DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to communications filed on 05/21/2026.
Claims 11, 13-14, and 17 have been canceled.
Claims 21-22 have been added.
Claims 1-10, 12, 15-16, and 18-22 are pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10, 12, 15-16, and 18-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 is amended to recite “dividing the attributation data into a plurality of groups; analyzing the artificial neural network based on the plurality of groups of the attributation data by at least one of: detecting errors in the artificial neural network, or detecting errors in input data to the artificial neural network”. However, the specification does not support the above features. The specification describes separately “dividing the attributation data into a plurality of groups, wherein the artificial neural network is analyzed based on the plurality of groups” (e.g. in page 4) and “analyzing the artificial neural network comprises detecting errors in the artificial neural network or in input data to the artificial neural network” (e.g. in page 5), but it does not describe that analyzing the artificial neural network based on the plurality of groups is by at least one of: detecting errors in the artificial neural network, or detecting errors in input data to the artificial neural network. As can be seen, the specification does not describe analyzing based on groups is by detecting errors. As such, the claim lacks written description. Independent claims 19 and 20 also recite similar limitations and therefore have the same problem. Due at least to their dependency upon claim 1, dependent claims 2-10, 12, 15-16, 18 and 21-22 also fail to comply with the written description requirement.
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 1-10, 12, 15-16, and 18-22 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 has been amended to recite “computer implemented method for analyzing at least one reinforcement learning agent based on an artificial neural network, the method comprising:… wherein the at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle”. However, this is unclear because “wherein….vehicle” is not a method step and it appears to modify the preamble, but “at least one reinforcement learning agent” is recited as intended use (“for analyzing at least one reinforcement learning agent”). This raises question as to whether or not “wherein the at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle” is intended to have patentable weight (note: in the interest of advancing prosecution, the limitation is given weight in the rejections below). As such, the claim is indefinite. Independent claims 19 and 20 also recite similar limitations and therefore have the same problem. Due at least to their dependency upon claim 1, dependent claims 2-10, 12, 15-16, 18 and 21-22 also are indefinite.
Response to Arguments
Previous rejections under 35 USC 101 have been withdrawn in view of amendments.
Applicant’s arguments with respect to references have been considered but are moot in view of new grounds of rejection. See Brennan et al. (US 20180075368 A1) and Balakrishnan et al. (US 20190299978 A1) below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7-9, 12, 15, 18-20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Gou et al. (US 20190303765 A1) in view of Heuillet et al. (“Explainability in Deep Reinforcement Learning”, 12/18/2020, 24 pages cited in IDS dated 05/11/2023), Brennan et al. (US 20180075368 A1), and Balakrishnan et al. (US 20190299978 A1).
As per independent claim 1, Gou teaches a computer implemented method for analyzing at least one reinforcement learning agent based on an artificial neural network (e.g. in paragraph 4, “reinforcement learning (RL) model may be intended to train an agent (e.g., a programmed actor and/or the like) to perform actions within an environment to achieve a desired goal. For example, a deep Q-network (DQN) model may include a neural network (e.g. a deep convolutional neural network and/or the like)”; note: not an actual brain, i.e. “artificial”), the method comprising:
acquiring data of a plurality of runs of the artificial neural network (e.g. in paragraphs 9, 115, and 161, “for each epoch of a first predetermined number of epochs, performing a second predetermined number of training iterations and a third predetermined number of testing iterations using a first neural network… determining one or more patterns based on segments of iterations (e.g., testing and/or training iterations)… the event sequences over time or at a particular time step (e.g., iteration)”);
processing the acquired data using a method to obtain data (e.g. in paragraphs 115 and 161, “analytic framework to help interpret behavior, enhance understanding, provide insight, and/or the like of a neural network (and/or an agent including and/or using such a neural network)… quantitatively summarize the event sequences over time or at a particular time step (e.g., iteration)”);
dividing the data into a plurality of groups (e.g. in paragraph 143, “at least one pattern (e.g., a first pattern, a second pattern, etc.) may be determined based on the training and/or testing iterations…divided into segments… one or more patterns may be determined by clustering the segments by any suitable clustering technique and/or algorithm… a user may observe/inspect (e.g., as further described below) the clusters and identify/define patterns based on the observation”); and
analyzing the artificial neural network based on the plurality of groups of the data and fixing at least one of: errors in the artificial neural network, or errors in the input data to the artificial neural network (e.g. in paragraphs 115, 140, 143, 154, 161, and 173, “provide a visual analytic framework to help interpret behavior, enhance understanding, provide insight, and/or the like of a neural network (and/or an agent including and/or using such a neural network)… multiple visual depictions (e.g., charts, graphs, and/or the like) of the iterations (e.g., testing iterations) and/or statistics thereof as well as visual depictions of subsets (e.g., epochs, episodes, segments, and/or the like) of the iterations and/or statistics thereof may be displayed… a user may observe/inspect (e.g., as further described below) the clusters and identify/define patterns based on the observation” including “a user may observe patterns and make adjustments (e.g., to hyperparameters) to improve the neural network (and/or an agent including and/or using such a neural network)… adjusting the first set of parameters to…reduce (e.g., minimize) a loss, error, or difference between the predicted reward and a target reward… Through iterative trainings, the agent 520 may become increasingly intelligent”),
but does not specifically teach wherein the method comprises an attributation method and the data comprises attributation data, analyzing the artificial neural network based on the plurality of groups by at least one of: detecting errors in the artificial neural network, or detecting errors in input data to the artificial neural network; and fixing at least one of: the detected errors; wherein the at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle.
However, Heuillet teaches a method comprising an attributation method to obtain data comprising attributation data (e.g. in pages 1, 5-6, 11, and 17, “explain a deep neural network… provide explanations of an RL algorithm after its training, such as SHAP (SHapley Additive exPlanations)… Shapley Q-values Deep Deterministic Policy Gradient… it is worth noting that all presented methods decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration... proposed approaches (such as such as Integrated Gradients [111] based on the continuous extension of Shapley value”; note: “attributation” does not appear to be a word defined in a dictionary; while applicant can act as his or her own lexicographer, it is noted that the specification only generally describes what attributation “may be” [e.g. in page 3], but does not specifically define the term; applicant may be referring to “attribution” as page 10 describes integrated gradients as an example of “attributation” in the context of the article “Axiomatic Attribution for Deep Networks”; for the purposes of examination, the term “attributation” is broadly interpreted to include any element(s) that is attributed to other element(s), or integrated gradients, etc.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Gou to include the teachings of Heuillet because one of ordinary skill in the art would have recognized the benefit of further facilitating explanation of neural network(s)/agent behavior (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
but does not specifically teach analyzing the artificial neural network based on the plurality of groups by at least one of: detecting errors in the artificial neural network, or detecting errors in input data to the artificial neural network; and fixing at least one of: the detected errors; wherein the at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle.
However, Brennan teaches analyzing a neural network based on a plurality of groups by at least one of detecting errors in the artificial neural network or detecting errors in input data to the artificial neural network and fixing at least one of the detected errors in the artificial neural network or detected errors in the input data to the artificial neural network (e.g. in paragraphs 39 and 52, “the disclosed ground truth verification scheme efficiently clusters entity/relationship instances [i.e. groups] from a machine-annotated ground truth for batch verification to maximize the use of SME time by prioritizing clusters with high misclassification rates by using confusion matrices to identify candidate features from commonly confused examples to further hone the accuracy in flagging clusters for human verification using a browser-based ground truth verification interface window to efficiently verify, add or remove, either individually or in bulk (e.g., by cluster)… editing selected instances, removing selected instances, removing an entire cluster [i.e. fix], and/or leaving the training set unchanged”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Brennan because one of ordinary skill in the art would have recognized the benefit of improving training.
but does not specifically teach wherein the at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle.
However, Balakrishnan teaches at least one reinforcement learning agent executes driving maneuvers of a vehicle and controls acceleration of the vehicle (e.g. in paragraphs 19, 31, and 50-51, “automatically navigating an autonomous vehicle using deep reinforcement learning in accordance with the invention may guide an autonomous vehicle from an initial location to a desired target location in a step-by-step process [i.e. maneuvers]. In certain embodiments, steering angle, brake and accelerator values may be calculated [i.e. controls acceleration] in situ by an onboard neural network… automatic maneuvering… avoid the collision”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Balakrishnan because one of ordinary skill in the art would have recognized the benefit of facilitating automated driving.
As per claim 2, the rejection of claim 1 is incorporated and the combination further teaches wherein the data is acquired during at least one of: a real-life run or a simulated run (e.g. Gou, in paragraphs 9, 115, and 161, “for each epoch of a first predetermined number of epochs, performing a second predetermined number of training iterations and a third predetermined number of testing iterations using a first neural network… determining one or more patterns based on segments of iterations (e.g., testing and/or training iterations)… the event sequences over time or at a particular time step (e.g., iteration)”).
As per claim 7, the rejection of claim 1 is incorporated and the combination further teaches wherein the attributation method comprises at least one of: Integrated Gradients; DeepLIFT; Gradient SHAP; or Guided Backpropagation and Deconvolution (e.g. Heuillet, in pages 5-6, 11, and 17, “provide explanations of an RL algorithm after its training, such as SHAP (SHapley Additive exPlanations)… Shapley Q-values Deep Deterministic Policy Gradient… it is worth noting that all presented methods decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration... proposed approaches (such as such as Integrated Gradients [111] based on the continuous extension of Shapley value”).
As per claim 8, the rejection of claim 1 is incorporated and the combination further teaches dividing the attributation data into a plurality of groups, wherein the artificial neural network is analyzed based on the plurality of groups (e.g. Gou, in paragraphs 115, 138, and 156, “multiple visual depictions (e.g., charts, graphs, and/or the like) of the iterations (e.g., testing iterations) and/or statistics thereof as well as visual depictions of subsets (e.g., epochs, episodes, segments, and/or the like) of the iterations and/or statistics thereof may be displayed… Data associated with each iteration may be grouped in a tuple” and “a user may observe patterns and make adjustments (e.g., to hyperparameters) to improve the neural network (and/or an agent including and/or using such a neural network)”, and/or “visual analytic system may be used, e.g., to help a user in understanding the experiences of a DQN agent 520 in multiple levels (e.g., four or five different levels)… overall training level, epoch level, episode level, and segment level”, i.e. different groups; Heuillet, in pages 1, 5-6 and 17, “explain a deep neural network… decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration”; Brennan, in paragraphs 39 and 52, “clusters entity/relationship instances… flagging clusters”).
As per claim 9, the rejection of claim 1 is incorporated and the combination further teaches determining a correlation between parameters, attributation related to the parameters, and an output of the artificial neural network (e.g. Heuillet, in pages 1, 5-6 and 17, “explain a deep neural network (DNN) output… decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration”).
As per claim 12, the rejection of claim 1 is incorporated and the combination further teaches wherein the computer implemented method is applied to a motion planning module (e.g. Heuillet, in page 11, “navigation task… planning module, used for route planning”).
As per claim 15, the rejection of claim 1 is incorporated and the combination further teaches determining a correlation between parameters, attributation related to the parameters, and an output of the artificial neural network (e.g. Heuillet, in pages 1, 5-6 and 17, “explain a deep neural network (DNN) output… decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration”).
As per claim 18, the rejection of claim 1 is incorporated and the combination further teaches wherein the computer implemented method provides a local and post-hoc method for explaining the artificial neural network (e.g. Heuillet, pages 4-6 and 13, “local… Post-Hoc explainability”).
Claim 19 is the system claim corresponding to method claim 1 and is rejected under the same reasons set forth, and the combination further teaches a plurality of computer hardware components (e.g. Gou, in paragraphs 125-127, “processor 204, memory 20”, etc.).
Claim 20 is the medium claim corresponding to method claim 1 and is rejected under the same reasons set forth, and the combination further teaches a non-transitory computer readable medium comprising instructions that, when executed, configure computer hardware components (e.g. Gou, in paragraphs 125-127, “processor 204, memory 20…that stores information and/or instructions for use by processor… a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk”, etc.).
As per claim 22, the rejection of claim 1 is incorporated and the combination further teaches wherein controlling the acceleration of the vehicle is further based on at least one of: driving as fast as possible within a speed limit applicable to the vehicle; keeping a safe distance to another vehicle ahead of the vehicle; and increasing comfort by minimizing jerks; and avoiding collisions (e.g. Balakrishna, in paragraph 19, 31, and 50-51, “steering angle, brake and accelerator values may be calculated [i.e. controls acceleration] in situ by an onboard neural network… automatic maneuvering… avoid the collision”).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Gou et al. (US 20190303765 A1) in view of Heuillet et al. (“Explainability in Deep Reinforcement Learning”, 12/18/2020, 24 pages cited in IDS dated 05/11/2023), Brennan et al. (US 20180075368 A1), and Balakrishnan et al. (US 20190299978 A1) and further in view of Sundararajan et al. (“Axiomatic Attribution for Deep Networks”, 06/13/2017, 11 pages cited in IDS dated 05/11/2023).
As per claim 3, the rejection of claim 2 is incorporated, but the combination does not specifically teach wherein the attributation method is based on determining a gradient with respect to input data along a path from a baseline to the input data. However, Sundararajan teaches determining a gradient with respect to input data along a path from a baseline to the input data (e.g. in page 3, “We consider the straightline path (in Rn) from the baseline x' to the input x, and compute the gradients at all points along the path. Integrated gradients are obtained by cumulating these gradients”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Sundararajan because one of ordinary skill in the art would have recognized the benefit of incorporating well-known integrated gradient calculation (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 4, the rejection of claim 1 is incorporated, but the combination does not specifically teach wherein the attributation method is based on determining a gradient with respect to input data along a path from a baseline to the input data. However, Sundararajan teaches determining a gradient with respect to input data along a path from a baseline to the input data (e.g. in page 3, “We consider the straightline path (in Rn) from the baseline x' to the input x, and compute the gradients at all points along the path. Integrated gradients are obtained by cumulating these gradients”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Sundararajan because one of ordinary skill in the art would have recognized the benefit of incorporating well-known integrated gradient calculations (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 5, the rejection of claim 4 is incorporated and the combination further teaches wherein the baseline represents a general reference to all possible inputs (e.g. Sundararajan, in page 3, “We consider the straightline path (in Rn) from the baseline x' to the input x, and compute the gradients at all points along the path”, i.e. calculates all possible inputs x to a general reference, which is called “baseline”).
As per claim 6, the rejection of claim 4 is incorporated and the combination further teaches wherein the attributation method comprises at least one of: Integrated Gradients; DeepLIFT; Gradient SHAP; or Guided Backpropagation and Deconvolution (e.g. Heuillet, in pages 5-6, 11, and 17, “provide explanations of an RL algorithm after its training, such as SHAP (SHapley Additive exPlanations)… Shapley Q-values Deep Deterministic Policy Gradient… it is worth noting that all presented methods decompose final prediction into additive components attributed to particular features [109], and thus interaction between features should be accounted for, and included in the explanation elaboration... proposed approaches (such as such as Integrated Gradients [111] based on the continuous extension of Shapley value”; Sundararajan, in page 3, “Integrated Gradients”).
Claims 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gou et al. (US 20190303765 A1) in view of Heuillet et al. (“Explainability in Deep Reinforcement Learning”, 12/18/2020, 24 pages cited in IDS dated 05/11/2023), Brennan et al. (US 20180075368 A1), and Balakrishnan et al. (US 20190299978 A1) and further in view of Stephens et al. (US 20210133742 A1).
As per claim 10, the rejection of claim 9 is incorporated, but the combination does not specifically teach wherein the correlation comprises at least one of: a Pearson correlation coefficient; or a Spearman’s rank correlation coefficient. However, Stephens teaches a correlation comprising at least one of a Pearson correlation coefficient or a Spearman’s rank correlation coefficient (e.g. in page 3, “A feature may be defined as correlated with another when a measure of the correlation exceeds a threshold value... measure may be any known parameter for quantifying correlation, e.g. Pearson product-moment correlation coefficient, Spearman's rank correlation coefficient, etc.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Stephens because one of ordinary skill in the art would have recognized the benefit of incorporating well-known correlation calculations (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 16, the rejection of claim 15 is incorporated, but the combination does not specifically teach wherein the correlation comprises at least one of: a Pearson correlation coefficient; or a Spearman’s rank correlation coefficient. However, Stephens teaches a correlation comprising at least one of a Pearson correlation coefficient or a Spearman’s rank correlation coefficient (e.g. in page 3, “A feature may be defined as correlated with another when a measure of the correlation exceeds a threshold value... measure may be any known parameter for quantifying correlation, e.g. Pearson product-moment correlation coefficient, Spearman's rank correlation coefficient, etc.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Stephens because one of ordinary skill in the art would have recognized the benefit of incorporating well-known correlation calculations (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gou et al. (US 20190303765 A1) in view of Heuillet et al. (“Explainability in Deep Reinforcement Learning”, 12/18/2020, 24 pages cited in IDS dated 05/11/2023), Brennan et al. (US 20180075368 A1), and Balakrishnan et al. (US 20190299978 A1) and further in view of Kobilarov et al. (US 10133275 B1).
As per claim 21, the rejection of claim 1 is incorporated, but the combination does not specifically teach wherein the driving maneuvers of the vehicle include at least one of: a follow lane maneuver; a prepare for lane change maneuver; a lane change maneuver; and an abort maneuver. However, Kobilarov teaches at least one of a follow lane maneuver, a prepare for lane change maneuver, a lane change maneuver, and an abort maneuver (e.g. in column 4 lines 14-48, column 6 lines 40-53, and column 9 lines 56-67, “based on a current state of an environment, the MCTS, coupled with machine learning for action exploration and selection, can determine candidate trajectories that are most likely to result in satisfactory outcomes based on learned low-level policies (e.g., how to travel in a road lane, how to change lanes, how to stop, how not to tailgate, etc.)… stay within a lane”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Kobilarov because one of ordinary skill in the art would have recognized the benefit of incorporating well-known driving maneuvers (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Pai et al. (US 20200279140 A1) teaches “after a first few rounds of training, the model training system 102 may cause a user interface to be displayed to a client device, which graphically illustrates local and/or global explanation scores for each feature of one or more classes or test points. In this way, for example, a machine learning model developer can visually identify any potential biases or other problems in the data such that the machine learning model can be modified if needed” (e.g. in paragraph 51).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
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/W.W/Examiner, Art Unit 2144 08/12/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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