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 is in response to the application filed on March 27, 2023.
Claims 1-21 are pending and have been examined. Claims 1-21 are rejected.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed March 27, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION – The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. § 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 14, and 21 are rejected under 35 U.S.C. 112(b) 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.
Regarding Claims 7, 14, and 21, the limitations "for each leaf node of the trained portion of the global decision tree: determining that the global decision tree should be extended at the leaf node" does not clearly set the metes and bounds of the patent protection desired. There is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because “each leaf node” and “the leaf node” are not previously defined in Claims 1, 8 and 15, which Claims 7, 14, and 21 are dependent on respectively.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(III) states: “Accordingly, the “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-21 are each directed to non-statutory subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-7 are directed to a method for training a global decision tree through a federated learning (FL) procedure.
Claims 8-14 are directed to a non-transitory computer-readable medium storing computer-executable instructions which, when executed by each client of a plurality of clients participating in an FL procedure, cause each client to perform a set of operations.
Claims 15-21 are directed to a computer system participating in an FL method with other participating computer systems, where each computer system is comprised of a processor, a training dataset inaccessible to other computer systems, and a non-transitory computer-readable medium storing computer-executable instructions which, when executed by the processor, cause the processor to perform a set of operations.
With respect to Claims 1, 8, and 15, which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 8 is directed to a non-transitory computer readable storage medium on which computer-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 15 is directed to a computer system comprised of a processor, training dataset, and a non-transitory computer readable medium having stored program code that is executed by the processor to perform a set of operations, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating, by said each client, a local decision tree using the training dataset”; Generating a local decision tree by clients participating in a federated learning procedure for training a global decision tree covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
“generating, by said each client, a set of items, wherein each item in the set of items corresponds to a subtree in the local decision tree and is associated with a payload comprising properties of nodes in the subtree;”; Generating a set of items, where each item corresponds to a subtree in a local decision tree and is associated with a payload, covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
“and determining, by said each client, a trained portion of the global decision tree based on an output of the QPSIA protocol”; Determining a trained portion of a global decision tree based on an output of a QPSIA protocol covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“executing, by said each client in collaboration with the other clients in the plurality of clients, a quorum private set intersection analytics (QPSIA) protocol, the executing including providing the set of items as input to the QPSIA protocol;”; The execution of a QPSIA protocol only amounts to “apply it” and mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). A set of items provided as input to the QPSIA protocol is regarded as a generic computer function of receiving input data. Mere data gathering is considered insignificant extra-solution activity – see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. QPSIA protocol execution only amounts to “apply it” and mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1). Providing input data is well-understood, routine, and conventional activity of transmitting or receiving data over a network - see MPEP 2106.05(d).
Therefore, Claims 1, 8, and 15 are directed to non-statutory subject matter and rejected.
With respect to Claims 2, 9, and 16, which have identical claim limitations and are dependent upon Claims 1, 8, and 15, respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes and mathematical calculations, which are abstract ideas:
“checking whether said each subtree meets a topology requirement;”; Checking whether a subtree meets a topology requirement covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
“and upon determining that said each subtree meets the topology requirement: computing a unique fingerprint for said each subtree,”; Determining that each subtree in a set meets the topology requirement covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement. Computing a unique fingerprint is a mathematical calculation where a hash value is computed so that a single and unique value can represent large datasets within a subtree.
“computing a payload for said each subtree”; Computing a payload for each subtree is a mathematical calculation which involves extracting specific information from a node through the traversal of a subtree and computing a value or statistic that is then used to represent that subtree.
“and adding the unique fingerprint and the payload as a new item to the set of items”; Adding a unique fingerprint and payload as a new item to a set of items is considered a mathematical calculation where changing the size (cardinality) of a set of items is done by a structural update through a mathematical operation.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 2, 9, and 16 are directed to non-statutory subject matter and rejected.
With respect to Claims 3, 10, and 17 which have identical claim limitations and are dependent upon Claims 2, 9, and 16, respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mathematical calculations, which are abstract ideas:
“wherein computing the unique fingerprint comprises computing a hash of a subset of the properties of the nodes in said each subtree”; Computing a hash of a subset of the properties of nodes in a subtree is a mathematical calculation which typically involves using a mathematical algorithm (hash function) to change input data into a fixed hash (fingerprint) value.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 3, 10, and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 4, 11, and 18, which have identical claim limitations and are dependent upon Claims 1, 8, and 17 respectively: Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“wherein the payload comprises, for each node in the subtree: a feature from the training dataset that is associated with said each node;”; Reciting the composition of a payload as a feature from a training dataset that is associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g).
“a verdict function that is associated with said each node;”; Reciting the composition of a payload as a verdict function that is associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g).
“and a size of a dataset that is associated with said each node”; Reciting the composition of a payload as a size of a dataset associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Describing the composition of a payload as a feature from a training dataset that is associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g). Describing the composition of a payload as a verdict function that is associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g). Describing the composition of a payload as a size of a dataset associated with each node is considered insignificant extra-solution activity because it is merely a nominal or tangential addition to the primary process and is well-understood and conventional – see MPEP 2106.05(g).
Therefore, Claims 4, 11, and 18 are directed to non-statutory subject matter and rejected.
With respect to Claims 5, 12, and 19, which have identical claim limitations and are dependent upon Claims 1, 8, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“and wherein the particular subtree is deemed to be a best subtree for the global decision tree by an analytics function of the QPSIA protocol”; Using an analytics function from a QPSIA protocol to judge whether a particular subtree is deemed to be the best subtree for a global decision tree covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“wherein the output of the QPSIA protocol is a particular subtree selected from among all subtrees that appear in at least a threshold number of sets provided as input to the QPSIA protocol”; Describing an output result, such as a particular subtree selected among all subtrees, is considered insignificant extra-solution activity because it is merely post-solution activity and is well-understood and conventional – see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Presenting output results (such as a selected subtree) are well-understood, routine, and conventional activities - see MPEP 2106.05(d).
Therefore, Claims 5, 12, and 19 are directed to non-statutory subject matter and rejected.
With respect to Claims 6, 13, and 20, which have identical claim limitations and are dependent upon Claims 5, 12, and 19, respectively: Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mathematical calculations, which are abstract ideas:
“wherein the analytics function determines that the particular subtree is the best subtree based on the payloads associated with the particular subtree in the sets”; An analytics function determining a best subtree from multiple subtrees based on payloads associated with the particular subtree in the sets is a mathematical calculation where the analytics function is computed via Quorum Private Set Intersection (QPSI).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 6, 13, and 20 are directed to non-statutory subject matter and rejected.
With respect to Claims 7, 14, and 21 which have identical claim limitations and are dependent upon Claims 1, 8, and 15, respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“The method of claim 1 further comprising, for each leaf node of the trained portion of the global decision tree: determining that the global decision tree should be extended at the leaf node;”; Determining that the global decision tree should be extended at the leaf node covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
“and recursively performing the method of claim 1 under an assumption that the trained portion of the global decision tree is fixed in place”; Recursively performing a federated learning method for training a global decision tree maintaining a training dataset that is inaccessible by other clients covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 7, 14, and 21 are directed to non-statutory subject matter and rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e. changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over “Privacy Preserving Vertical Federated Learning for Tree-based Models” by Wu et al., (non-patent literature published on 8/14/2020, hereinafter “Wu”), in view of “Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI” by Chandran et al., (non-patent literature published on 11/13/2021, hereinafter “Chandran”).
With respect to Claims 1, 8, and 15:
Wu teaches:
“A method performed by each client of a plurality of clients participating in a federated learning (FL) procedure for training a global decision tree, said each client maintaining a training dataset that is inaccessible by other clients in the plurality of clients, the method comprising: generating, by said each client, a local decision tree using the training dataset;” (Page 1, Section 1 titled “Introduction” recites the process of federated learning (FL), which enables multiple clients possessing data to jointly train a model without revealing their private data to each other. This involves each client performing local computations on their datasets, deriving results, and exchanging these results in a secure manner with other participating clients. Page 3, Section 2.3 recites the existence of a training dataset, which is used by the CART algorithm. This algorithm builds a decision tree recursively.)
“generating, by said each client, a set of items, wherein each item in the set of items corresponds to a subtree in the local decision tree and is associated with a payload comprising properties of nodes in the subtree;” (Page 3, Section 2.3 from Wu recites the process of the CART algorithm, where a decision tree is built recursively and each split creates a subtree (among multiple subtrees) with distinct structural components (multiple leaf nodes) containing information (akin to a payload).)
“and determining, by said each client, a trained portion of the global decision tree based on an output of the QPSIA protocol.” (Page 3, Section 2.3 from Wu recites recursively building a decision tree where part of the process involves determining the best split to construct two sub-trees. Specific splits/subtrees are created during the training process to form the final trained tree model.)
Wu does not appear to explicitly disclose:
“generating, by said each client, a set of items, wherein each item in the set of items corresponds to a subtree in the local decision tree and is associated with a payload comprising properties of nodes in the subtree;”
“executing, by said each client in collaboration with the other clients in the plurality of clients, a quorum private set intersection analytics (QPSIA) protocol, the executing including providing the set of items as input to the QPSIA protocol”
“and determining, by said each client, a trained portion of the global decision tree based on an output of the QPSIA protocol.”
However, Chandran teaches:
“generating, by said each client, a set of items, wherein each item in the set of items corresponds to a subtree in the local decision tree and is associated with a payload comprising properties of nodes in the subtree;” (Page 8, Section 3 from Chandran recites each party in the QPSIA protocol provides a set of elements to be compared across parties (clients).)
“executing, by said each client in collaboration with the other clients in the plurality of clients, a quorum private set intersection analytics (QPSIA) protocol, the executing including providing the set of items as input to the QPSIA protocol” (Page 2, Section 1.1 from Chandran describes a Quorum Private Set Intersection (QPSI) protocol where a leader wishes to obtain elements of their set that are also present in at least a certain amount of the other parties’ sets. Page 11, Section 5 from Chandran formally explains the functionality of the QPSI protocol, where for each quorum threshold, a set (Xi) of size m is provided as input.)
“and determining, by said each client, a trained portion of the global decision tree based on an output of the QPSIA protocol.” (Page 23, Section F.1 from Chandran describes an output from the QPSI protocol which indicates whether an element satisfies a specified threshold condition and is shared across clients.)
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to implement Claims 1, 8, and 15 that utilized the federated decision tree training teachings of Wu and the quorum private set intersection (QPSI) protocol teachings of Chandran. A PHOSITA would have been motivated to combine Wu’s federated learning for tree-based models method with Chandran’s QPSIA protocol in order to create more efficient and secure identification of common candidate tree structures across a group of participating clients. Representing candidate subtrees created by each client as elements in sets processed within Chandran’s QPSIA protocol would do a more efficient job in allowing clients to determine which candidate structures are shared across datasets without the direct exchanging of sensitive and confidential information. Combining the two methods would have improved the federated decision tree training process through the use of secure multi-party computations in order to decrease the chance of disclosing sensitive data among clients, while still preserving a highly collaborative process between them.
Therefore, Claims 1, 8, and 15 are rejected.
With respect to Claims 2, 9, and 16:
Wu teaches:
“The method of claim 1 wherein generating the set of items comprises, for each subtree in the local decision tree: checking whether said each subtree meets a topology requirement;” (Page 3, Section 2.3 from Wu recites how the CART algorithm decides whether some pruning conditions (pertaining to the structure/topology of the tree node) are satisfied for each subtree node, and if conditions are met, a leaf node is returned and expands the tree.)
“and upon determining that said each subtree meets the topology requirement: computing a unique fingerprint for said each subtree,” (Page 3, Section 2.3 from Wu describes the recursive building of subtrees.)
“computing a payload for said each subtree;” (Page 3, Section 2.3 from Wu recites a subtree containing a set of available features being split into two partitions that contains data describing the subtree, akin to a payload.)
Wu does not appear to explicitly disclose:
“and upon determining that said each subtree meets the topology requirement: computing a unique fingerprint for said each subtree,”
“and adding the unique fingerprint and the payload as a new item to the set of items”
However, Chandran teaches:
“and upon determining that said each subtree meets the topology requirement: computing a unique fingerprint for said each subtree,” (Page 23, Figure 16 from Chandran describes the steps involved in the QPSI protocol where elements from a party are transformed using a hash function to create unique, hash-based representations/values (fingerprints).)
“and adding the unique fingerprint and the payload as a new item to the set of items” (Page 23, Figure 16 from Chandran describes each party in the QPSI protocol having an input set containing individual elements, where each element is represented by an encoded value (similar to how fingerprints and payloads are encoded representations) and compared across participating parties.)
Therefore, Claims 2, 9, and 16 are rejected.
With respect to 3, 10, and 17:
Wu teaches:
“wherein computing the unique fingerprint comprises computing a hash of a subset of the properties of the nodes in said each subtree” (Page 3, Section 2.3 from Wu recites each subtree (containing nodes) has a set of available features with splits and associated partitioned data which define its characteristics.)
Wu does not appear to explicitly disclose:
“wherein computing the unique fingerprint comprises computing a hash of a subset of the properties of the nodes in said each subtree”
However, Chandran teaches:
“wherein computing the unique fingerprint comprises computing a hash of a subset of the properties of the nodes in said each subtree” (Page 3, Section 2.3 from Wu recites. Page 23, Figure 16 from Chandran recites the use of a type of hashing (cuckoo hashing) on inputted elements during the QPSI protocol, which creates a unique hash (fingerprint).)
Therefore, Claims 3, 10, and 17 are rejected.
With respect to Claims 4, 11, and 18:
Wu teaches:
“wherein the payload comprises, for each node in the subtree: a feature from the training dataset that is associated with said each node;” (Page 3, Section 2.3 from Wu recites how each node in the generated decision tree is defined by a split feature that is selected from a dataset containing the set of available features.)
“a verdict function that is associated with said each node;” (Page 3, Section 2.3 from Wu recites a sample set that can be split into two partitions through a verdict function from the node that decides in which direction data is sent.)
“and a size of a dataset that is associated with said each node” (Page 3, Section 2.3 from Wu recites the existence of a training data set containing an “n” number (size) of data points. During the construction of the decision tree, the CART algorithm involves deciding whether certain pruning conditions are satisfied for each tree node, like the number of samples (or data points) associated with a node.)
Therefore, Claims 4, 11, and 18 are rejected.
With respect to Claims 5, 12, and 19:
Wu teaches:
“wherein the output of the QPSIA protocol is a particular subtree selected from among all subtrees that appear in at least a threshold number of sets provided as input to the QPSIA protocol” (Page 3, Section 2.3 from Wu recites the creation of multiple subtrees/splits, where the CART algorithm selects the best subtree/split.)
“and wherein the particular subtree is deemed to be a best subtree for the global decision tree by an analytics function of the QPSIA protocol” (Page 3, Section 2.3 from Wu recites the CART algorithm determining the best split from each tree node for certain conditions.)
Wu does not explicitly disclose:
“wherein the output of the QPSIA protocol is a particular subtree selected from among all subtrees that appear in at least a threshold number of sets provided as input to the QPSIA protocol”
“and wherein the particular subtree is deemed to be a best subtree for the global decision tree by an analytics function of the QPSIA protocol”
However, Chandran teaches:
“wherein the output of the QPSIA protocol is a particular subtree selected from among all subtrees that appear in at least a threshold number of sets provided as input to the QPSIA protocol” (Page 23, Section F.1 from Chandran recites each element participating in the QPSI protocol are counted across sets, where only elements meeting the defined threshold conditions are selected. Chandran also recites the production of an output value, “cj”, from the QPSI protocol.)
“and wherein the particular subtree is deemed to be a best subtree for the global decision tree by an analytics function of the QPSIA protocol” (Page 23, Figure 16 from Chandran recites the comparison of shared elements across a number of parties (sets), where a threshold condition is applied. An analytics function of the QPSI protocol is counting occurrences of these elements across parties (sets).)
Therefore, Claims 5, 12, and 19 are rejected.
With respect to Claims 6, 13, and 20:
Wu teaches:
“wherein the analytics function determines that the particular subtree is the best subtree based on the payloads associated with the particular subtree in the sets” (Page 3, Section 2.3 from Wu recites the CART algorithm determining the best split from each tree node for certain conditions using features (data) associated with the subtree (similar to what a payload represents).
Wu does not appear to explicitly disclose:
“wherein the analytics function determines that the particular subtree is the best subtree based on the payloads associated with the particular subtree in the sets”
However, Chandran teaches:
“wherein the analytics function determines that the particular subtree is the best subtree based on the payloads associated with the particular subtree in the sets” (Page 23, Figure 16 from Chandran recites the existence of parties in the QPSI protocol that provide sets of elements. Shared elements are then compared across a number of parties (sets), where a threshold condition is applied. An analytics function of the QPSI protocol is counting occurrences of these elements across parties (sets).)
Therefore, Claims 6, 13, and 20 are rejected.
With respect to Claims 7, 14, and 21:
Wu teaches:
“The method of claim 1 further comprising, for each leaf node of the trained portion of the global decision tree: determining that the global decision tree should be extended at the leaf node;” (Page 3, Section 2.3 from Wu recites the evaluation of each tree node condition in order to determine whether to return a leaf node or stop once conditions are satisfied.)
“and recursively performing the method of claim 1 under an assumption that the trained portion of the global decision tree is fixed in place” (Page 3, Section 2.3 from Wu recites the CART algorithm recursively building a decision tree, where the tree is expanded at each node if certain pruning conditions are satisfied. If conditions aren’t satisfied, a best split is determined to recursively construct trained, fixed subtrees.)
Therefore, Claims 7, 14, and 21 are rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vibha Bhat whose telephone number is (571)-272-7091. The examiner can normally be reached on Monday – Thursday from 8:00 AM to 5:00 PM EST and every other Friday from 8:00 AM to 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes, can be reached at telephone number (571)-270-1006. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300. Information regarding the status of an application may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or (572)-272-1000.
/Vibha Bhat/Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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