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 Office Action has been issued in response to Applicant’s Communication of application S/N 18/323,014 filed on May 24, 2023. Claims 1 to 20 are currently pending with the application.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent Application No. CN20211112640812, filed on 10/27/2021, and relationship to 371 PCT/CN2022/120080, filed on 09/21/2022.
Claim Objections
Claim 2-9, 12-18 and 20 are objected to because of the following informalities:
Claims 2-9 recites “The method”. Claim should include “federated learning” prior to “method”
Claims 11-18 recites “The device”. Claim should include “computer” prior to “device”
Claim 20 recites “The storage medium according to claim 19”. Claim should include “non-transitory” prior to “storage medium”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
Regarding claim 1
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
“determining at least one candidate feature from data features corresponding to a training data-set, the candidate feature corresponding to at least two decision trends in a decision tree model;”
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
“determining at least one second decision tree model from the n first decision tree models based on prediction results of the n first decision tree models on training data in the training data-set;”
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
“obtaining n first decision tree models by taking the at least one candidate feature as a model construction foundation, value of n corresponding to number of the at least one candidate feature;
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
“the second computing device being configured to fuse at least two decision tree models that comprise the second decision tree model to obtain a federated learning model,”
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “A federated learning method, performed by a first computing device and comprising:” as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation “transmitting the second decision tree model to a second computing device,” amounts to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claims 10 and 19
Claims 10 and 19 recite analogous limitations to independent claim 1 and therefore is rejected on the same ground as independent claim 1.
Regarding claim 2
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A
Claim 2. The method according to claim 1, wherein obtaining the n first decision tree models by taking the at least one candidate feature as the model construction foundation comprises: generating at least two leaf nodes based on the candidate feature and the decision trends; assigning values respectively to the at least two leaf nodes based on classification number of the decision tree models to obtain at least two leaf nodes marked with leaf node values; and constructing the n first decision tree models based on the candidate feature, the decision trends and the at least two leaf nodes marked with the leaf node values.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “method,” referring to the federated learning method performed by a first computing device , as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, The insignificant extra-solution activity identified above, which include the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claims are not patent eligible.
Thus, the claim is not patent eligible.
Regarding claims 11 and 20
Claims 11 and 20 recite analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 2.
Regarding claim 3
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
Claim 3. The method according to claim 2, wherein the decision tree model comprises a binary classification model; and assigning the values respectively to the at least two leaf nodes based on the classification number of the decision tree models to obtain the at least two leaf nodes marked with the leaf node values comprises: assigning values to the at least two leaf nodes based on a binary classification standard of a binary classification model to obtain the at least two leaf nodes marked with the leaf node values, the binary classification standard indicating that the leaf node has two assignment cases.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “method,” referring to the federated learning method performed by a first computing device , as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, The insignificant extra-solution activity identified above, which include the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claims are not patent eligible.
Thus, the claim is not patent eligible.
Regarding claim 12
Claim 12 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 3.
Regarding claim 4
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 4. The method according to claim 2, wherein generating the at least two leaf nodes based on the candidate feature and the decision trends comprises: using a first candidate feature of the at least one candidate feature as a root node of the decision tree model, the first candidate feature being a feature of the at least one candidate feature; and generating a leaf node having an association relationship with the root node based on the decision trends;
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
or, determining an associated node having an association relationship with the root node based on the decision trends corresponding to the root node, the associated node indicating a second candidate feature, the second candidate feature being a feature of the candidate features other than the first candidate feature;
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
and generating a leaf node having an association relationship with the associated node based on the decision trends corresponding to the associated node.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim does not recite additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 13
Claim 13 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 4.
Regarding claim 5
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 5. The method according to claim 2, wherein determining the at least one second decision tree model from the n first decision tree models based on the prediction results of the n first decision tree models on the training data in the training data-set comprises: inputting the training data in the training data-set into the first decision tree model, and determining a prediction label corresponding to the training data; matching the prediction label with a reference label of the training data to obtain a prediction result, the reference label indicating a reference classification case of the training data; and determining the at least one second decision tree model from the n first decision tree models based on the corresponding prediction results of the n first decision tree models for the training data.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 14
Claim 14 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 5.
Regarding claim 6
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 6. The method according to claim 5, wherein determining the at least one second decision tree model from the n first decision tree models based on the corresponding prediction results of the n first decision tree models for the training data comprises: determining matching scores respectively corresponding to the n first decision tree models based on the corresponding prediction results of the n first decision tree models for the training data; and determining the at least one second decision tree model based on the matching scores respectively corresponding to the n first decision tree models.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 15
Claim 15 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 6.
Regarding claim 7
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 7. The method according to claim 6, wherein determining the at least one second decision tree model based on the matching scores respectively corresponding to the n first decision tree models comprises: determining selection probabilities respectively corresponding to the n first decision tree models based on the matching scores, the selection probability indicating the probability that the first decision tree model is selected as the second decision tree model; and using the first decision tree model with the selection probability satisfying a preset probability condition as the second decision tree model.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “decision tree model”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding claim 16
Claim 16 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 7.
Regarding claim 8
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 8. The method according to claim 6, wherein the prediction result comprises a prediction success result and a prediction failure result; and determining the matching scores respectively corresponding to the n first decision tree models based on the corresponding prediction results of the n first decision tree models for the training data comprises: performing bonus evaluation on the first decision tree model corresponding to the prediction success result in response to the prediction result being the prediction success result to obtain the matching score; or performing retention evaluation on the first decision tree model corresponding to the prediction failure result in response to the prediction result being the prediction failure result to obtain the matching score.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical. In particular, the claim only recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The additional element of “decision tree model”, as drafted, is reciting generic computer components. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding claim 17
Claim 17 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 8.
Regarding claim 9
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1: The claim recites multiple mental processes, as explained below. The claim recites, inter alia:
Claim 9. The method according to claim 1, wherein determining the at least one candidate feature from the data features corresponding to the training data-set comprises: randomly selecting at least one data feature from the data features corresponding to the training data-set as the candidate feature; or selecting at least one data feature from the data features corresponding to the training data-set as the candidate feature based on an exponential mechanism.
This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A Prong 2: This judicial exception is not integrated into a practical. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 18
Claim 18 recites analogous limitations to independent claim 1 and therefore is rejected on the same ground as dependent claim 9.
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.
Claim 1-4, 9-13, 18, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Reese (US 11,093,864) Filed on Nov. 10, 2001 in view of Zhou et al. (US 2021/0234687) Filed on March 22, 2021 and further in view of Shao et al. (US 2023/0116117) filed on Dec 13, 2022.
As per Claims 1, 10 and 19, A federated learning method, performed by a first computing device and comprising: determining at least one candidate feature from data features corresponding to a training data-set, (See Col. 10 lines 35-40, wherein input variables are provided as training data sets; as taught by Reese)
the candidate feature corresponding to at least two decision trends in a decision tree model; (See Col. 10 lines 35-45, the input variables that are considered for splitting a node are randomly selected from all available input variables indicated in operation; as taught by Reese)
Reese does not explicitly disclose obtaining n first decision tree models by taking the at least one candidate feature as a model construction foundation, value of n corresponding to number of the at least one candidate feature; determining at least one second decision tree model from the n first decision tree models based on prediction results of the n first decision tree models on training data in the training data-set; and transmitting the second decision tree model to a second computing device, the second computing device being configured to fuse at least two decision tree models that comprise the second decision tree model to obtain a federated learning model.;
On the other hand Zhou discloses obtaining n first decision tree models by taking the at least one candidate feature as a model construction foundation, value of n corresponding to number of the at least one candidate feature; (See para.32-39 and 41-42, Trains multiple tree models per collaborator and uses them as feature extractors; each tree uses a feature of the input; as taught by Zhou)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Reese, by including the teachings of Zhou relating to the creating multiple models from input because both are directed to the art of training decision models, the mapping provides an improvement in the processing time and improves efficiency (as taught by Zhou see para.2)
The combination of Reese and Zhou fails to teach determining at least one second decision tree model from the n first decision tree models based on prediction results of the n first decision tree models on training data in the training data-set; and transmitting the second decision tree model to a second computing device, the second computing device being configured to fuse at least two decision tree models that comprise the second decision tree model to obtain a federated learning model.
On the other hand Shao teaches determining at least one second decision tree model from the n first decision tree models based on prediction results of the n first decision tree models on training data in the training data-set; (Paragraphs [0051]–[0055] (Third aspect): The second node sends a federated model that includes “a plurality of machine learning models,” and the first node “selects a target machine learning model from the plurality of machine learning models.” Selection is described as based on “degrees of matching between the local training data and the plurality of machine learning models” also see [0053]–[0054] This is the nearest analogue to choosing one model from many based on performance on training/validation (prediction results ; as taught by Shao).
and transmitting the second decision tree model to a second computing device, the second computing device being configured to fuse at least two decision tree models that comprise the second decision tree model to obtain a federated learning model.; (Paragraphs [0250]–[0251]: Server updates the prior distributions for each neural network in the federated model using weighted averages of client updates—another form of fusion across at least two models/updates; as taught by Shao)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Reese and Zhou, by including the teachings Shao of relating to the creation of federated learning method because both are directed to the art of training decision models, the mapping provides an improvement in by reducing training time (as taught by Show see para.5)
As per Claims 2 and 11, The method according to claim 1, the combination of Reese, Zhou and Shao teaches wherein obtaining the n first decision tree models by taking the at least one candidate feature as the model construction foundation comprises: generating at least two leaf nodes based on the candidate feature and the decision trends; (See col. 8, lines 61-67 and co.9, lines l-17, wherein the splitting results in two separate nodes including leaf nodes as taught by Reese)
assigning values respectively to the at least two leaf nodes based on classification number of the decision tree models to obtain at least two leaf nodes marked with leaf node values;
and constructing the n first decision tree models based on the candidate feature, the decision trends and the at least two leaf nodes marked with the leaf node values; (See col. 8, lines 61-67 and col.9, lines l-17, wherein the splitting results in two separate nodes including leaf nodes as taught by Reese)
As per Claims 3 and 12, The method according to claim 2, he combination of Reese, Zhou and Shao teaches wherein the decision tree model comprises a binary classification model; and assigning the values respectively to the at least two leaf nodes based on the classification number of the decision tree models to obtain the at least two leaf nodes marked with the leaf node values comprises:
assigning values to the at least two leaf nodes based on a binary classification standard of a binary classification model to obtain the at least two leaf nodes marked with the leaf node values, the binary classification standard indicating that the leaf node has two assignment cases; (See co..8, liens 55-60, describing the classify classification tree model. Also see column18, describing how the tree is a binary tree; as taught by Reese)
As per Claims 4 and 13, The method according to claim 2, the combination of Reese, Zhou and Shao teaches wherein generating the at least two leaf nodes based on the candidate feature and the decision trends comprises: using a first candidate feature of the at least one candidate feature as a root node of the decision tree model, the first candidate feature being a feature of the at least one candidate feature; (See col. 8, lines 61-67 and col.9, lines l-17, describing the decision tree creating a root node based on the input variable as taught by Reese)
and generating a leaf node having an association relationship with the root node based on the decision trends; (See col. 8, lines 61-67 and col.9, lines l-17, describing the decision tree creating a root node based on the input variable and creating split decision that can be leaf nodes based on the root node entered as taught by Reese) or, determining an associated node having an association relationship with the root node based on the decision trends corresponding to the root node, the associated node indicating a second candidate feature, the second candidate feature being a feature of the candidate features other than the first candidate feature; and generating a leaf node having an association relationship with the associated node based on the decision trends corresponding to the associated node.
As per Claims 9 and 18, The method according to claim 1, wherein determining the at least one candidate feature from the data features corresponding to the training data-set comprises: randomly selecting at least one data feature from the data features corresponding to the training data-set as the candidate feature; ( See col.10, lines 35-45, describing the random selection of variables; as taught by Reese) or selecting at least one data feature from the data features corresponding to the training data-set as the candidate feature based on an exponential mechanism.
Allowable Subject Matter
Claims 5-8 and 14-17 objected to as being dependent upon a rejected base claim, but would be allowable if, 1) applicant overcomes outstanding 35 USC 101 rejection and 2) the claims are rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169