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 .
DETAILED ACTION
This action is responsive to the Application filed on 09/21/2026
Claims 1-20 are pending in this case. Claims 1, 11 and 20 are independent claims. Claims 1, 9, 11, 15 and 19-20 have been currently amended.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-8, 10-14, 16-18 and 20 are rejected under 35 U.S.C. 103 as beingUnpatentable over “Distilling a Neural Network into a soft decision tree".https://arxiv.org/pdf/1711.09784, Frost et al., 11/27/2017, hereinafter referred to as Frosst in view of “Multi-Layered Gradient Boosting Decision Trees”, https://arxiv.org/pdf/1806.00007, Feng et al., 05/31/2018, hereinafter referred to as Feng.
With respect to claim 1, Frosst disclose:
A system for automatic conversion of a target machine learning model into differentiable cascading decision tree (CDT)data structure adapted for improved machine learning explainability, the system comprising: a computer processor operating in conjunction with computer memory and a non-transitory computer readable medium, the computer processor configured to (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a deep neural net into a differentiable decision tree.)
Instantiate the cascading differentiable tree data structure having at least one feature learning tree data structure and a decision making tree data structure, the cascading differentiable tree data structure [[is]] capable of generating an output distribution data structure based on processing a set of data values representing a function of raw features x (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a differentiable decision tree, where each inner node has a learned filter and a bias, and each leaf node has a learned distribution. Fig. 1 & page 3 disclose a differentiable decision tree that can produce an output distribution based on learnable features f representing a corresponding
function sigma (wx+b). Fig. 2 & page 5 further disclose the cascading order of a soft decision tree.)
Iteratively train, until convergence. the cascading differentiable tree data structure against the target machine learning model to mimic representations of the target machine learning model: wherein each of the at least one feature learning tree data structure includes one or more decision nodes that represent the function #(x; w) of raw features x given a set of parameters w and one or more leaf nodes that each represent an intermediate learned feature f based at least on a corresponding intermediate feature representation function f =f(x;w) (In Fig. 1 and page 3, Frosst discloses that each of the at least one feature learning tree data structures includes one or more decision nodes that represent the function sigma(wx+b) of the learnable feature. The soft decision tree is iteratively training the targeted machine learning model to mimic representations using a loss function that seeks to minimize the cross entropy between each leaf, weighted by its path probability, and the target distribution.
Wherein the decision making tree data structure includes one or more decision nodes each representing a corresponding function 0(f; w') of intermediate learned features f given parameters w' from the feature representation functions of the feature learning tree data structure (Fig. 1 & page 3, Frosst disclose a decision tree including one or more nodes each representing a corresponding function sigma(wx+b) of the learnable features given output distribution of Ql or Qr.)
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure (In Fig.2 , Frosst disclose the feature learning tree is connected in a series with the soft decision tree)
With respect to claim 1, Frosst does not explicitly disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w'
However, it is known by Feng to disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure (In Fig. 1 and pages 4–5 (The Proposed Method), Feng discloses a multilayered gradient-boosting decision tree architecture comprising a plurality of sequentially connected tree-based learning layers. Feng teaches that each layer implements a mapping F_i that receives the output representation of the preceding layer and generates a corresponding intermediate representation, which is subsequently provided as input to an succeeding layer. Furthermore, Feng disclosed that the intermediate representation generated by the preceding layers is progressively supplied to a final output layer for generating a prediction.)
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w' (On pages 4 and 7, Feng teaches interactive training of multilayered tree-based architecture using gradient of loss function to determine pseudo-residuals, fitting regression trees to the pseudo-residuals, and updating the corresponding forward mappings through additive boosting operation (See Algorithm 1). Furthermore, Feng disclosed that when each Fi is parametric and differentiable, such a learning task can be achieved in an efficient way using back-propagation. The basic routine is to calculate the gradients of the loss function with respect to each parameter in each layer using the chain rule, and then perform gradient descent for parameter updates. )
Frosst, in view of Feng, they are analogous pieces of art because both references concern Gradient Boosting Decision Trees, which are decision tree ensemble methods exploiting deep cascade structures and using a cross-entropy-based feature representation. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to introduce predictions of a complex deep neural network into a single, easy-to-understand decision tree as taught by Frosst, with stochastic gradient descent as the workhorse behind the scene for updating parameters during training as taught by Feng. The motivation for doing so would have been to improve the interpretability of deep neural networks (CNNs) and the generalization of standard decision trees of Frosst.
Regarding claim 2, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein the cascading differentiable tree data structure is adapted to distill the target machine learning architecture by training the cascading differentiable tree data structure based on the target machine learning architecture to mimic an input output function of the target machine learning architecture (Frosst disclose distilling a neural network into a soft decision tree.)
Regarding claim 3, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The method of claim 1, wherein depth values of the two or more feature learning tree data structures is a tunable parameter to adjust a balance between the two or more feature learning tree data structures (On page 4, first paragraph, Frosst disclose the size of the tree and then using mini-batch gradient descent to update all of their parameters simultaneously.)
Regarding claim 4, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein there are two or more feature learning tree data structures that are coupled in parallel such that intermediate learned features of a both feature learning tree data structures is concatenated and the concatenation of the intermediate learned features is provided to the decision making tree data structure (In Fig. 2, Frost disclosed two or more feature learning trees that work together at the same time. On page 5 (MINIST Results). The features learned from both trees are combined and then given to the soft decision-making tree.)
Regarding claim 6, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein the functions #(x; w), f =f(x; w), and 0(f; w') are linear functions (Fig. 1, Frosst discloses a linear function sigma(wx+b))
Regarding claim 7, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein the cascading differentiable tree data structure is a discretized cascading differentiable tree data structure (In Fig. 2, Frosst disclose the process of converting continuous features into discrete ones, using a soft decision tree.)
Regarding claim 8, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 7, wherein only the at least one feature learning tree data structure is discretized (In Fig. 2 and Page 5, Frosst disclose the feature learning tree structure being discretized. )
Regarding claim 10, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein each leaf node of the at least one feature learning tree data structure represents one possible assignment of intermediate feature values for a total of L possibilities, and during an inference process, the leaf node with the largest probability is used to assign values for the intermediate features (On page 3, Frosst disclose averaging the distribution over all the leaves, weighted by the respective path probabilities. The predictive distribution from the leaf with the greatest path probability, the explanation for that prediction is simply the list of all the filters along the path from the route to the leaf together with the binary activation decisions.)
With respect to claim 11, Frosst disclose:
A method for automatic conversion of a target machine learning model into differentiable cascading decision tree (CDT)data structure adapted for improved machine learning explainability, the system comprising: a computer processor operating in conjunction with computer memory and a non-transitory computer readable medium, the computer processor configured to (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a deep neural net into a differentiable decision tree.)
Instantiating the cascading differentiable tree data structure having at least one feature learning tree data structure and a decision making tree data structure, the cascading differentiable tree data structure [[is]] capable of generating an output distribution data structure based on processing a set of data values representing a function of raw features x (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a differentiable decision tree, where each inner node has a learned filter and a bias, and each leaf node has a learned distribution. Fig. 1 & page 3 disclose a differentiable decision tree that can produce an output distribution based on learnable features f representing a corresponding function sigma (wx+b). Fig. 2 & page 5 further disclose the cascading order of a soft decision tree.)
Iteratively train, until convergence. the cascading differentiable tree data structure against the target machine learning model to mimic representations of the target machine learning model: wherein each of the at least one feature learning tree data structure includes one or more decision nodes that represent the function #(x; w) of raw features x given a set of parameters w and one or more leaf nodes that each represent an intermediate learned feature f based at least on a corresponding intermediate feature representation function f =f(x;w) (In Fig. 1 and page 3, Frosst discloses that each of the at least one feature learning tree data structures includes one or more decision nodes that represent the function sigma(wx+b) of the learnable feature. The soft decision tree is iteratively training the targeted machine learning model to mimic representations using a loss function that seeks to minimize the cross entropy between each leaf, weighted by its path probability, and the target distribution.
Wherein the decision making tree data structure includes one or more decision nodes each representing a corresponding function 0(f; w') of intermediate learned features f given parameters w' from the feature representation functions of the feature learning tree data structure (Fig. 1 & page 3, Frosst disclose a decision tree including one or more nodes each representing a corresponding function sigma(wx+b) of the learnable features given output distribution of Ql or Qr.)
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure (In Fig.2 , Frosst disclose the feature learning tree is connected in a series with the soft decision tree)
With respect to claim 1, Frosst does not explicitly disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w'
However, it is known by Feng to disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure (In Fig. 1 and pages 4–5 (The Proposed Method), Feng discloses a multilayered gradient-boosting decision tree architecture comprising a plurality of sequentially connected tree-based learning layers. Feng teaches that each layer implements a mapping F_i that receives the output representation of the preceding layer and generates a corresponding intermediate representation, which is subsequently provided as input to an succeeding layer. Furthermore, Feng disclosed that the intermediate representation generated by the preceding layers is progressively supplied to a final output layer for generating a prediction.)
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w' (On pages 4 and 7, Feng teaches interactive training of multilayered tree-based architecture using gradient of loss function to determine pseudo-residuals, fitting regression trees to the pseudo-residuals, and updating the corresponding forward mappings through additive boosting operation (See Algorithm 1). Furthermore, Feng disclosed that when each Fi is parametric and differentiable, such a learning task can be achieved in an efficient way using back-propagation. The basic routine is to calculate the gradients of the loss function with respect to each parameter in each layer using the chain rule, and then perform gradient descent for parameter updates. )
Frosst, in view of Feng, they are analogous pieces of art because both references concern Gradient Boosting Decision Trees, which are decision tree ensemble methods exploiting deep cascade structures and using a cross-entropy-based feature representation. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to introduce predictions of a complex deep neural network into a single, easy-to-understand decision tree as taught by Frosst, with stochastic gradient descent as the workhorse behind the scene for updating parameters during training as taught by Feng. The motivation for doing so would have been to improve the interpretability of deep neural networks (CNNs) and the generalization of standard decision trees of Frosst.
Regarding claim 12, Frosst in view of Feng disclose the elements of claim 11. In addition, Frosst disclose:
The method of claim 11, wherein the cascading differentiable tree data structure is adapted to distill the target machine learning architecture by training the cascading differentiable tree data structure based on the target machine learning architecture to mimic an input-output function of the target machine learning architecture (Frosst disclose distilling a neural network into a soft decision tree.)
Regarding claim 13, Frosst in view of Feng disclose the elements of claim 11. In addition, Frosst disclose:
The method of claim 11, wherein depth values of the two or more feature learning tree data structures is a tunable parameter to adjust a balance between the two or more feature learning tree data structure (On page 4, first paragraph, Frosst disclose the size of the tree and then using mini-batch gradient descent to update all of their parameters simultaneously.)
Regarding claim 14, Frosst in view of Feng disclose the elements of claim 11. In addition, Frosst disclose:
The method of claim 11, wherein there are two or more feature learning tree data structures that are coupled in parallel such that intermediate learned features of a both feature learning tree data structures is concatenated and the concatenation of the intermediate learned features is provided to the decision making tree data structure (In Fig. 2, Frosst disclosed two or more feature learning trees that work together at the same time. On page 5 (MINIST Results). The features learned from both trees are combined and then given to the soft decision-making tree.)
Regarding claim 16, Frosst in view of Feng disclose the elements of claim 11. In addition, Frosst disclose:
The method of claim 11, wherein the functions #(x; w), f =f(x; w), and 0(f; w') are linear functions (Fig. 1, Frosst discloses a linear function sigma(wx+b))
Regarding claim 17, Frosst in view of Feng disclose the elements of claim 1. In addition, Frosst disclose:
The system of claim 1, wherein the cascading differentiable tree data structure is a discretized cascading differentiable tree data structure (In Fig. 2, Frosst disclose the process of converting continuous features into discrete ones, using a soft decision tree.)
Regarding claim 18, Frosst in view of Feng disclose the elements of claim 17. In addition, Frosst disclose:
The system of claim 17, wherein only the at least one feature learning tree data structure is discretized (In Fig. 2 and Page 5, Frosst disclose the feature learning tree structure being discretized. )
With respect to claim 20, Frosst disclose:
A non-transitory computer readable medium storing machine interpretable instructions, which when executed by a processor, cause the processor to perform steps of a method for automatic conversion of a target machine learning model into differentiable cascading decision tree (CDT)data structure adapted for improved machine learning explainability, the system comprising: a computer processor operating in conjunction with computer memory and a non-transitory computer readable medium, the computer processor configured to (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a deep neural net into a differentiable decision tree.)
Instantiating the cascading differentiable tree data structure having at least one feature learning tree data structure and a decision making tree data structure, the cascading differentiable tree data structure [[is]] capable of generating an output distribution data structure based on processing a set of data values representing a function of raw features x (On page 2, The Hierarchical Mixture of Bigots, Frosst discloses a differentiable decision tree, where each inner node has a learned filter and a bias, and each leaf node has a learned distribution. Fig. 1 & page 3 disclose a differentiable decision tree that can produce an output distribution based on learnable features f representing a corresponding function sigma (wx+b). Fig. 2 & page 5 further disclose the cascading order of a soft decision tree.)
Iteratively train, until convergence. the cascading differentiable tree data structure against the target machine learning model to mimic representations of the target machine learning model: wherein each of the at least one feature learning tree data structure includes one or more decision nodes that represent the function #(x; w) of raw features x given a set of parameters w and one or more leaf nodes that each represent an intermediate learned feature f based at least on a corresponding intermediate feature representation function f =f(x;w) (In Fig. 1 and page 3, Frosst discloses that each of the at least one feature learning tree data structures includes one or more decision nodes that represent the function sigma(wx+b) of the learnable feature. The soft decision tree is iteratively training the targeted machine learning model to mimic representations using a loss function that seeks to minimize the cross entropy between each leaf, weighted by its path probability, and the target distribution.
Wherein the decision making tree data structure includes one or more decision nodes each representing a corresponding function 0(f; w') of intermediate learned features f given parameters w' from the feature representation functions of the feature learning tree data structure (Fig. 1 & page 3, Frosst disclose a decision tree including one or more nodes each representing a corresponding function sigma(wx+b) of the learnable features given output distribution of Ql or Qr.)
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure (In Fig.2 , Frosst disclose the feature learning tree is connected in a series with the soft decision tree.)
With respect to claim 20, Frosst does not explicitly disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w'
However, it is known by Feng to disclose:
Wherein the at least one feature learning tree data structure is coupled in a cascaded arrangement with the decision making tree data structure, and wherein there are two or more feature learning tree data structures that are coupled in the cascaded arrangement such that an intermediate learned feature f of a preceding feature learning tree data structures is provided as a raw input into a subsequent feature learning tree data structure. and the intermediate learned feature f of a final feature learning tree data structure is then provided to the decision making tree data structure (In Fig. 1 and pages 4–5 (The Proposed Method), Feng discloses a multilayered gradient-boosting decision tree architecture comprising a plurality of sequentially connected tree-based learning layers. Feng teaches that each layer implements a mapping F_i that receives the output representation of the preceding layer and generates a corresponding intermediate representation, which is subsequently provided as input to an succeeding layer. Furthermore, Feng disclosed that the intermediate representation generated by the preceding layers is progressively supplied to a final output layer for generating a prediction.)
Wherein the two or more feature learning tree data structures and the decision making tree data structure are trained by gradient-based updating of the parameters w and w' (On pages 4 and 7, Feng teaches interactive training of multilayered tree-based architecture using gradient of loss function to determine pseudo-residuals, fitting regression trees to the pseudo-residuals, and updating the corresponding forward mappings through additive boosting operation (See Algorithm 1). Furthermore, Feng disclosed that when each Fi is parametric and differentiable, such a learning task can be achieved in an efficient way using back-propagation. The basic routine is to calculate the gradients of the loss function with respect to each parameter in each layer using the chain rule, and then perform gradient descent for parameter updates. )
Frosst, in view of Feng, they are analogous pieces of art because both references concern Gradient Boosting Decision Trees, which are decision tree ensemble methods exploiting deep cascade structures and using a cross-entropy-based feature representation. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to introduce predictions of a complex deep neural network into a single, easy-to-understand decision tree as taught by Frosst, with stochastic gradient descent as the workhorse behind the scene for updating parameters during training as taught by Feng. The motivation for doing so would have been to improve the interpretability of deep neural networks (CNNs) and the generalization of standard decision trees of Frosst.
Claims 5, 9, 15 and 19 are rejected under 35 U.S.C. 103 as being unpalatable over Frosst in view of Feng and further in view of “Deep Neural Decision Forests”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410529, 2015, hereinafter referred to as Peter.
Regarding claim 5, Frosst in view of Feng disclose the elements of claim 1. Frosst in view of Feng do not explicitly disclose:
The system of claim 1, wherein the decision making tree data structure is utilized to process a perturbed set of intermediate feature representation functions to generate a new output distribution data structure, the perturbed set of feature representation functions being perturbed to modify an explainable parameter represented in the perturbed set of intermediate feature representation functions
However, Peter disclose the limitation (On page 2, Peter discloses a deep neural decision forest architecture comprising a neural network feature architecture comprising a neural-network feature representation component coupled to a differentiable decision-tree structure (See Fig. 2). The learned feature representation is supplied to decision-tree nodes to determine probabilistic routing through the tree. Peter teaches generating class-label probability distributions based on the routing probabilities and probability distributions associated with the leaf nodes (See section 3, Eq 1-3).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Frosst and Feng to include Peters, with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner as taught by Peter. The motivation for doing so would have been to computational efficiency (See (Page 7, paragraph 3-4) of Peter.)
Regarding claim 9, Frosst in view of Feng disclose the elements of claim 1. Frosst in view of Feng do not explicitly disclose:
The system of claim 1, wherein the cascading differentiable tree data structure is utilized in a random forest of decision tree data structures, and generated outputs of the random forest are utilized in concert to determine an aggregated output distribution data structure
However, Peter disclose the limitation (On page 5 and Fig. 2, Peter discloses a decision forest comprising a plurality of differentiable decision trees, wherein each decision tree generates a prediction probability distribution and the individual predictions are averaged to produce an aggregated forest prediction (See Equation 4).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Frosst and Feng to include Peters, with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner as taught by Peter. The motivation for doing so would have been to computational efficiency (See (Page 7, paragraph 3-4) of Peter.)
Regarding claim 15, Frosst in view of Feng disclose the elements of claim 11. Frosst in view of Feng do not explicitly disclose:
The method of claim 11, wherein the decision making tree data structure is utilized to process a perturbed set of intermediate feature representation functions to generate a new output distribution data structure, the perturbed set of feature representation functions being perturbed to modify an explainable parameter represented in the perturbed set of intermediate feature representation functions
However, Peter disclose the limitation (On page 2, Peter discloses a deep neural decision forest architecture comprising a neural network feature architecture comprising a neural-network feature representation component coupled to a differentiable decision-tree structure (See Fig. 2). The learned feature representation is supplied to decision-tree nodes to determine probabilistic routing through the tree. Peter teaches generating class-label probability distributions based on the routing probabilities and probability distributions associated with the leaf nodes (See section 3, Eq 1-3).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Frosst and Feng to include Peters, with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner as taught by Peter. The motivation for doing so would have been to computational efficiency (See (Page 7, paragraph 3-4) of Peter.)
Regarding claim 19, Frosst in view of Feng disclose the elements of claim 11. Frosst in view of Feng do not explicitly disclose:
The method of claim 11, wherein the cascading differentiable tree data structure is utilized in a random forest of decision tree data structures, and generated outputs of the random forest are utilized in concert to determine an aggregated output distribution data structure
However, Feng disclose the limitation (On page 5 and Fig. 2, Peter discloses a decision forest comprising a plurality of differentiable decision trees, wherein each decision tree generates a prediction probability distribution and the individual predictions are averaged to produce an aggregated forest prediction (See Equation 4).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Frosst and Feng to include Peters, with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner as taught by Peter. The motivation for doing so would have been to computational efficiency (See (Page 7, paragraph 3-4) of Peter.)
Response to Arguments
The applicant's arguments filed 09/21/2026 have been fully considered, but in part are not persuasive.
Pertaining to Rejection under 101
Applicant’s argument in regard to 101 abstract idea is found persuasive and 101 abstract idea rejection is withdrawn.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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EVEL HONORE
Examiner
Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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