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 .
Effective Filing Date
The effective filing date of 04/24/2023 is acknowledged.
Information Disclosure Statement
The information disclosure statement(s) submitted on 04/24/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Specification
The specification is objected to under 37 CFR 1.75(d)(1) for failure to provide clear support for the claim language. Refer to the 35 U.S.C. § 112(b) section of this document for the basis of this objection.
Status of Claims
The present application is being examined under the claims filed on 04/24/2023.
Claim(s) 1-20 is/are rejected.
Claim(s) 1-20 is/are pending.
Prior Art References
Wehrmann, J., Cerri, R. and Barros, R., 2018, July. Hierarchical multi-label classification networks. In International conference on machine learning (pp. 5075-5084). PMLR. (Hereafter, “Wehrmann”).
Cerri, R., Barros, R.C. and de Carvalho, A.C., 2012, March. A genetic algorithm for hierarchical multi-label classification. In Proceedings of the 27th annual ACM symposium on applied computing (pp. 250-255). (Hereafter, “Cerri”).
Behera, S., 2018, May. Implementation of a finite state automaton to recognize and remove stop words in english text on its retrieval. In 2018 2nd international conference on trends in electronics and informatics (ICOEI) (pp. 476-480). IEEE. (Hereafter, “Behera”).
Cicalese, F., Jacobs, T., Laber, E. and Molinaro, M., 2010, December. On greedy algorithms for decision trees. In International Symposium on Algorithms and Computation (pp. 206-217). Berlin, Heidelberg: Springer Berlin Heidelberg. (Hereafter, “Cicalese”).
Erradi, A. and Mansouri, Y., 2020. Online cost optimization algorithms for tiered cloud storage services. Journal of Systems and Software, 160, p.110457. (Hereafter, “Erradi”).
Claim Rejections - 35 U.S.C. § 112(b)
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.
Claim(s) 4 and 13 is/are rejected as being indefinite under 35 U.S.C. § 112(b) for the usage of the term “best transition function value” as it is unclear what it means for a transition function to have a “best” value.
Claim Rejections - 35 U.S.C. § 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. This judicial exception is not integrated into a practical application as outlined in the 2-step analyses for each claim that follows.
Combined Step 1 (Statutory Category) - Is the claim to a process, machine, manufacture or composition of matter?
Yes - claims 1-9 claim a method and claims 10-20 claim machines.
In reference to Steps 2A Prong 1 regarding independent claims 1, 10, 19.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes - representative claim 1 recites:
“determining, by the computer, cause-effect-decision relationships of the attributes of the subject;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“deriving, by the computer, custom attributes based on the cause-effect-decision relationships of the attributes of the subject;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“identifying, by the computer, custom attribute relationships and labels for the derived custom attributes of the subject based on the cause-effect-decision relationships of the attributes of the subject, the custom attribute relationships including at least one belongs-to relationship and at least one decision relationship;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“generating, by the computer, a hierarchy of the custom attributes using a finite state automaton;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“creating, by the computer, a hierarchical multi-label classifier based on the generated hierarchy;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“using, by the computer, the classifier to generate class label decisions associated with labels of the classifier to objects of the subject; and” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Claims 10 and 19 are substantially similar to claim 1 and thus also recite abstract ideas.
In reference to Steps 2A Prong 1 regarding dependent claims 2-6, 8, 9, 11, 13-18, 20.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes -
“2. The computer-implemented method of claim 1, wherein identifying the custom attribute relationships includes identifying the attributes of the subject as having the belongs-to relationship with the subject in response to determining that the subject has a first order dependency with the attributes and directly contributes to a problem to be solved by the classifier; and identifying the attributes of the subject as having the decision relationship in response to determining that a derived action from the respective attribute, when applied, indirectly contributes to a solution of the problem.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“3. The computer-implemented method of claim 1, wherein generating the hierarchy includes computing degradation rates and balance coefficients for possible hierarchies, wherein the degradation rates are rates at which the labels for the derived custom attributes are pruned when the attributes are placed at a particular level in the hierarchy, and wherein the balance coefficients define how well samples for attributes at the respective levels are distributed. which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“4. The computer-implemented method of claim 1, wherein generating the hierarchy using the finite state automaton includes:
“initializing a set of the custom attributes;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“using an initial state transition function to select one of the custom attributes to fix to a first level of the hierarchy;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“removing, from the set of the custom attributes, the custom attribute fixed to the first level;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“assigning the custom attributes remaining in the set to levels of the hierarchy by performing the following sequence:”
“determining a next state using a state transition function by computing balance coefficients and degradation rates for the custom attributes remaining in the set;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“in response to determining that one of the custom attributes remaining in the set has a best transition function value, fixing the one of the custom attributes at a current level;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“in response to determining that more than one of the custom attributes remaining in the set have a same transition function value, entering a first transition state ts0;”
“in response to entering the first transition state ts0, determining whether more than one of the custom attributes remaining in the set have the same attribute importance;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“in response to determining that none of the custom attributes remaining in the set have the same attribute importance, fixing the custom attribute having the highest attribute importance to the current level;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“in response to determining that more than one of the custom attributes remaining in the set have the same attribute importance, entering a second transition state ts1 from the first transition state ts0;” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“in response to entering the second transition state ts1, computing an error rate for hierarchies at the current level, and fixing the custom attribute with a least error to the current level; and” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“removing, from the set of the custom attributes, the custom attribute fixed to the current level.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“5. The computer-implemented method of claim 1, wherein the hierarchy is generated without training every possible hierarchical multi-label classifier.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“6. The computer-implemented method of claim 1, wherein the labels of the classifier have no predefined hierarchy.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“8. The computer-implemented method of claim 1, wherein the objects include cloud objects, cloud assets, or cloud objects and cloud assets.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“9. The computer-implemented method of claim 8, wherein the subject is a cloud based storage system, wherein the objects include cloud-storage objects and cloud-storage assets in the cloud based storage system.” which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Claims 2-4, and 6-9 are substantially similar to claims 11-13, and 15-18 and thus also recite abstract ideas.
Claims 14 and 20 are substantially similar to claim 5 and thus also recite abstract ideas.
In reference to Steps 2A Prong 2 and 2B regarding independent claims 1, 10, 19.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
Yes - representative claim 1 recites:
“collecting, by a computer, attributes of a subject;” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network.
“outputting the decisions.” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network.
Claims 10 and 19 are substantially similar to claim 1 and thus are also rejected for reciting patent ineligible subject matter.
In reference to Steps 2A Prong 2 and 2B regarding dependent claims 7, 12.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
Yes - representative claim 7 recites:
“7. The computer-implemented method of claim 1, comprising applying the class label decisions to the objects of the subject to make changes to the subject.” which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network.
Claim 12 is substantially similar to claim 7 and thus are also rejected for reciting patent ineligible subject matter.
Thus, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 5, 6, 10, 11, 13, 14, 15, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cerri in view of Behera.
Claim(s) 3, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cerri in view of Behera in further view of Cicalese.
Claim(s) 7, 8, 9, 16, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cerri in view of Behera in further view of Erradi.
“1. A computer-implemented method, comprising:”
Cerri teaches:
“collecting, by a computer, attributes of a subject; determining, by the computer, cause-effect-decision relationships of the attributes of the subject;”
(Cerri 252, “Seven datasets related to protein functions of the Saccharomyces cerevisiae organism were employed in the experiments. The datasets are freely available at [URL], and are related to bioinformatics data such as phenotype data and gene expression levels. […] Table 1 shows the main characteristics of the training, valid and test datasets used.”
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“collecting […] attributes” is taught by the “datasets” of Cerri wherein the datasets are comprised of attributes.
“determining cause-effect-decision relationships of the attributes of the subject” is taught by the phenotype and gene expression levels. Under a broadest reasonable interpretation of “cause-effect-decision relationship”, the “cause” is taught by the protein data, the “effect” is the phenotypic expression, and the “decision” is the class it belongs to.
“deriving, by the computer, custom attributes based on the cause-effect-decision relationships of the attributes of the subject;”
(Cerri 251, “In this section, we present a novel global method for HMC problems called Hierarchical Multi-Label Classification with Genetic Algorithm (HMC-GA). […] In HMC-GA, each individual is a string of integer/real values, representing a sequence of tests (antecedent) that constitute the classification rule.”)
“deriving […] custom attributes based on the […] relationships of the attributes” is taught by “the sequence of tests that constitute the classification rule”. The sequence of tests teach the custom attributes.
“identifying, by the computer, custom attribute relationships and labels for the derived custom attributes of the subject based on the cause-effect-decision relationships of the attributes of the subject, the custom attribute relationships including at least one belongs-to relationship and at least one decision relationship;”
(Cerri Abstract, “In this work, we propose a novel global method called HMC-GA, which employs a genetic algorithm for solving the HMC problem. In our approach, the genetic algorithm evolves the antecedents of classification rules, in order to optimize the level of coverage of each antecedent. Then, the set of optimized antecedents is selected to build the corresponding consequent of the rules (set of classes to be predicted).”)
“identifying […] custom attribute relationships and labels for the derived custom attributes” is taught by the selection of “optimized antecedents” to “build the corresponding consequent of the rules (the set of classes to be predicted)”.
(Cerri 251, “The antecedent of the rule is comprised of AND clauses, which test a given dataset attribute Ai according to a threshold [Symbol font/0x44], based on a corresponding operator OP. For the case of nominal attributes, the operators employed are = and =/=. For numeric attributes, operators available are >, <, >=, <=.”)
“belongs-to relationship” is taught by an “=” “nominal attribute”.
“decision relationship” is taught by “>, <, >=, <=”.
“generating, by the computer, a hierarchy of the custom attributes [using a finite state automaton];”
(Cerri Abstract, “In this work, we propose a novel global method called HMC-GA, which employs a genetic algorithm for solving the HMC problem. In our approach, the genetic algorithm evolves the antecedents of classification rules, in order to optimize the level of coverage of each antecedent. Then, the set of optimized antecedents is selected to build the corresponding consequent of the rules (set of classes to be predicted).”)
“generating […] a hierarchy of the custom attributes” is taught by the antecedent.
“creating, by the computer, a hierarchical multi-label classifier based on the generated hierarchy; using, by the computer, the classifier to generate class label decisions associated with labels of the classifier to objects of the subject; and outputting the decisions.”
(Cerri 252, “HMC-GA evolves the antecedents of the rules, i.e., the AND clauses that form a rule. The consequent of the rule, which indicates the classes to which the examples that satisfy the rule belong to, is calculated using a deterministic procedure as follows.”)
“creating […] a hierarchical multi-label classifier” is taught by the “consequent of the rule, which indicates the classes to which the examples that satisfy the rule belong to”.
Behera teaches:
“[generating, by the computer, a hierarchy of the custom attributes] using a finite state automaton;”
(Behera Abstract, “Here I am proposing a solution to identify the stop word present in the English language using finite state automata.”)
Motivation to combine Cerri, Behera.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Cerri, Behera.
Cerri discloses a genetic algorithm for hierarchical multi-label classification.
Behera discloses an implementation of a finite state automata for natural language processing.
One would be motivated to combine these references because the finite state machine structure of Behera provides a clear mechanism for implementing the algorithmic logic of Cerri.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
“2. The computer-implemented method of claim 1,”
Cerri teaches:
“wherein identifying the custom attribute relationships includes identifying the attributes of the subject as having the belongs-to relationship with the subject in response to determining that the subject has a first order dependency with the attributes and directly contributes to a problem to be solved by the classifier; and identifying the attributes of the subject as having the decision relationship in response to determining that a derived action from the respective attribute, when applied, indirectly contributes to a solution of the problem.”
(Cerri 251, “The antecedent of the rule is comprised of AND clauses, which test a given dataset attribute Ai according to a threshold [Symbol font/0x44], based on a corresponding operator OP. For the case of nominal attributes, the operators employed are = and =/=. For numeric attributes, operators available are >, <, >=, <=.”)
“belongs-to relationship” is taught by an “=” “nominal attribute”.
“decision relationship” is taught by “>, <, >=, <=”.
“3. The computer-implemented method of claim 1,”
Cicalese teaches:
“wherein generating the hierarchy includes computing degradation rates and balance coefficients for possible hierarchies, wherein the degradation rates are rates at which the labels for the derived custom attributes are pruned when the attributes are placed at a particular level in the hierarchy, and wherein the balance coefficients define how well samples for attributes at the respective levels are distributed.”
(Cicalese 3, “This might be a result of the two specific greedy criteria considered in those papers, namely the shrinkage-cost ratio and the minimization of the heaviest group. In the paper considering the former criterium, at each step the algorithm selects the test i which maximizes the shrinkage-cost ratio defined by [...] where S is the set of objects consistent with the tests performed so far [2, 7, 15]. In the case when only uniform weights are considered the weights are substituted by cardinalities of the corresponding sets. In the papers considering the latter criterium, greedy means selecting the test that generates a partition of S whose heaviest group is as light as possible [6, 21].”)
The “degradation rates” are taught by the “shrinkage-cost ratio” criteria.
The “balance coefficients” are taught by the “minimization of the heaviest group” criteria.
Motivation to combine Cerri, Behera, Cicalese.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Cerri, Behera, Cicalese.
Cerri, Behera discloses an algorithm for hierarchical multi-label classification.
Cicalese discloses greedy criteria in the development of decision tress.
One would be motivated to combine these references because greedy criteria provide a performant way for forming hierarchical structures without exhausting the entire search space.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(B) Simple substitution of one known element for another to obtain predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
“4. The computer-implemented method of claim 1, wherein generating the hierarchy [using the finite state automaton] includes:”
Cerri teaches:
“initializing a set of the custom attributes; using an initial state transition function to select one of the custom attributes to fix to a first level of the hierarchy;”
(Cerri Algorithm 1 line 3, “Generate InitialPop”)
“removing, from the set of the custom attributes, the custom attribute fixed to the first level;”
(Cerri Algorithm 1 line 25, “Remove examples from D covered by Rules”)
“assigning the custom attributes remaining in the set to levels of the hierarchy by performing the following sequence:”
“in response to determining that one of the custom attributes remaining in the set has a best transition function value, fixing the one of the custom attributes at a current level;”
(Cerri Algorithm 1, “selection”, “crossover”, “calculateFitness”)
The “transition function value” is taught by “calculateFitness”
“in response to determining that more than one of the custom attributes remaining in the set have a same transition function value, entering a first transition state ts0; in response to entering the first transition state ts0, determining whether more than one of the custom attributes remaining in the set have the same attribute importance;”
(Cerri Algorithm 1, “selection”, “crossover”)
“in response to determining that none of the custom attributes remaining in the set have the same attribute importance, fixing the custom attribute having the highest attribute importance to the current level;”
(Cerri Algorithm 1, “selection”, “crossover”)
“in response to determining that more than one of the custom attributes remaining in the set have the same attribute importance, entering a second transition state ts1 from the first transition state ts0; in response to entering the second transition state ts1, computing an error rate for hierarchies at the current level, and fixing the custom attribute with a least error to the current level; and”
(Cerri Algorithm 1, “calculateFitness”)
“removing, from the set of the custom attributes, the custom attribute fixed to the current level.”
(Cerri Algorithm 1, “selection”)
Behera teaches:
“using a finite state automaton;”
(Behera Abstract, “Here I am proposing a solution to identify the stop word present in the English language using finite state automata.”)
Cicalese teaches:
“determining a next state using a state transition function by computing balance coefficients and degradation rates for the custom attributes remaining in the set;”
(Cicalese 3, “This might be a result of the two specific greedy criteria considered in those papers, namely the shrinkage-cost ratio and the minimization of the heaviest group. In the paper considering the former criterium, at each step the algorithm selects the test i which maximizes the shrinkage-cost ratio defined by [...] where S is the set of objects consistent with the tests performed so far [2, 7, 15]. In the case when only uniform weights are considered the weights are substituted by cardinalities of the corresponding sets. In the papers considering the latter criterium, greedy means selecting the test that generates a partition of S whose heaviest group is as light as possible [6, 21].”)
The “degradation rates” are taught by the “shrinkage-cost ratio” criteria.
The “balance coefficients” are taught by the “minimization of the heaviest group” criteria.
“5. The computer-implemented method of claim 1,”
Cerri teaches:
“wherein the hierarchy is generated without training every possible hierarchical multi-label classifier.”
(Cerri Algorithm 1)
The algorithm generates hierarchies for a finite number of generations and does not train every possible classifier.
“6. The computer-implemented method of claim 1,”
Cerri teaches:
“wherein the labels of the classifier have no predefined hierarchy.”
(Cerri 252, “HMC-GA evolves the antecedents of the rules, i.e., the AND clauses that form a rule. The consequent of the rule, which indicates the classes to which the examples that satisfy the rule belong to, is calculated using a deterministic procedure as follows. Given the set of examples Sr covered by a rule r, the consequent is a vector of length k (where k is the number of class labels in the class hierarchy). The value for each ith component of the consequent vector for rule r is given by [EQUATION 3]”)
The labels generated by the classifier have a hierarchy that is not predefined as it is generated from the HMC-GA algorithm.
“7. The computer-implemented method of claim 1,”
Erradi teaches:
“comprising applying the class label decisions to the objects of the subject to make changes to the subject.”
(Erradi 4, “Our system model uses tiered cloud storage services with hot and cool tiers. For example, an object can be a tweet or a photo posted by the user on Twitter feed or Facebook timeline with either hot or cool status. The status of the object depends on the access frequency and whether it is in the either hot or cool status in each time slot of its lifetime.”)
Motivation to combine Cerri, Behera, Erradi.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Cerri, Behera, Erradi.
Cerri, Behera discloses an algorithm for hierarchical multi-label classification.
Erradi discloses cost optimization strategies for tiered cloud storage systems.
One would be motivated to combine these references because cloud storage is inherently hierarchical with tiers like “hot” and “cold” storage and the hierarchical classification methodology of Cerri, Behera could be utilized to optimize cloud storage usage.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
“8. The computer-implemented method of claim 1,”
Erradi teaches:
”wherein the objects include cloud objects, cloud assets, or cloud objects and cloud assets.”
(Erradi 4, “Our system model uses tiered cloud storage services with hot and cool tiers. For example, an object can be a tweet or a photo posted by the user on Twitter feed or Facebook timeline with either hot or cool status. The status of the object depends on the access frequency and whether it is in the either hot or cool status in each time slot of its lifetime.”)
“9. The computer-implemented method of claim 8,”
Erradi teaches:
”wherein the subject is a cloud based storage system, wherein the objects include cloud-storage objects and cloud-storage assets in the cloud based storage system.”
(Erradi 4, “Our system model uses tiered cloud storage services with hot and cool tiers. For example, an object can be a tweet or a photo posted by the user on Twitter feed or Facebook timeline with either hot or cool status. The status of the object depends on the access frequency and whether it is in the either hot or cool status in each time slot of its lifetime.”)
Claims 10-18 are substantially similar to claims 1-9 and are thus rejected using the same art.
Claim 19 is substantially similar to claim 1 and is rejected using the same art.
Claim 20 is substantially similar to claim 5 and is rejected using the same art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CODY RYAN GILLESPIE whose telephone number is (571)272-1331. The examiner can normally be reached M-F, 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker A Lamardo can be reached on 5172705871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications 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://www.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 571-272-1000.
/CODY RYAN GILLESPIE/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147