DETAILED ACTION
This action is in response to the claims filed 02/28/2024 for Application number 18/590,873. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
filtering and selecting one rule from a plurality of rules can be considered to be an evaluation in the human mind,
identify one or more groups in the one or more subgraphs, each group comprising electronic documents and entities selectively identified from a corresponding subgraph based on the representations can be considered to be an evaluation in the human mind
building a cluster model of rules from at least some of the plurality of rules having a vector distance replaced by a specific vector distance; wherein the building of the cluster model of rules is based on identifying a tree-similarity related distance of some of the plurality of rules from the selected one rule can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas.
The limitation of vectorizing an expression of the one rule with different dimensions can be considered to be a mathematical calculation
This limitation as drafted, is a process that, under broadest reasonable interpretation, covers mathematical calculations which falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “a computer-implemented method of optimizing structured rules in data processing…”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). 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.
Step 2B Analysis: The claims do 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 elements of utilizing a computer to perform the steps of the claimed process amount 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. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the filtering and selecting of the one rule is performed using machine learning. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein the machine learning filters and selects specific rules from the plurality of rules based on an identified purpose. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein vectorizing an expression of the one rule includes forming an and-or-not tree from the expression. This claim recites additional mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: further comprising building at least another cluster model of rules based on a different setting of the specific vector distance; and wherein when the building of the cluster model of rules includes forming more than one and-or-not tree, the specific distance is set by performing a distance calculation starting from a last layer number that is common to each and-or-not tree. This claim recites additional mental and mathematical steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 5 is further incorporated, and further, the claim recites: further comprising selecting center vectors of one or more cluster models of rules is performed according to predetermined criteria. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 5 is further incorporated, and further, the claim recites: further comprising filtering and selecting center vectors of one or more cluster models of rules and combining the center vectors in different cluster models. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein the building of the clustering model of rules is performed using a k-means clustering operation. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein the building of the clustering model of rules is performed using a density-based clustering operation. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein the building of the clustering model of rules is performed using a grid-based clustering operation. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 2 is further incorporated, and further, the claim recites: further comprising building a plurality of cluster models of rules from at least some of the plurality of rules. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 2 is further incorporated, and further, the claim recites: further comprising combining two or more cluster models of rules. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Claim 13 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 13 additionally requires analysis for “A computing device configured to optimize structured rules in data processing, the computing device comprising: a processor; a storage device coupled to the processor, the storage device storing instructions to cause the processor to perform acts comprising …” however these are additional elements that amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding Claims 14-20, they recite features similar to claims 2, 8, 9, 11, 5, 7, and 12 are rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8, 11-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Perez et al. ("US 20200265264 A1", hereinafter "Perez") in view of Vaquero et al. ("WO 2017188986 A1", hereinafter "Vaquero") and further in view of Tahiri et al. ("Building alternative consensus trees and supertrees using kmeans and Robinson and Foulds distance", hereinafter "Tahiri").
Regarding claim 1, Perez teaches A computer-implemented method of optimizing structured rules in data processing, the method comprising:
filtering and selecting one rule from a plurality of rules (“Responsive to the Jaccard similarity being higher than the threshold, instructions 123 may implement block 420 to combine the pair with the highest Jaccard similarity as a new rule in the set of rules.” [¶0032]);
and
building a cluster model of rules from at least some of the plurality of rules [having a vector distance replaced by a specific vector distance]; wherein the building of the cluster model of rules is based on identifying a tree-similarity related distance of some of the plurality of rules from the selected one rule (“As such, in addition to information about which rules are combined as pairs, information may be stored in a tree structure or other hierarchical structure to show an order by which rules are combined. For example, a hierarchical structure may comprise nodes in an order that indicates that rules R1 and R3 were combined first, rules R13 and R2 were combined second, and then rules R123 and R4 were combined.” [¶0034; note: similarity between rules in a tree structure is interpreted as a “tree-similarity related distance”]).
However Perez fails to explicitly teach vectorizing an expression of the one rule with different dimensions;
Vaquero teaches vectorizing an expression of the one rule with different dimensions; (“For example, a computing device may provide a rule having a set of conditions, where each condition applies to a variable of a set of variables. A set of dimensions may be defined for each variable of the set of variables. Using the set of dimensions, a set of hypershapes may be created for the rule.” [¶0013])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Perez’s teachings in order to vectorize an expression of a rule with different dimensions as taught by Vaquero. One would have been motivated to make this modification in order to provide a robust way to assess a rule and interactions between multiple rules. [Vaquero, ¶0013]
However Perez/Vaquero fails to explicitly teach building a cluster model of rules from at least some of the plurality of rules having a vector distance replaced by a specific vector distance
Tahiri teaches building a cluster model of rules from at least some of the plurality of rules having a vector distance replaced by a specific vector distance (“Here, we discuss the main modifications that should be introduced into the conventional k-means algorithm in order to adapt it to tree clustering… In Equation 2, the RF distance, or more precisely its square root, which has the Euclidean property, replaces the traditional Euclidean distance used in k-means” [bottom right pg. 2 – left col, pg. 3- top right col, pg. 3])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Perez’s/Vaquero’s teachings in order to build a tree clustering model having a vector distance replaced by a specific vector distance as taught by Tahiri. One would have been motivated to make this modification as “The main advantage of using the precalculated RF distances (the right term in Equation 2) is that we should not calculate a consensus tree, or the cluster centroid, for any intermediate cluster of trees considered by k-means, and that an object relocation operation consisting in finding the best cluster for a given tree T belonging to cluster C (i.e. when we try to relocate T into each of the K-1 clusters that are different from C) can be performed in O(K) time” [pg. 3, right col, top para, Tahiri]
Regarding claim 2, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 1, Perez teaches wherein the filtering and selecting of the one rule is performed using machine learning. (“For example, instructions 123 may implement block 410 of FIG. 4 as well to determine whether a Jaccard similarity between a pair of rules is greater than a threshold. The threshold may be zero, may be machine-learned, may be provided by an administrator of system 100, and/or may be otherwise determined.” [¶0032])
Regarding claim 3, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, Perez teaches wherein the machine learning filters and selects specific rules from the plurality of rules based on an identified purpose. (“In some examples, like the example method described in FIG. 4, instructions 123 may combine a pair of rules with the highest Jaccard similarity as a new rule in the set of rules, replacing the two individual rules that were combined.” [¶0032; “highest Jaccard similarity” would correspond to “based on an identified purpose”])
Regarding claim 8, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, Tahiri teaches wherein the building of the clustering model of rules is performed using a k-means clustering operation. (“We show how an adapted version of the popular k-means clustering algorithm…” [Abstract])
Same motivation to combine the teachings of Perez/Vaquero/Tahiri as claim 1.
Regarding claim 11, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, Perez teaches further comprising building a plurality of cluster models of rules from at least some of the plurality of rules. (¶0034-¶0035; “In some examples, this hierarchical structure may be created and updated as the iterative process is being performed, with information about the Jaccard similarities and calculated hypervolumes being stored at each node and calculated for each updated set of rules based on that stored information.”)
Regarding claim 12, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, Perez teaches further comprising combining two or more cluster models of rules. (“For example, a hierarchical structure may comprise nodes in an order that indicates that rules R1 and R3 were combined first, rules R13 and R2 were combined second, and then rules R123 and R4 were combined. With each node, information about the sets of hypershapes combined, the individual rules combined, the hypervolumes calculated for each rule, the calculated Jaccard similarities, and/or other information may be stored.” [¶0034])
Claim 13 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 13 additionally requires A computing device configured to optimize structured rules in data processing, the computing device comprising: a processor; a storage device coupled to the processor, the storage device storing instructions to cause the processor to perform acts comprising (Perez, ¶0044)
Regarding claims 14, 15, 17, and 20, they are substantially similar to claims 2, 8 11 and 12 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Perez in view of Vaquero and Tahiri and further in view of Martino et al. ("US 20110004632 A1", hereinafter "Martino").
Regarding claim 4, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, however fails to explicitly teach wherein vectorizing an expression of the one rule includes forming an and-or-not tree from the expression.
Martino teaches wherein vectorizing an expression of the one rule includes forming an and-or-not tree from the expression. (“The operators such as ALL or ANY provided in the present disclosure allow users to express the rules in plain or natural language logic rather then in mathematical Boolean logic…” [¶0040-0044, See also Fig. 4])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Perez’s/Vaquero’s/Tahiri’s teachings in order to form an and-or-not tree from an expression as taught by Martino. One would have been motivated to make this modification as “Users want to make business policy maintenance an activity of business users rather than developers…However, business users may encounter difficulty in formalizing those rules in Boolean logic, that is, writing rules using expressions that involve the operators such as AND, OR and NOT.” [¶0008, Martino]
Claim 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Perez in view of Vaquero and Tahiri and further in view of Ouyang et al. ("Online structural clustering based on DBSCAN extension with granular descriptors", hereinafter "Ouyang").
Regarding claim 9, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, however fails to explicitly teach wherein the building of the clustering model of rules is performed using a density-based clustering operation.
Ouyang teaches wherein the building of the clustering model of rules is performed using a density-based clustering operation. (“DBSCAN is a typical density-based clustering algorithm [24]. It realizes clustering by distinguishing high-density regions with low-density regions in the feature space, and easily determines arbitrary clusters.” [pg. 690, 2.1, ¶1])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Perez’s/Vaquero’s/Tahiri’s teachings in order to use a density-based clustering algorithm as taught by Ouyang. One would have been motivated to make this modification “to realize an efficient online structural clustering, especially in big data analysis, a series of granular fuzzy models are built with consideration of structural information, then a rule-based model is formed for guiding online clustering of new testing data” [Abstract, Ouyang]
Regarding claim 16, it is substantially similar to claim 9 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Perez in view of Vaquero and Tahiri and further in view of Kilic et al. ("Comparison of different strategies of utilizing fuzzy clustering in structure identification", hereinafter "Kilic").
Regarding claim 10, Perez/Vaquero/Tahiri teaches The computer-implemented method according to claim 2, however fails to explicitly teach wherein the building of the clustering model of rules is performed using a grid-based clustering operation.
Kilic teaches wherein the building of the clustering model of rules is performed using a grid-based clustering operation. (“This may be achieved by assigning linear membership functions to each output cluster (e.g., grid based clustering with triangular fuzzy sets).” [pg. 5157, bottom para])
t would have been obvious to one of ordinary skill in the art before the effective filing date to modify Perez’s/Vaquero’s/Tahiri’s teachings in order to use a grid based clustering algorithm as taught by Kilic. One would have been motivated to make this modification in order to provide a solution to “These problems are namely, the problems of harmonics, the problems with the boundary fuzzy sets, the problems associated with the classification and the more general problem of cluster validity, i.e., determination of number of clusters (c) and level of fuzziness (m)” [pg. 5157, bottom para, Kilic]
Allowable Subject Matter
Claims 5-7 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 5 and 18:
building at least another cluster model of rules based on a different setting of the specific vector distance; and wherein when the building of the cluster model of rules includes forming more than one and-or-not tree, the specific distance is set by performing a distance calculation starting from a last layer number that is common to each and-or-not tree.
No prior art was uncovered which explicitly discloses building at least another cluster model of rules based on a different setting of the specific vector distance.
The closest prior art uncovered was Tahiri et al. (“Building alternative consensus trees and supertrees using kmeans and Robinson and Foulds distance”) which discloses building alternative consensus trees and supertrees however it does not explicitly state that building the trees is based on a different setting of the specific vector distance nor includes any details regarding the step of wherein when the building of the cluster model of rules includes forming more than one and-or-not tree, the specific distance is set by performing a distance calculation starting from a last layer number that is common to each and-or-not tree in claim 5
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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