DETAILED ACTION
Response to Amendment
The amendment filed on 21 October 2025 has been entered.
Claims 1-2, 4-9, 11-16 and 18-21 are pending.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 are currently amended.
Claims 7, 14, 21 are cancelled.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 will be pending.
Applicant’s amendments to the Claims have overcome each and every rejection under 35 USC 101 previously set forth in the Non-Final Office Action mailed 21 July 2025.
Response to Arguments
Applicant's arguments filed on 21 October 2025 have been fully considered but they are not persuasive.
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully
considered.
Applicant submits Kale teaches away from the emphasized features of Claim 1. Applicant notes the Office Action alleges [0122] and [0136] discloses "determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model" as recited in Claim 1. Applicant submits [0122] of Kale does not teach the claimed difference.
Applicant submits a skilled artisan would not, as alleged by the Office Action, modify Maughan with Kale's disclosure of deleting a data point and then retraining a model for each data point to identify the influence "in order to improve computational efficiency and accuracy", as Kale teaches this very process is computationally expensive, and its MRR and SYNTHQ methods are express alternatives that avoid repeated retraining.
Examiner respectfully disagrees.
In response to Applicant’s argument of Kale teaching away from “determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model”, Examiner notes Kale teaches influence functions as a means to estimate the influence of training data points on a particular prediction made by a model and by use of said influence functions, repeated retraining of the model is avoided (cf. Kale, [0009]). Kale teaches obtaining the training data point, obtaining the prediction having a predicted value for each feature, and for each feature, calculating an influence score representing the influence of the training data point on the prediction of the corresponding predicted value for the feature (cf. Kale, [0012]), as well use of mean reciprocal rank (MRR) to calculate quality of the synthetic data based directly on the influence scores of the data points. Kale’s embodiments disclose more efficient methods for retraining, not removal of retraining altogether, similar to claimed embodiments of the claimed invention. Examiner submits Kale does not teach away from “determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model” as Kale discloses embodiments that allow for determining the quality of machine learning predictions and taking remedial actions, such as retraining the system (cf. Kale, [0168]), the predictions (e.g. the synthetic data points) as input for use in determining the influence values (cf. Kale, [0172]), and further configurations to determine whether the quality metric is above a threshold, issuing instructions to retrain the model, and generating new predictions using the retrained model (cf. Kale, [0174]).
The rejections of Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 under 35 USC 103 have been maintained.
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 Objections
Claim 1 and analogous claims 8 and 15 are objected to because of the following informalities: “a measure of influence for each training data points” should be “a measure of influence for each training data point”. Appropriate correction is required.
Claim 4 and analogous claims 11 and 18 are objected to because of the following informalities: “of one or more of the training data points” should be “each of the training data points”. Appropriate correction is required.
Claim 5 and analogous claims 12 and 19 are objected to because of the following informalities: “of the of the” should be “of the”. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: “The system of claim 2” should be “The system of claim 9”. Appropriate correction is required.
Claim 18 is objected to because of the following informalities: “The non-transitory computer-readable storage medium of claim 15” should be “The non-transitory computer-readable storage medium of claim 16”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 and analogous claims 8 and 15 recites the limitation "the training data set" in line 28. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the training data set" has been construed to be “the amended training data set” in lines 13-14 of claim 1. Claims 2, 4-6, 9, 11-13, 16, 18-20, which are dependent on claims 1, 8, 15, are similarly rejected.
Claim 1 and analogous claims 8 and 15 recites the limitation "the another training data point" in line 30. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the another training data point" has been construed to be “another training data point”. Claims 2, 4-6, 9, 11-13, 16, 18-20, which are dependent on claims 1, 8, 15, are similarly rejected.
Claim 1 and analogous claims 8 and 15 recites the limitation "the procedure" in line 32. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the procedure" has been construed to be “a procedure”. Claims 2, 4-6, 9, 11-13, 16, 18-20, which are dependent on claims 1, 8, 15, are similarly rejected.
Claim 5 and analogous claims 12 and 19 recites the limitation "the selected training data point" in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term “the selected training data point" has been construed to be “a selected training data point”. Claims 6, 13, 20, which are dependent on claims 5, 12, 19, are similarly rejected.
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.
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.
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, 5-6, 8, 12-13, 15, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maughan et al. (U.S. Pre-Grant Publication No. 2017/0330109, previously made of record, hereinafter 'Maughan'), in view of Kale et al. (U.S. Pre-Grant Publication No. 20200334557, hereinafter 'Kale'), Ghorbani et al. (NPL: "What is your data worth? Equitable Valuation of Data", hereinafter 'Ghorbani '), and further in view of Fu et. al. (NPL: "Representing financial time series based on data point importance", hereinafter 'Fu').
Regarding claim 1 and analogous claims 8 and 15, Maughan teaches A method comprising, training, by a processor ([0031] These computer program instructions may be provided to a processor)
a regression machine learning model ([0042] Regression models may be trained using supervised learning to predict a continuous numeric outcome)
using training data comprising training data points for a historical time-series ([0041] models are trained using sample historic data and associated historic outcomes; [0055] Thus, in certain embodiments, training data may refer to historical data for which one or more results are known, and workload data may refer to present or prospective data for which one or more results are to be predicted.);
utilizing, by the processor, the trained regression machine learning model to generate a prediction ([0042] Regression models may be trained using supervised learning to predict a continuous numeric outcome; [0048] In various embodiments, the predictive analytics module 102 may apply a model (e.g., a predictive ensemble, one or more learned functions or the like) to workload data to produce predictive results);
receiving, by a machine learning interpretability module, the training data ([0103] The data receiver module 402, in certain embodiments, is configured to receive client data, such as training data),
the trained model and the prediction ([0104] One type of data that the data receiver module 402 may receive, as part of a new ensemble request or the like, is initialization data. The prediction module 202, in certain embodiments, may use initialization data to train and test learned functions from which the prediction module 202 may build a predictive ensemble);
retraining, by the machine learning interpretability module, the trained regression machine learning model on the amended training data set (Fig. 3 Retrain Module 302 Paragraph [0008] In some embodiments, an operation includes retraining a model based on updated training data);
utilizing, by the machine learning interpretability module, the retrained regression machine learning model to generate an amended prediction ([0142] For example, if a client 104 has requested a predictive ensemble 504 to predict whether a customer will be a repeat customer, and provided historical customer information as initialization data, the function evaluator module 512 may input a test data set comprising one or more features of the initialization data other than whether the customer was a repeat customer into the learned function, and compare the resulting predictions to the initialization data to determine the accuracy and/or effectiveness of the learned function);
Maughan fails to teach determining, for each of the training data points of the training data, a measure of influence for each training data points, wherein determining the measure of influence for each of the training data points comprises: removing, by the machine learning interpretability module, the training data point from the training data to form an amended training data set; determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model; determining, by the machine learning interpretability module, a measure of influence for the removed training data point, based on the difference; return the removed training data point to the training data set along with the measure of influence for the removed training data point: remove, by the machine learning interpretability module, the another training data point from the training data to form another amended training data set; and repeat the procedure for determining and returning the measure of influence for each of the training data points in the training data; and visually outputting the measure of influence of each of the training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction.
Kale teaches determining, for each of the training data points of the training data, a measure of influence for each training data points ([0086] Influence functions allow the determining, for each of the training data points of the training data, a measure of influence for each training data points computation of the influence of a particular training data point on the prediction of a particular value. An observed (training) data point is influential (has a high influence value) when the deletion of that observed data point from the observed set of data points produces a large change in the parameters or predictions of the model that is trained on the observed set. The influence can be determined either through deletion of the data point and retraining the model; however, retraining the model for each data point can be computationally expensive.),
wherein determining the measure of influence for each of the training data points comprises: removing, by the machine learning interpretability module, the training data point from the training data to form an amended training data set ([0086] Influence functions allow the wherein determining the measure of influence for each of the training data points comprises computation of the influence of a particular training data point on the prediction of a particular value. An observed (training) data point is influential (has a high influence value) when the removing, by the machine learning interpretability module, the training data point from the training data to form an amended training data set deletion of that observed data point from the observed set of data points produces a large change in the parameters or predictions of the model that is trained on the observed set. The influence can be determined either through deletion of the data point and retraining the model; however, retraining the model for each data point can be computationally expensive.);
determining, by the machine learning interpretability module, a difference between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model ([0122] The embodiments described herein provide various quality scores for predictions (e.g. synthesized data) proposed in the present embodiment. A first embodiment makes use of influence scores to calculate a quality metric based on determining, by the machine learning interpretability module, a difference mean reciprocal rank (MRR). A second embodiment makes use of a synthetic data quality metric (termed SYNTHQ) that is based on the notion of distance functions and can be applied to any data synthesis method. Both cases make use of the distance to one or more of the closest observed data points.; [0136] Accordingly, the MRR can be used as a metric to control the training of a machine learning system. Where the MRR is low, the system can make a decision to between the prediction generated by the trained regression machine learning model and the amended prediction generated by the retrained regression machine learning model repeat a training step (or take another alternative action) to improve the quality of the predictions being made. This can be repeated until a sufficiently high MRR has been achieved (or a maximum number of iterations have been reached).);
determining, by the machine learning interpretability module, a measure of influence for the removed training data point, based on the difference ([0086] Influence functions allow the computation of the influence of a particular training data point on the prediction of a particular value. An observed (training) data point is determining, by the machine learning interpretability module, a measure of influence influential (has a high influence value) when the for the removed training data point, based on the difference deletion of that observed data point from the observed set of data points produces a large change in the parameters or predictions of the model that is trained on the observed set. The influence can be determined either through deletion of the data point and retraining the model; however, retraining the model for each data point can be computationally expensive.); and
Maughan and Kale are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maughan, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Kale to Maughan before the effective filing date of the claimed invention in order to improve computational efficiency and accuracy of determining the influence of a training data point on a machine learning model and of determining one or more quality metrics quantifying the quality of a set of one or more predictions by a machine learning model (cf. Kale, [0001] The present disclosure relates to improvements in the computational efficiency and accuracy of determining the influence of a training data point on a machine learning model and of determining one or more quality metrics quantifying the quality of a set of one or more predictions by a machine learning model. In particular, but without limitation, this disclosure relates to improvements in the computational efficiency and accuracy of determining statistical measures of quality of machine learning predictions, particularly for predictions based on a chain of machine learning models.).
Ghorbani teaches return the removed training data point to the training data set along with the measure of influence for the removed training data point; remove, by the machine learning interpretability module, the another training data point from the training data to form another amended training data set; and repeat the procedure for determining and returning the measure of influence for each of the training data points in the training data ([All data sources are not created equal, pg. 6] As our first experiment, we remove, by the machine learning interpretability module, the another training data point from the training data to form another amended training data set remove data points one by one starting with the order of their value; from the most valuable to the least valuable point. repeat the procedure for determining and returning the measure of influence for each of the training data points in the training data Each time, after the point is removed, a new model is trained on the remaining train data. Fig. 3(a) shows how the prediction performance of the trained models evolves through the experiment (accuracy on the held-out set); points that DATA SHAPLEY considers valuable are crucially important for prediction. We can perform the experiment in reverse; Fig. 3(b) depicts the results for the opposite setting where we remove data points starting from the least valuable (reverse value order). It is interesting to notice that removing points with low Shapley value actually helps with better performance. In addition to removing data from the train set, we examine the return the removed training data point to the training data set along with the measure of influence for the removed training data point opposite setting of adding data. Inspecting the train data points with high Shapley value can inform us about which new data points to collect—by recruiting similar individuals—in order to improve the model performance.); and
Maughan, Kale, and Ghorbani are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maughan and Kale, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Ghorbani to Maughan before the effective filing date of the claimed invention in order to quantify the value of each training datum to the predictor performance (cf. Ghorbani, [Abstract, pg. 1] As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on n data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.).
Fu teaches visually outputting the measure of influence of each of the training data points to a graphical user interface so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction ([5.2. Performance analysis, pg. 298] visually outputting the measure of influence of each of the training data points to a graphical user interface Fig. 38 shows the visualization results for sampling, PAA and the proposed method. It is obvious that important signals (fluctuations) were lost in the sampling approach due to the sampling rate (see the circled region in Fig. 38a). Better result was achieved by the PAA approach as shown in Fig. 38b. Despite the fact that the shape of the time series has been better captured, the data points particularly the salient points of the time series have been smoothed out due to the averaging effect. On the other hand, so that a visualization of the training data comprises a visualization of the measure of influence of each of the training points on the prediction PIP-based approach could capture the fluctuations of the times series and the important signals in the time series could be preserved even if the time series was compressed and displayed on a small screen (Fig. 38c). Therefore, it is more suitable for applications that the fluctuations of the time series are important like financial chart analysis (e.g. wave principle analysis).).
Maughan, Kale, Ghorbani, and Fu are considered to be analogous to the claimed invention because they are in the same field of data management. In view of the teachings of Maughan, Kale, and Ghorbani, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Fu to Maughan before the effective filing date of the claimed invention in order to present time series data in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data (cf. Fu, [Abstract, pg. 277] Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated.).
Regarding claim 5 and analogous claims 12 and 19, Maughan, as modified by Kale, Ghorbani, and Fu, teaches The method of claim 1, The system of claim 8, and The non-transitory computer-readable storage medium of claim 15, respectively.
Fu teaches wherein the visualization of the of the measure of influence of each of the training points on the prediction comprises a visualization of changes from a resulting amended data forecast from the amended training data set for the selected training data point relative to a full data forecast from the training data ([5.2. Performance analysis, pg. 298] Fig. 38 shows the wherein the visualization of the of the measure of influence of each of the training points on the prediction comprises a visualization visualization results for sampling, PAA and the proposed method. It is obvious that important signals (fluctuations) were lost in the sampling approach due to the sampling rate (see the circled region in Fig. 38a). Better result was achieved by the PAA approach as shown in Fig. 38b. Despite the fact that the shape of the time series has been better captured, the data points particularly the salient points of the time series have been smoothed out due to the averaging effect. On the other hand, PIP-based approach could capture the fluctuations of the times series and the important signals in the time series could be preserved even if the time series was compressed and displayed on a small screen (Fig. 38c).; [3.2. Incremental updating, pg. 284] The tree built in of changes from a resulting amended data forecast from the amended training data set for the selected training data point relative to a full data forecast from the training data Fig. 9 needs to be updated when new data points have been appended to it (Fig. 12). Given the data point 11, as the shape of the overall time series has been greatly changed, when evaluating the root node with the new entry, data point 9 (the point with maximum distance on the opposite side before) has replaced the original PIP and become the new PIP identified. Therefore, rebuilding the whole tree is needed (Fig. 13b). The same case happens again when adding data point 12 (Fig. 13c).).
Maughan, Kale, Ghorbani, and Fu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6 and analogous claims 13 and 20, Maughan, as modified by Kale, Ghorbani, and Fu, teaches The method of claim 5, The system of claim 12, and The non-transitory computer-readable storage medium of claim 19, respectively.
Fu teaches wherein the graphical user interface is configured to allow a user to select a given one of the training data points from the training data set and visualize the changes from the resulting amended data forecast from the amended training data set for the selected training data point relative to the full data forecast from the training data ([5. Mobile application, pg. 296-297] In view of the characteristics of mobile environment and financial time series data mentioned previously, a new representation for financial wherein the graphical user interface is configured time series visualization on mobile devices is needed. It should be able to Facilitate multi-resolution visualization. Before making investment decisions, the mobile users would like to analyze the time series data with respect to different time spans, start and/or end date, levels of detail, etc.; [5.1. Adaptation of the proposed representation in mobile environment, pg. 297] It is an easy task to introduce multi-resolution displaying ability to the proposed visualization framework. While the lower level resolution time series is being displayed, users can select the segment they are interested in at anytime by sending the starting and ending points of the requested segment to the server and the to allow a user to select a given one of the training data points from the training data set data points of that segment can then be sent to the user. The strategy of sending the corresponding data points is simply based on their order in accessing the tree (i.e. same as the proposed mechanism). Except the starting and ending points are sent to the user first, other and visualize the changes from the resulting amended data forecast from the amended training data set for the selected training data point relative to the full data forecast from the training data data points within that segment are sent to the user based on their orders in accessing the tree which retrieves from the root of the tree. Therefore, the progressive visualization effect is still applied in displaying of the subsequences.).
Maughan, Kale, Ghorbani, and Fu are combinable for the same rationale as set forth above with respect to claim 1.
Claims 2, 4, 9, 11, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Maughan, in view of Kale, Ghorbani, Fu, and further in view of (NPL: "Gradient colour scales", hereinafter 'GCS').
Regarding claim 2 and analogous claims 9 and 16, Maughan, as modified by Kale, Ghorbani, and Fu, teaches The method of claim 1, The system of claim 8, and The non-transitory computer-readable storage medium of claim 15, respectively.
Fu teaches wherein the visualization of the training data, comprises a gradient representation of the measure of influence of each of the training points ([5.2. Performance analysis, pg. 298] Fig. 38 shows the wherein the visualization of the training data, comprises visualization results for sampling, PAA and the proposed method. It is obvious that important signals (fluctuations) were lost in the sampling approach due to the sampling rate (see the circled region in Fig. 38a). Better result was achieved by the PAA approach as shown in Fig. 38b. Despite the fact that the shape of the time series has been better captured, the data points particularly the salient points of the time series have been smoothed out due to the averaging effect. On the other hand, the measure of influence of each of the training points PIP-based approach could capture the fluctuations of the times series and the important signals in the time series could be preserved even if the time series was compressed and displayed on a small screen (Fig. 38c).).
Maughan, Kale, Ghorbani, and Fu are combinable for the same rationale as set forth above with respect to claim 1.
GCS teaches wherein the visualization of the training data, comprises a gradient representation of the measure of influence of each of the training points ([rescaler] Used by diverging and n colour gradients (i.e. wherein the visualization of the training data, comprises a gradient representation scale_colour_gradient2(), scale_colour_gradientn()). A function used to scale the input values to the range [0, 1].).
Maughan, Kale, Ghorbani, Fu, and GCS are considered to be analogous to the claimed invention because they are in the same field of data management. In view of the teachings of Maughan, Kale, Ghorbani, and Fu, it would have been obvious for a person of ordinary skill in the art to apply the teachings of GCS to Maughan before the effective filing date of the claimed invention in order to scale the input values to the range [0, 1] (cf. GCS, [rescaler] Used by diverging and n colour gradients (i.e. scale_colour_gradient2(), scale_colour_gradientn()). A function used to scale the input values to the range [0, 1].).
Regarding claim 4 and analogous claims 11 and 18, Maughan, as modified by Kale, Ghorbani, and Fu, teaches The method of claim 2, The system of claim 9, and The non-transitory computer-readable storage medium of claim 16, respectively.
GCS teaches wherein the gradient representation comprises a gradient key that indicates the measure of influence of one or more of the training data points ([Guide] comprises a gradient key Type of legend. Use "colourbar" for continuous colour bar, or "legend" for discrete colour legend.; [rescaler] Used by diverging and n colour gradients (i.e. scale_colour_gradient2(), scale_colour_gradientn()). A function indicates the measure of influence of one or more of the training data points used to scale the input values to the range [0, 1].).
Maughan, Kale, Ghorbani, Fu, and GCS are combinable for the same rationale as set forth above with respect to claim 2.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129