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 action is in response to the original application filed on April 16th, 2021 and the amendments made and submitted Sept. 8th, 2025.
The examiner would like to note that this detailed action is a Non-Final Rejection based on the amended claim and arguments submitted on Sept. 8th 2025. The examiner has been reevaluating the claims and addressing the concerns and arguments brought up in the interview on Jan. 5th, 2026 which were based on the arguments submitted on Sept. 8th, 2025. Further, the examiner has recognized that the applicant has submitted another amendment to the claims and has brought forward further remarks and arguments. Since there are now two sets of arguments and amendments that have not been properly addressed, the examiner has decided to respond the amended claims submitted Sept. 8th, 2025 and the issues brought forward in the interview which occurred on Jan. 5th, 2026. This rejection will not discuss the recently amended claims submitted Feb. 05th, 2026 and the remarks because the issues in the arguments and amendments submitted Sept. 8th, 2025 were never properly addressed by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Sept. 8th, 2025 has been entered.
Response to Amendment
The Examiner thanks the applicant for the remarks, edits and arguments.
Regarding claim Rejections – 35 U.S.C. 112(b)
Applicant Remarks:
The applicant and the examiner had an interview about this application on Jan. 5th, 2026 where the amended claims were discussed. One of the topics was the use to the word “DNA” in the claims. This term was removed during an amendment and the applicant left one instance of the word “DNA”. During the interview both parties agreed that the word “DNA” is to be removed from the claims. Therefore, the applicant requests the at rejection under 35 U.S.C. 112 to be removed.
Examiner response:
The examiner has agreed with the applicant during an interview that occurred Jan. 5th, 2026 that this term “DNA” was to be removed from the claims. Therefore, the rejection under 35 U.S.C. 112(b) has been withdrawn.
Regarding Claim Rejections - 35 U.S.C. 101
Applicant Remarks:
The applicant argues that the claimed invention does not recite any abstract ideas. The applicant further states that even if the claims recite abstract ideas, there is sufficient evidence that this invention provides "significantly more" and qualifies for the judicial exception. Therefore, the applicant believes the rejection under 35 U.S.C. 101 should be withdrawn.
The applicant argues that the claimed invention is not an abstract idea. This is because the applicant believes that a human is incapable of processing large amounts of computer data and generate a machine learning model without training. Further, the applicant states that it is not feasible for a human mind to be able to extract different portions of other machine learning models or computer data to generate a completely new model without training.
Further the applicant states that even if the claimed subject matter contains abstract ideas, this invention provides "significantly more" than an abstract idea and therefore should be patent eligible. It is stated that this invention will eliminate excess training costs, reduce processor usage and automatically perform actions on network equipment. The applicant states that the limitations in the amened independent claims are not generic functions and instead are specific operations designed to improve machine learning systems.
Finally, the applicant states that the examiner has improperly utilized the MPEP 2106.044(a)(2)(III)(c) stating that a federal circuit court has stated that not all computer-implemented process are abstract and that claims directed to specific improvement is considered patent eligible.
Examiner Response:
The MPEP 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, judgments, and opinions." (MPEP 2106.04(a)(2)(III)). Taking this into consideration, the definition of the word "Observation" according to the Merriam-Webster dictionary is: "an act of recognizing and noting a fact or occurrence often involving measurement with instruments" and the Merriam-Webster definition of "Evaluation" is: "determination of the value, nature, character, or quality of something or someone". These definitions can be applied to the amended claims, for example, claim 1 recites: "detecting, by a processing system including a processor, first [second] data associated with training of a first [second] machine learning model;". Using the definition of "observation", the process of “detecting data” is "an act of recognizing and noting a fact or occurrence often involving measurement with instruments". A human mind is capable of detecting data and with the assistance of a generic computer. Large amounts of data can theoretically be observed and evaluated using a generic computer. Therefore, the claim: "detecting, by a processing system including a processor, first [second] data associated with training of a first [second] machine learning model;" would recite the abstract idea of observation and evaluation. Next, claim 1 recites the identification of patterns within the data and other found patterns. Pattern recognition is common process that is performed by humans routinely. So, a human, with the assistance of a generic computer or pen and paper, would be able to observe and evaluate patterns within datasets and within given sets of patterns in varying sizes of data pools. After reviewing the claimed subject matter the examiner found abstract ideas such as detecting data and identifying patterns.
Next the applicant argues the examiner misuses MPEP 2106.04(a)(2)(III)(c). This section of the MPEP 2106.04(a)(2)(III)(c) states, "Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer's shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary."". As it is understood per the provided specification [0082] "The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet, a smart phone, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.". This would lead one to believe that this invention is executed on and with a computing systems which are recognized as a "computer". All of the computing devices listed would not be considered to be specially built machines but instead are considered existing computers, which are in use. Therefore, the use of the MPEP 2106.04(a)(2)(III)(c) is considered applicable to this patent application because it recites the use of generic computing devices. Further the MPEP(a)(2)(III)(c) states, "In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.". It is noted that this section of the MPEP uses "or" denoting that only one of these categories is necessary to evaluate the claims under this section. However, it could be argued that this invention would fall into more than one of these categories. Using this definition from the MPEP, and the definitions given above, it is noted that this invention is claiming a concept which is performed on a generic computer and is executing the claimed method in a computer environment and, finally, this invention also uses a computer as a tool to perform the concept. Therefore, the examiner believes that the MPEP(a)(2)(III)(c) is applicable and appropriate.
After a claim has been determined to recite an abstract idea, further analysis is performed using the Alice/Mayo test. In particular it is important to evaluate the specification and the claims to see if the claimed invention provides "significantly more". While evaluating a claim the examiner should question whether a claim or limitations provide: "Improvements to the functioning of a computer" (MPEP 2106.05(l)(A)(i)), "Improvements to any other technology or technical field" (MPEP 2106.0S(l)(A)(ii)) and "Applying the judicial exception with, or by use of, a particular machine" (MPEP 2106.0S(l)(A)(iii)). The claims currently do not disclose the improvement to technology or computing system. The examiner would like to clarify that the overall improvements may be present in the specification but they are not reflected in the claims as a whole.
The applicant argues the invention does not train newly created models and therefore it saves on computation cost because training machine learning models is computationally expensive. According to IBM.com "Model training is the machine learning (ML) step where the "learning" occurs. In machine learning, learning involves adjusting the parameters of an ML model." (Bergmann, https://www.ibm.com/think/topics/model-training). The methods claimed discloses a process of evaluating, extracting and altering blocks of data from other data sources or models to generate a new model. Using the above definition of a training a machine learning model, this invention discloses steps, determining, identifying, extracting, which is used to learn and store new building blocks for future models. Therefore, according to the information presented by the applicant, the process disclosed in the specification and claims do disclose a training process to generate a new machine learning model from learning patterns. Further, the process of observing/detecting data, evaluating data for patterns, extracting and altering of data does still have a computational cost on a processing system. Therefore, the processing system still needs to allocate and exert computational energy on performing these learning steps, possibly negating some of the "gained" computational savings. While considering the MPEP and all definitions stated above the claims have been reexamined and evaluated for patent eligibility. It was found that the claimed subject matter does in fact recite abstract ideas and fails to provide "significantly more", therefore, the current amended claims are still rejected under 35 U.S.C. 101, see 101 rejection below.
Regarding Claim Rejections - 35 U.S.C. 102
Applicant Remarks:
The applicant argues that the Iglesias fails to teach the amended independent claims. In particular Iglesias teaches a method called stacking which uses multiple models in succession to perform actions or evaluate functions. The applicant states Iglesias fails to teach the creation of a new machine learning model without any training. Because of this, the applicant requests the 102 rejection be withdrawn.
Examiner Response:
Upon further evaluation of the claims the art Iglesias fails to explicitly disclose the current amended claims. After the amendments were submitted, further search was performed to ensure that the current claims cannot be anticipated by a single art prior to the effective filing date. No single art was found which could properly teach the current claims. Therefore, the examiner has withdrawn the rejection under 35 U.S.C. 102.
Regarding Claim Rejections - 35 U.S.C. 103
Applicant Remarks:
The applicant argues that the art Yao in combination of Iglesias fails to properly teach the amended dependent claims. This is because Iglesias fails to properly teach the independent claims. Making the combination of the two arts moot and would fail to teach all limitations of the amened dependent claims. Therefore, the applicant requests the rejection under 35 U.S.C. should be withdrawn.
Examiner Response:
Upon further evaluation it was found that Iglesias does fail to explicitly teach the independent claims. Therefore, the combination of Iglesias and Yao would fail to teach the current amended claims. However, after each amendment a complete search is conducted. After completing the new search, new art was found which the examiner believes discloses the current amended claim according to 35 U.S.C. 103. Therefore, the current rejection under 35 U.S.C. 103 is upheld, see 103 rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 ("2019 PEG").
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 recites, "A method comprising:", therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim states inter alia:
“detecting, by a processing system including a processor, first data associated with training of a first machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting, by the processing system, second data associated with training of a second machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the
human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying, by the processing system, a first pattern in the first data based on a first frequency threshold usage for the first data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying, by the processing system, a second pattern in the second data based on a second frequency threshold usage for the second data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying, by the processing system, a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage for the first data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying, by the processing system, a second MP in the second data based on the second pattern and based on a second frequency threshold usage for the second data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “extracting, by the processing system, a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting, by the processing system, a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating, by the processing system, a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing, by the processing system, the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “extracting, by the processing system, a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting, by the processing system, a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating, by the processing system, a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing, by the processing system, the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data".
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A Human is capable of evaluating user inputs and determine a pattern based on a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs fail to satisfy a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe user inputs where those inputs fail to meet a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on an evaluation of a result of a query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating and observing a result from a system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on a use of calculation to obtain the result of the query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to perform and evaluate a calculation to obtain a result. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the first MP is adapted based on a satisfaction comparison.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the first MP is adapted based on a satisfaction comparison.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
The claim recites, “A system comprising: one or more processors; and a memory coupled with the one or more processors, the memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to effectuate operations comprising:” therefore is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“detecting first data associated with training of a first machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting second data associated with training of a second machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a first pattern in the first data based on a first frequency threshold usage for the first data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a second pattern in the second data based on a second frequency threshold usage for the second data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a second MP in the second data based on the second pattern and based on a second MP frequency threshold usage;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.0S(f)).
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data".
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A Human is capable of evaluating user inputs and determine a pattern based on a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs that fail to satisfy a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe user inputs where those inputs fail to meet a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on an evaluation of a result of a query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating and observing a result from a system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on a use of calculation to obtain the result of the query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to perform and evaluate a calculation to obtain a result. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the first MP is adapted based on a satisfaction comparison.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the first MP is adapted based on a satisfaction comparison.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 15, recites, “A computer readable storage medium storing computer executable instructions that when executed by a computing device cause said computing device to effectuate operations comprising:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“detecting first data associated with training of a first machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting second data associated with training of a second machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human can reasonably observe and evaluate data to locate training data sets which are associated with training machine learning models. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a first pattern in the first data based on a first frequency threshold usage for the first data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a second pattern in the second data based on a second frequency threshold usage for the second data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a dataset to observe patterns based on frequency of occurrence in a dataset. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identifying a second MP in the second data based on the second pattern and based on a second MP frequency threshold usage;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating a set of patterns to identify further patterns based on a frequency of occurrences in the set of patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data".
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides
significantly more than the abstract idea. The additional elements, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A Human is capable of evaluating user inputs and determine a pattern based on a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on determining a number of user inputs fail to satisfy a pattern threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe user inputs where those inputs fail to meet a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on an evaluation of a result of a query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is capable of evaluating and observing a result from a system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the first MP is based on a use of calculation to obtain the result of the query.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to perform and evaluate a calculation to obtain a result. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 8, 9, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., (Chen et al., “Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network”, 2015, hereinafter “Chen”) in view of Khan et al., (Khan et al., “Ensemble of optimal trees, random forest and random projection ensemble classification”, 2020, hereinafter “Khan”).
Regarding claim 1, Chen discloses, “A method comprising:” (Introduction, pp. 1; “To make an accurate prediction, this paper analyses the main factors that affect the prediction performance and proposes a prediction method that proves to be more accurate and effective.)
“detecting, by a processing system including a processor, first data associated with training of a first machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“detecting, by the processing system, second data associated with training of a second machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“identifying, by the processing system, a first pattern in the first data based on a first frequency threshold usage for the first data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“identifying, by the processing system, a second pattern in the second data based on a second frequency threshold usage for the second data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“providing, by the processing system, the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” (Base Prediction Models, pp. 5; “As we know, diversity is necessary for the survival and evolution of species ensemble model. So as for the performance of the prediction models, it is important to introduce the diversity to the prediction ensemble model. To guarantee the prediction performance; the base prediction models should be firstly selected. Besides the prediction models mentioned in Section 3, some other models are introduced. The guideline of choosing is based on the capacity and overheads.” This model uses multiple sets of models and combines two machine learning architectures. In the first step of the model, as seen in figure 3, the model uses a set of base prediction models. This art states that many diverse prediction models should be used to ensure greater accuracy. This article states that the system computing requirement is the limiting factor, however, it does not restrict which type of prediction models can be used.) And (system overview and preparation for prediction, pp. 2; “The output of fuzzy neural network is used to instruct the resource allocation in IaaS cloud center. Prediction results and the actual resource demands are evaluated using statistical analysis and different criteria. The evaluation results are fed back to the historical database to improve the prediction performance. The overview of resource demands prediction system is depicted in Figure 1.” The whole model in this article is designed for cloud computing and resource provisioning. The model will use historical data to make predictions about cloud service demands and resource allocation. This model would be able to evaluate data and instruct the resource allocation of a cloud center.)
Chen fails to explicitly disclose the remaining limitations of this claim. However, Khan is able to disclose, “identifying, by the processing system, a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage for the first data;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“identifying, by the processing system, a second MP in the second data based on the second pattern and based on a second frequency threshold usage for the second data;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“extracting, by the processing system, a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“extracting, by the processing system, a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“based on a trigger, creating, by the processing system, a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” (The Algorithm, pp. 100; “4. Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” After the models or trees have been evaluated, they are then ranked. The top ranked models are added to the main model one by one, representing their own functions, to generate a new ensemble model. The model is tested each time a new sub model is added to ensure the final model fits accuracy criteria or threshold. The top selected trees are added main model without training the main model.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Chen and Khan. Chen teaches a machine learning system that is able to predict and provide recommendations for cloud services and resource provisioning. Khan teaches a method which is able to generate a prediction model using a random tree model which consists of a combination of multiple sub models. One of ordinary skill would have motivation to combine a system that is able to use multiple prediction models in an ensemble style architecture with a system that is able to generate a new prediction or classification model that can evaluate network or user data, “In the case of classification problems, the new method is giving better results than the other methods considered on 9 data sets out of a total of 21 data sets and comparable to random forest on 1 data set. On 3 data sets, random forest gives the best performance. On three of the data sets, Mammographic, Appendicitis and SAHeart, node harvest classifier gives the best result among all other methods. SVM is better than the others on 3 data sets. Random projection ensemble gave better results on 3 data set.” (Khan, Discussion, pp. 109).
Regarding claim 2, Khan discloses, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” (The Algorithm, pp. 100; “Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” The model will take a set of prediction models and evaluate them. The most accurate trees will be selected and added to the main model. The remaining trees will not be selected because they fail to meet an accuracy requirement and are not used in the main model.) And (Figure 1, pp.101; This figure shows the workflow of the proposed model.)
Regarding claim 8, Chen discloses, “A system comprising: one or more processors; and a memory coupled with the one or more processors, the memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors to effectuate operations comprising:” (Experimental Evaluation, pp. 9; “In this section, experiments are conducted to validate the proposed prediction method. When we predict the fine-grained resource demands, the method of each kind of resource is similar to others. Here we do not distinguish resource type, and we use network traffic as the representation. From [42], we sample 400 days network visit traffic data. We use anterior 350 days traffic data as training data and posterior 50 days traffic data as test data. The training effect is shown in Figure 5.” This article discloses an experiment where they executed their method. This experiment was designed to be executed on a generic computing system able to handle and evaluate network data. This would lead one to believe they used a computing system which contains processors which are couple to memory which store the instructions for the given method.)
“detecting first data associated with training of a first machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“detecting second data associated with training of a second machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“identifying a first pattern in the first data based on a first frequency threshold usage for the first data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“identifying a second pattern in the second data based on a second frequency threshold usage for the second data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” (Base Prediction Models, pp. 5; “As we know, diversity is necessary for the survival and evolution of species ensemble model. So as for the performance of the prediction models, it is important to introduce the diversity to the prediction ensemble model. To guarantee the prediction performance; the base prediction models should be firstly selected. Besides the prediction models mentioned in Section 3, some other models are introduced. The guideline of choosing is based on the capacity and overheads.” This model uses multiple sets of models and combines two machine learning architectures. In the first step of the model, as seen in figure 3, the model uses a set of base prediction models. This art states that many diverse prediction models should be used to ensure greater accuracy. This article states that the system computing requirement is the limiting factor, however, it does not restrict which type of prediction models can be used.) And (system overview and preparation for prediction, pp. 2; “The output of fuzzy neural network is used to instruct the resource allocation in IaaS cloud center. Prediction results and the actual resource demands are evaluated using statistical analysis and different criteria. The evaluation results are fed back to the historical database to improve the prediction performance. The overview of resource demands prediction system is depicted in Figure 1.” The whole model in this article is designed for cloud computing and resource provisioning. The model will use historical data to make predictions about cloud service demands and resource allocation. This model would be able to evaluate data and instruct the resource allocation of a cloud center.)
Chen fails to explicitly disclose the remaining limitations of this claim. However, Khan is able to disclose, “identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“identifying a second MP in the second data based on the second pattern and based on a second MP frequency threshold usage;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” (The Algorithm, pp. 100; “4. Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” After the models or trees have been evaluated, they are then ranked. The top ranked models are added to the main model one by one, representing their own functions, to generate a new ensemble model. The model is tested each time a new sub model is added to ensure the final model fits accuracy criteria or threshold. The top selected trees are added main model without training the main model.)
Regarding claim 9, Khan discloses, “further comprising using the new machine learning model as a base model, wherein the creating of the new machine learning model includes ignoring a third pattern in the first data that is identified based on a third frequency threshold usage for the first data, wherein the third pattern fails to qualify as a third MP in the first data based on not satisfying a third MP frequency threshold usage for the first data.” (The Algorithm, pp. 100; “Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” The model will take a set of prediction models and evaluate them. The most accurate trees will be selected and added to the main model. The remaining trees will not be selected because they fail to meet an accuracy requirement and are not used in the main model.) And (Figure 1, pp.101; This figure shows the workflow of the proposed model.)
Regarding claim 15, Chen discloses, “A computer readable storage medium storing computer executable instructions that when executed by a computing device cause said computing device to effectuate operations comprising:” (Experimental Evaluation, pp. 9; “In this section, experiments are conducted to validate the proposed prediction method. When we predict the fine-grained resource demands, the method of each kind of resource is similar to others. Here we do not distinguish resource type, and we use network traffic as the representation. From [42], we sample 400 days network visit traffic data. We use anterior 350 days traffic data as training data and posterior 50 days traffic data as test data. The training effect is shown in Figure 5.” This article discloses an experiment where they executed their method. This experiment was designed to be executed on a generic computing system able to handle and evaluate network data. This would lead one to believe they used a computing system which contains processors which are couple to memory which store the instructions for the given method.)
“detecting first data associated with training of a first machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“detecting second data associated with training of a second machine learning model;” (System Overview and Preparation for Prediction, pp. 2; “Before prediction of user demands, we firstly analyze the user requests, including the utilization data structure, content, and number of historical resources. By analyzing the historical data, we may draw conclusions about user preference, demand description, and so forth.” This system is able to use and identify different types of data. This data is used to train the fuzzy model and the base predictors.)
“identifying a first pattern in the first data based on a first frequency threshold usage for the first data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“identifying a second pattern in the second data based on a second frequency threshold usage for the second data;” (Optimized Clustering Method, pp. 7; “Fuzzy clustering is an efficient technique for constructing the antecedent structures. The aim of clustering methods is to identify a certain group of data from a large data set, such that a concise representation of the behavior of the system is produced. Each cluster center can be translated into a fuzzy rule for identifying the class.” This model uses a Fuzzy network to further analyze the input data and historical data. Clustering will evaluate the data and place similar datapoints into similar clusters. In order for a data item to be allowed into a cluster certain threshold criteria must be met.) And (System Overview and Preparation for Prediction, pp. 2; “The output of the base predictors is sent to the fuzzy neural network as input. Fuzzy neural network uses the historical data and the base prediction value as training data, which improves the accuracy of the results.” The Fuzzy network will use the prediction data from the base prediction models. It will also use the training data which was used by the predictor models.)
“providing the new machine learning model to a network server of a communication network that causes the network server to provide provisioning functionality by using the new machine learning model to the communication network based on port assignment functionality associated with the first pattern function and port configuration functionality associated with the second pattern function.” (Base Prediction Models, pp. 5; “As we know, diversity is necessary for the survival and evolution of species ensemble model. So as for the performance of the prediction models, it is important to introduce the diversity to the prediction ensemble model. To guarantee the prediction performance; the base prediction models should be firstly selected. Besides the prediction models mentioned in Section 3, some other models are introduced. The guideline of choosing is based on the capacity and overheads.” This model uses multiple sets of models and combines two machine learning architectures. In the first step of the model, as seen in figure 3, the model uses a set of base prediction models. This art states that many diverse prediction models should be used to ensure greater accuracy. This article states that the system computing requirement is the limiting factor, however, it does not restrict which type of prediction models can be used.) And (system overview and preparation for prediction, pp. 2; “The output of fuzzy neural network is used to instruct the resource allocation in IaaS cloud center. Prediction results and the actual resource demands are evaluated using statistical analysis and different criteria. The evaluation results are fed back to the historical database to improve the prediction performance. The overview of resource demands prediction system is depicted in Figure 1.” The whole model in this article is designed for cloud computing and resource provisioning. The model will use historical data to make predictions about cloud service demands and resource allocation. This model would be able to evaluate data and instruct the resource allocation of a cloud center.)
Chen fails to explicitly disclose the remaining limitations of this claim. However, Khan is able to disclose, “identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“identifying a second MIP in the second data based on the second pattern and based on a second MP frequency threshold usage;” (OTE: optimal trees ensemble, pp. 99; “To this end, we partition the given training data L = (X,Y) randomly into two non-overlapping partitions, LB = (XB,YB) and LV = (XV,YV). Grow T classification or regression trees on T bootstrap samples from the first partition LB = (XB,YB). While doing so, select a random sample of p < d features from the entire set of d predictors at each node of the trees. This inculcates additional randomness in the trees.” This model will develop multiple different trees or models and then evaluate the models. Each tree is trained using different boot strapped training data subsets. The trees generated from the random boot strap clusters would represent a found pattern in that bootstrapped data subset.)
“extracting a first building block pattern function (first pattern function) from the first machine learning model, wherein the first pattern function is identified based on the first MP reaching a first pattern function frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“extracting a second building block pattern function (second pattern function) from the second machine learning model, wherein the second pattern function is identified based on the second MP reaching a second pattern function frequency threshold;” (The Algorithm, pp. 100; “Rank the trees in ascending order with respect to their prediction error on out-of-bag data. Choose the first M trees with the smallest individual prediction error.” Once the trees or models have been generated, they are evaluated. The individual trees will be further evaluated to ensure they meet a threshold or accuracy criteria. After the trees are evaluated the top most accurate trees will be added and tested with the main base model. Only the most accurate trees will be selected to be included in the main ensemble model.)
“based on a trigger, creating a new machine learning model based on a combination of the first pattern function and the second pattern function without training the new machine learning model; and” (The Algorithm, pp. 100; “4. Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” After the models or trees have been evaluated, they are then ranked. The top ranked models are added to the main model one by one, representing their own functions, to generate a new ensemble model. The model is tested each time a new sub model is added to ensure the final model fits accuracy criteria or threshold. The top selected trees are added main model without training the main model.)
Regarding claim 16, Khan discloses, “further comprising using the new machine learning model as a base model.” (The Algorithm, pp. 100; “Add the M selected trees one by one and select a tree if it improves performance on validation data, LV = (XV,YV), using unexplained variance and Brier score in cases of regression and classification as the respective performance measures.” The model will take a set of prediction models and evaluate them. The most accurate trees will be selected and added to the main model. The remaining trees will not be selected because they fail to meet an accuracy requirement and are not used in the main model.) And (Figure 1, pp.101; This figure shows the workflow of the proposed model.)
Claims 3-7, 10-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and Khan in view of Yao et al., (Yao et al., “RLPer: A Reinforcement Learning Model for Personalized Search”, 2020, hereinafter “Yao”).
Regarding claim 3,
Yao discloses, “wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Chen, Khan and Yao. Chen teaches a machine learning system that is able to predict and provide recommendations for cloud services and resource provisioning. Khan teaches a method which is able to generate a prediction model using a random tree model which consists of a combination of multiple sub models. Yao teaches a machine learning method which is able to evaluate user data and make recommendations or prediction based on that data. One of ordinary skill would have motivation to combine a system that is able to use multiple prediction models in an ensemble style architecture with a system that is able to generate a new prediction or classification model that can evaluate user network data and with a system that is able to evaluate user data and make recommendations or predictions, “In terms of all evaluation metrics, our RLPer model shows significant improvements on all baselines with paired test at p<0.01 level. Pay attention to our RLPer (off), it outperforms state-of-the-art HRNN model greatly, with 10.29% improvement on the metric MAP, 10.50% on MRR and 21.41% improvement on the Avg. Click metric. In addition, our RLPer also outperforms PSGAN a lot. It improves 9.14% on the MAP and 9.73% on the P@1 metric. PSGAN is a model to enhance the training data for HRNN based on GAN and achieves certain effects as presented in Table 2.” (Yao, Overall Performance, pp. 2305)
Regarding claim 4, Yao discloses, “wherein the first MP is based on determining a number of user inputs fail to satisfy a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur. This limitation is similar to claim 3, and it would be obvious to view the two limitations as taking in all user input, that fails or succeeds, to mean all user input.)
Regarding claim 5, Yao discloses, “wherein the first MP is based on an evaluation of a result of a query.” (RLPer – The proposed Model, pp. 2300; “As for the high level MDP of interactions, the user inputs a query
q
T
at each time step T. The search engine (agent) is expected to re-rank the documents based on both the inputted query and the user interests reflected in the search history. Therefore, we define the state at the step T as
s
T
=
H
T
,
q
T
,
D
T
.” As the user searches, the search engine will evaluate the search and re-rank the document list to reflect the user interests.)
Regarding claim 6, Yao discloses, “wherein the first MP is based on a use of calculation to obtain the result of the query.” (RLPer – The proposed Method, pp. 2301; “Reward R(S,A) provides supervision signals for the model training in reinforcement learning, used to measure the influence of actions. Due to we focus on using document pairs as the training data, we refer to the state-of-the-art pairwise LTR algorithm LambdaRank [6] to design our rewards. In LambdaRank, there is a matrix △ where each element
ℷ
i
,
j
means the difference between the metric values before and after exchanging the documents
d
i
and
d
j
in the ranking list. This matrix reflects the relative relationship of the documents.” The reward system used in this method will help find relative relationship in the document list. It uses a pairwise LTR algorithm to create the rewards from the patterns found in the user search history.)
Regarding claim 7, Yao discloses, “wherein the first MP is adapted based on a satisfaction comparison.” (RLPer – The proposed Model, pp. 2301; “Different from those supervised learning models which calculate the matrix △ on the document list recorded in the query log, we calculate it based on the currently returned personalized document list
D
'
T
in the interaction. Such real-time feedback reflects the user’s current interests which can help RLPer train the personalized ranking model better.” To get better personalized search results this system calculates the document list of interactions. The system can be provided feedback to provide a satisfaction of the user calculation based on the search results provided.)
Regarding claim 10, Yao discloses, “wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur.)
Regarding claim 11, Yao discloses, “wherein the first MP is based on determining a number of user inputs that fail to satisfy a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur. This limitation is similar to claim 3, and it would be obvious to view the two limitations as taking in all user input, that fails or succeeds, to mean all user input.)
Regarding claim 12, Yao discloses, “wherein the first MP is based on an evaluation of a result of a query.” (RLPer – The proposed Model, pp. 2300; “As for the high level MDP of interactions, the user inputs a query
q
T
at each time step T. The search engine (agent) is expected to re-rank the documents based on both the inputted query and the user interests reflected in the search history. Therefore, we define the state at the step T as
s
T
=
H
T
,
q
T
,
D
T
.” As the user searches, the search engine will evaluate the search and re-rank the document list to reflect the user interests.)
Regarding claim 13, Yao discloses, “wherein the first MP is based on a use of calculation to obtain the result of the query.” (RLPer – The proposed Method, pp. 2301; “Reward R(S,A) provides supervision signals for the model training in reinforcement learning, used to measure the influence of actions. Due to we focus on using document pairs as the training data, we refer to the state-of-the-art pairwise LTR algorithm LambdaRank [6] to design our rewards. In LambdaRank, there is a matrix △ where each element
ℷ
i
,
j
means the difference between the metric values before and after exchanging the documents
d
i
and
d
j
in the ranking list. This matrix reflects the relative relationship of the documents.” The reward system used in this method will help find relative relationship in the document list. It uses a pairwise LTR algorithm to create the rewards from the patterns found in the user search history.)
Regarding claim 14, Yao discloses, “wherein the first MP is adapted based on a satisfaction comparison.” (RLPer – The proposed Model, pp. 2301; “Different from those supervised learning models which calculate the matrix △ on the document list recorded in the query log, we calculate it based on the currently returned personalized document list
D
'
T
in the interaction. Such real-time feedback reflects the user’s current interests which can help RLPer train the personalized ranking model better.” To get better personalized search results this system calculates the document list of interactions. The system can be provided feedback to provide a satisfaction of the user calculation based on the search results provided.)
Regarding claim 17, Yao discloses, “wherein the first MP is based on determining a number of user inputs that satisfies a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur.)
Regarding claim 18, Yao discloses, “wherein the first MP is based on determining a number of user inputs fail to satisfy a pattern threshold.” (RLPer – The proposed Model, pp. 2301; “Then, this query and document list with real-time clicks are added to the user’s search history for building new user profile. With the clicked document list, we create a set of document pairs
P
T
, and the agent takes action
a
t
T
to judge the relative relationship of the two documents in the pair
P
t
T
step by step in the low level MDP. All the document pairs in
P
T
, the actions and the corresponding rewards are collected to update the personalized ranking model from
M
T
to
M
T
+
1
.” This model will take in user input and evaluate and compare it to the document list. The list is them evaluated and rewards are used to update the model. Under the broadest reasonable interpretation, a threshold has to be met in order for a reward to occur. This limitation is similar to claim 3, and it would be obvious to view the two limitations as taking in all user input, that fails or succeeds, to mean all user input.)
Regarding claim 19, Yao discloses, “wherein the first MP is based on an evaluation of a result of a query.” (RLPer – The proposed Model, pp. 2300; “As for the high level MDP of interactions, the user inputs a query
q
T
at each time step T. The search engine (agent) is expected to re-rank the documents based on both the inputted query and the user interests reflected in the search history. Therefore, we define the state at the step T as
s
T
=
H
T
,
q
T
,
D
T
.” As the user searches, the search engine will evaluate the search and re-rank the document list to reflect the user interests.)
Regarding claim 20, Yao discloses, “wherein the first MP is based on a use of calculation to obtain the result of the query.” (RLPer – The proposed Method, pp. 2301; “Reward R(S,A) provides supervision signals for the model training in reinforcement learning, used to measure the influence of actions. Due to we focus on using document pairs as the training data, we refer to the state-of-the-art pairwise LTR algorithm LambdaRank [6] to design our rewards. In LambdaRank, there is a matrix △ where each element
ℷ
i
,
j
means the difference between the metric values before and after exchanging the documents
d
i
and
d
j
in the ranking list. This matrix reflects the relative relationship of the documents.” The reward system used in this method will help find relative relationship in the document list. It uses a pairwise LTR algorithm to create the rewards from the patterns found in the user search history.)
Conclusion
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147