constraint model C 2970… In another example, C may implement a combination of pre-defined knowledge priors, physical world models such as physics simulation models ”. Also [0214] “In an exemplary embodiment, an XAED and/or XGAN system may be implemented together with an XRL agent to generate realistic environmental simulations and/or provide experience learning data samples for the XRL agent. It is further contemplated that such an exemplary embodiment may be utilized in the creation of virtual reality simulations, augmented reality simulations, virtual collaboration spaces, and metaverses.”).
update the training data generator by modifying parameters used for simulating the problem domain based on one or more distributions of properties ([0150] “A model explanation may include coefficients θ of the explainable architecture x that may be utilized to explain the feature importance of the input features for a given observation” and [0164] “The detected bias, feature attributions and partition related explanations may be utilized as a feedback input 2600 to an explainable generator XG or a black-box generator G to tune and construct more realistic samples.”).
Dalli does not teach:
train the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain
Determine, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and
However Ghorbani does:
train the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain (Section 4.2 Synthetic Data “For the first sets of data set we us a logistic regression model and for the second set we use both a logistic regression and a neural network with one hidden layer.”).
Determine, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and (Section 1. Introduction page 2 “We propose data Shapley value, leveraging powerful results from game theory, to quantify the contribution of individual data points to a learning task.” And section 4. Experiments & Applications “Moreover we conduct two experiments showing that data points that are noisy or have label corruption will be assigned low Shapley value. Lastly we demonstrate that Shapley values can also give informative scores for groups of individuals. Taken together, these experiments suggest that, in addition to its equitable properties, data Shapley provides meaningful values to quantify the importance of data and can inform downstream analysis.” Also on page 2 right column “We can define
V
k
=
-
l
k
to be the predictor’s performance on the k-th test point. Similarly
ϕ
i
(
V
k
)
quantifies the value of the i-th training point to the k-th test point.”. )
Dalli and Ghorbani are considered analogous art to the claimed invention because they are in the same field of endeavor being data/model evaluation. It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to combine the data generation system of Dalli the training and feedback of Ghorbani. One of ordinary skill in the art would have been motivated to do this as Ghorbani discusses using their system on synthetic data. (Section 4.2 Synthetic Data)
Regarding claim 13, Dalli in view of Ghorbani teaches claim 12 as outlined above. Ghorbani further teaches:
the set of importance factors is determined by the second machine learning model; and the second machine learning model uses an importance function to determine the set of importance factors based on the set of inferences and the set of reference data (Section 3. Approximating Data Shapley describes how they decide what data is important specifically algorithm 1 and algorithm 2)
Regarding claim 20, Dalli teaches the following
A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising ([0054] “Additionally, the sequence of actions described herein can be embodied entirely within any form of computer-readable storage medium such that execution of the sequence of actions enables the at least one processor to perform the functionality described herein”).
generating, by a training data generator, a set of candidate training data, including simulating a problem domain using a perception system comprising a first machine learning model and a second machine learning model ([0180] “G 2900 may include a function P 2950 that drives the simulator model S 2960… S also receives constraint updates and priors from a simulator constraint model C 2970… In another example, C may implement a combination of pre-defined knowledge priors, physical world models such as physics simulation models ”. Also [0214] “In an exemplary embodiment, an XAED and/or XGAN system may be implemented together with an XRL agent to generate realistic environmental simulations and/or provide experience learning data samples for the XRL agent. It is further contemplated that such an exemplary embodiment may be utilized in the creation of virtual reality simulations, augmented reality simulations, virtual collaboration spaces, and metaverses.”).
updating the training data generator by modifying parameters used for simulating the problem domain based on one or more distributions of properties ([0150] “A model explanation may include coefficients θ of the explainable architecture x that may be utilized to explain the feature importance of the input features for a given observation” and [0164] “The detected bias, feature attributions and partition related explanations may be utilized as a feedback input 2600 to an explainable generator XG or a black-box generator G to tune and construct more realistic samples.”).
Dalli does not teach:
training the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain
Determining, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and
However Ghorbani does:
training the first machine learning model based on the set of candidate training data, , wherein the first machine learning model outputs a set of inferences during the training based on the set of candidate training data generated from simulating the problem domain (Section 4.2 Synthetic Data “For the first sets of data set we us a logistic regression model and for the second set we use both a logistic regression and a neural network with one hidden layer.”).
Determining, by the second machine learning model, a set of importance factors including evaluating the set of inferences relative to a set of reference data, obtained from a computing device separate from the perception system to identify an effect of respective candidate training data on outputs of the first machine learning model, wherein the set of importance factors identifies one or more portions of the set of candidate training data that were important to the training of the first machine learning model; and (Section 1. Introduction page 2 “We propose data Shapley value, leveraging powerful results from game theory, to quantify the contribution of individual data points to a learning task.” And section 4. Experiments & Applications “Moreover we conduct two experiments showing that data points that are noisy or have label corruption will be assigned low Shapley value. Lastly we demonstrate that Shapley values can also give informative scores for groups of individuals. Taken together, these experiments suggest that, in addition to its equitable properties, data Shapley provides meaningful values to quantify the importance of data and can inform downstream analysis.” Also on page 2 right column “We can define
V
k
=
-
l
k
to be the predictor’s performance on the k-th test point. Similarly
ϕ
i
(
V
k
)
quantifies the value of the i-th training point to the k-th test point.”. )
Dalli and Ghorbani are considered analogous art to the claimed invention because they are in the same field of endeavor being data/model evaluation. It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to combine the data generation system of Dalli the training and feedback of Ghorbani. One of ordinary skill in the art would have been motivated to do this as Ghorbani discusses using their system on synthetic data. (Section 4.2 Synthetic Data)
Claims 3-9 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dalli in view of Ghorbani and Maruta (US 20240078468 A1).
Regarding claim 3, Dalli in view of Ghorbani teaches claim 1 as outlined above. Maruta teaches:
the set of reference data comprises a set of validation data (Fig. 6 & [0059] “dividing data [reference data] into training data D1_1 and validation data”).
Dalli, Ghorbani and Maruta are considered analogous art to the claimed invention because they are in the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to combine the validation method of Maruta with the data generation system of Dalli. One of ordinary skill in the art would have been motivated to do this obtain accurate training data (Maruta: [0010] & [0011]).
Regarding claim 4, Dalli in view of Ghorbani teaches claim 1 as outlined above. Maruta teaches:
determining a first distribution of properties for the set of candidate training data and a second distribution of properties for the set of reference data ([0056] “the distribution of the training data D1_1 and the distribution of the test data D1_2”).
Regarding claim 5, Dalli in view of Ghorbani and Maruta teaches claim 4 as outlined above. Maruta further teaches:
determining whether the first distribution of properties for the set of candidate training data is within a threshold of the second distribution of properties for the set of reference data ([0056] “Next, the validation method recommending unit 12 determines whether or not the overlap between the distribution of the training data D1_1 and the distribution of the test data D1_2 in the column data C3 is equal to or more than a threshold value”).
Regarding claim 6, Dalli in view of Ghorbani and Maruta teaches claim 5 as outlined above. Maruta further teaches:
wherein the training data generator is updated in response to determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of reference data ([0159] “the distribution difference between the distribution of the training data and the distribution of the test data is less than the threshold value, the validation method recommending unit 12A recommends a validation method for extracting the training data and the validation data in such a manner that the localization relationship between the training data and the test data is maintained”).
Regarding claim 7, Dalli in view of Ghorbani and Maruta teaches claim 5 as outlined above. Dalli further teaches:
generating a second set of candidate training data based on a training data generator (Fig. 19: Generator 2900; [0180] “it is further contemplated that S [part of the generator] may be a symbolic model that generates simulated or synthetic data”).
Dalli does not teach:
training the first machine learning model based on the second set of candidate training data, wherein the first machine learning model generates a second set of inferences during the training based on the second set of candidate training data; and
determining a second set of importance factors based on the second set of inferences and the second machine learning model
determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of validation data:
Ghorbani teaches:
training the first machine learning model based on the second set of candidate training data, wherein the first machine learning model generates a second set of inferences during the training based on the second set of candidate training data; and (Section 4.2 Synthetic Data “For the first sets of data set we us a logistic regression model and for the second set we use both a logistic regression and a neural network with one hidden layer.”).
determining a second set of importance factors based on the second set of inferences and the second machine learning model (Section 1. Introduction page 2 “We propose data Shapley value, leveraging powerful results from game theory, to quantify the contribution of individual data points to a learning task.” And section 4. Experiments & Applications “Moreover we conduct two experiments showing that data points that are noisy or have label corruption will be assigned low Shapley value. Lastly we demonstrate that Shapley values can also give informative scores for groups of individuals. Taken together, these experiments suggest that, in addition to its equitable properties, data Shapley provides meaningful values to quantify the importance of data and can inform downstream analysis.” And there is discussions in section 3.2 on iterating the process.)
Ghorbani does not teach:
determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of validation data:
But Maruta does teach this ([0159] “the distribution difference between the distribution of the training data and the distribution of the test data is less than the threshold value, the validation method recommending unit 12A recommends a validation method for extracting the training data and the validation data in such a manner that the localization relationship between the training data and the test data is maintained”).
Regarding claim 8, Dalli in view of Ghorbani and Maruta teaches claim 5 as outlined above. Maruta further teaches:
determining that the first distribution of properties for the set of candidate training data is within a threshold of the second distribution of properties for the set of reference data, generating training data based on the training data generator, wherein the training data is provided to other machine learning models to train the other machine learning models ([0056] “the distribution of the training data D1_1 and the distribution of the test data D1_2 in the column data C3 is equal to or more than a threshold value (step ST1B). In the portion where the distributions overlap, the training data D1_1 and the test data D1_2 include the same data”).
Regarding claim 9, Dalli in view of Ghorbani and Maruta teaches claim 5 as outlined above. Maruta further teaches:
the first distribution of properties and the second distribution of properties are associated with labels for the set of reference data ([0059] “a concept diagram illustrating data divided into the training data D1_1 and the validation data in such a manner that the rates of the training labels are the same and the training labels do not overlap in the 3-fold cross validation”).
Regarding claim 14, Dalli in view of Ghorbani teaches claim 12 as outlined above. Maruta teaches:
the set of reference data comprises a set of validation data (Fig. 6 & [0059] “dividing data [reference data] into training data D1_1 and validation data”).
Regarding claim 15, Dalli in view of Ghorbani teaches claim 12 as outlined above. Maruta teaches:
determining a first distribution of properties for the set of candidate training data and a second distribution of properties for the set of reference data ([0056] “the distribution of the training data D1_1 and the distribution of the test data D1_2”).
Regarding claim 16, Dalli in view of Ghorbani and Maruta teaches claim 15 as outlined above. Maruta further teaches:
determining whether the first distribution of properties for the set of candidate training data is within a threshold of the second distribution of properties for the set of reference data ([0056] “Next, the validation method recommending unit 12 determines whether or not the overlap between the distribution of the training data D1_1 and the distribution of the test data D1_2 in the column data C3 is equal to or more than a threshold value”).
Regarding claim 17, Dalli in view of Ghorbani and Maruta teaches claim 15 as outlined above. Maruta further teaches:
wherein the training data generator is updated in response to determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of valid reference data ([0159] “the distribution difference between the distribution of the training data and the distribution of the test data is less than the threshold value, the validation method recommending unit 12A recommends a validation method for extracting the training data and the validation data in such a manner that the localization relationship between the training data and the test data is maintained”).
Regarding claim 18, Dalli in view of Ghorbani and Maruta teaches claim 15 as outlined above. Dalli further teaches:
generating a second set of candidate training data based on a training data generator (Fig. 19: Generator 2900; [0180] “it is further contemplated that S [part of the generator] may be a symbolic model that generates simulated or synthetic data”).
training the first machine learning model based on the second set of candidate training data, wherein the first machine learning model generates a second set of inferences during the training based on the second set of candidate training data; and (Fig. 19 Simulator model S 2960 [equivalent to first machine learning model] and sample from simulator model 2910 [equivalent to inferences]).
determining a second set of importance factors based on the second set of inferences and the second machine learning model (Discriminator D (2720 from Fig. 19) [0180] “generated explanations [equivalent to importance factors] from the explainable discriminator [equivalent to second machine learning model]”. [0179] “explanations may be in multiple formats… numeric formats which may represent the importance of the input dimensions or the bias in the given input dimensions”).
Dalli does not teach:
training the first machine learning model based on the second set of candidate training data, wherein the first machine learning model generates a second set of inferences during the training based on the second set of candidate training data; and
determining a second set of importance factors based on the second set of inferences and the second machine learning model
determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of validation data:
Ghorbani teaches:
training the first machine learning model based on the second set of candidate training data, wherein the first machine learning model generates a second set of inferences during the training based on the second set of candidate training data; and (Section 4.2 Synthetic Data “For the first sets of data set we us a logistic regression model and for the second set we use both a logistic regression and a neural network with one hidden layer.”).
determining a second set of importance factors based on the second set of inferences and the second machine learning model (Section 1. Introduction page 2 “We propose data Shapley value, leveraging powerful results from game theory, to quantify the contribution of individual data points to a learning task.” And section 4. Experiments & Applications “Moreover we conduct two experiments showing that data points that are noisy or have label corruption will be assigned low Shapley value. Lastly we demonstrate that Shapley values can also give informative scores for groups of individuals. Taken together, these experiments suggest that, in addition to its equitable properties, data Shapley provides meaningful values to quantify the importance of data and can inform downstream analysis.” And there is discussions in section 3.2 on iterating the process.)
Ghorbani does not teach:
determining that the first distribution of properties for the set of candidate training data is not within a threshold of the second distribution of properties for the set of validation data:
But Maruta does teach this ([0159] “the distribution difference between the distribution of the training data and the distribution of the test data is less than the threshold value, the validation method recommending unit 12A recommends a validation method for extracting the training data and the validation data in such a manner that the localization relationship between the training data and the test data is maintained”).
Regarding claim 19, Dalli in view of Ghorbani and Maruta teaches claim 15 as outlined above. Maruta further teaches:
determining that the first distribution of properties for the set of candidate training data is within a threshold of the second distribution of properties for the set of reference data, generating training data based on the training data generator, wherein the training data is provided to other machine learning models to train the other machine learning models ([0056] “the distribution of the training data D1_1 and the distribution of the test data D1_2 in the column data C3 is equal to or more than a threshold value (step ST1B). In the portion where the distributions overlap, the training data D1_1 and the test data D1_2 include the same data”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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