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 amendment filed on Jun. 10th, 2026. The amendments are linked to the original application filed on Apr. 16th, 2021.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 Sep. 8th, 2025 has been entered.
Response to Amendment
Regarding Claim Rejections – 35 U.S.C. 101 Rejection
The applicant requests the current rejection under 35 U.S.C. 101 be withdrawn because the current claims overcome a 101 rejection. The applicant relies on previous stated reasons, in previous responses, and provides further arguments supporting the withdrawal of the 101 rejection. The examiner notes no further amendments to the claims and the applicant relies prior amendments to overcome both rejections, 101 and 103. The examiner has reviewed the previous office actions, current claims, specification, and has found the claims still recite abstract ideas and further evaluation using the Alice/Mayo test is required. The applicant has provided multiple supporting arguments as to why the claims overcome the 101 rejection and is discussed below.
First, the applicant reemphasizes Ex parte Desjardins and the Appeal Review Panel (ARP) decision to withdraw the 101 rejection in that case. In particular, the applicant highlights the withdrawal was based on integration of the claims into a practical application using the remaining elements and when the claims are viewed as a whole in light of the specification. Further, the applicant emphasizes the ARP stated that the Board took too broad of an interpretation of the claims and did not incorporate the technical improvements provided in the claims. Finally, the applicant highlights the importance of evaluation of the additional claims and how additional limitations can be used to integrate the claimed subject matter into a practical application by providing a technical improvement. The examiner would like to note that every application, new or amended, is evaluated for subject matter eligibility using the Alice/Mayo test per MPEP 2106. This includes determining the Broadest reasonable interpretation, or BRI, of the claims and taking that interpretation to evaluate claims using the Alice/Mayo test. Further included in this test, is evaluating, if any, remaining limitations integrate the concept into a practical application (Step 2A, Prong 2). The examiner would like to note that the claims in this application have been evaluated per the MPEP and has followed the flowchart provided by the MPEP. This includes evaluation of claims, if needed, under Step 2A, Prong 2 as well as in light of the Ex parte Desjardins decision.
Next, applicant states the claims, even if they recite abstract concepts, further recite technical improvements and sufficient subject matter to integrate the claimed invention into a practical application. The applicant states that the current invention discloses the use of building block functions from different models to generate a new machine learning model without training. The applicant argues that this limitation, in conjunction with other claim limitations, provide a concrete improvement to creating and deploying machine learning models. To support this argument, the applicant has also provided paragraphs from the specification which further disclose the technical improvements of the claimed invention. The examiner has reviewed the claims and specification and believes the current claims fail to recite a concrete technical process. Using the BRI of the claims, the process recited is still broad and does not recite sufficient technical elements as for one of ordinary skill in the art to readily notice the claimed improvements to machine learning. As the claims have been composed, they recite generic process of pattern identification and combination to generate machine learning models. The examiner believes the claims fail to reflect the claimed improvements as stated in the specification and in the paragraphs noted by the applicant in this remarks. The limitations are interpreted, as a result of the claim language and in light of the specification, to be generic algorithms, “pattern functions”, to find repeating patterns in data based on set thresholds. Therefore, the examiner believes this limitation is reciting mere instructions to implement abstract concepts, identified in previous and current claims, on a generic computer per MPEP 2106.05(f).
Next, the examiner would like to note the generation of machine learning models, without training, itself is not a novel concept in machine learning. Currently, or from the effective filing date of this application, ML methods such as zero-shot learning, certain reinforcement learning models, decision trees, etc., (see 103 rejection below) use this concept of generating models with limited or no training. Since the concept of generation models without training is not novel, the claimed process would be required to prove an improvement to the model generation process. As stated by the examiner, the claims recite a broad process of pattern combination. The examiner does not believe this to be an overly broad interpretation of the limitations because the claims fail to recite technical concepts which would narrow the interpretation of the claims.
Next, the applicant states Desjardins and the current invention are similar because both cases recite mathematic/mental concepts/process in the claims but the ARP found that the remaining limitations of the claims as a whole and in light of the specification recite sufficient subject matter as to provide a technical improvement which integrates the practical concept or invention into a practical application. The examiner has reviewed the claimed subject matter and the ARP decision for Desjardins and does see some comparisons between the cases. However, the examiner believes that the claims in Desjardins are starkly different than the claimed invention. First, the Desjardins discloses the training of a machine learning model for a multi-task training system that utilizes multiple different models and methods to minimalize critical forgetting. The examiner would like to note that the claims in Desjardins disclose a thorough method that includes generating, storing and handling of model parameters for one or more models. Further, Desjardins discloses, in the claims, an explicit training method to store, handle, and generate models using parameters and other claimed technical processes. The examiner notes that the claims in Desjardins, at the time of the ARP, explicitly disclosed a concrete process to train machine learning models without suffering critical forgetting of previously stored training information. Keeping this in mind, the examiner would like to note the current claims in this application lack the level of disclosure and detail as compared to Desjardins claims. The current claims recite a generic process of identifying and evaluating training data and further manipulation of the that data to generate a new model “without training”. As in Desjardin case, the claims obviously recite abstract ideas, as a human using a generic computing system is able to identify and review training data. However, as stated above, this is where the comparisons end.
Next, the applicant states that the Office should caution in an overly broad interpretation as in Desjardins. As stated above, the examiner does not believe the interpretations taken are overly broad. The examiner would again like to note the claims in Desjardins explicitly recited technical elements, such as parameters and weights, as to narrow the interpretation of the claims which required restricted interpretations. The Office has interpreted the current claims to be broader because the claims fail to disclose further technical elements which would narrow the claims further.
Next, the applicant argues they have provided a real-world technical deployment of the claimed invention to recite the specific architecture to deploy a generated model. The applicant again draws a comparison from Desjardins. The examiner has reviewed the claim limitation, “providing, by the processing system, the new machine learning model …” from the independent claims. As claimed, this limitation discloses a process where the new generated model is sent to network server to perform designated tasks which it was trained to perform. The examiner has interpreted this limitation merely recite a process of sending a ML model, trained or without training, over a network to another location where it performs its designed function. The examiner is not convinced that this limitation alone or in combination with the claims as a whole integrate the claimed invention into practical application because it fails to disclose any technical improvement to machine learning or technical field.
Next, the applicant points to comparisons with Example 47 from the USPTO Subject Matter Eligibility Examples and the current invention. The applicant states that the current claims also integrate abstract ideas into practical application by, “creating an operational machine learning capability by extracting building-block pattern functions from trained models and creating a new machine learning model from those building blocks without training, and then deploying that new model to a network server for use in a communication network”. As stated above, the claims language have allowed for a broad interpretation of the claims. The claims do recite a process of extracting patterns of data from models and training data to compose a new model however the claims still fail to recite further technical process or details that would restrict a broad interpretation. The claims are interpreted to currently recite a generic process of pattern identification, evaluation and combination. A person of ordinary skill in the art could evaluate the claims and fail to identify the proposed concrete technical improvement even in light of the specification.
Next, the applicant argues further that Example 47 Claim 3, claim 1, recites a technical process which is deployed in a particular manner and as such is considered a technical improvement. As stated above, the examiner has evaluated the last limitation in the independent claims to be a well understood, routine or conventional process of sending information over a network. The examiner does not believe the claim recites a technical improvement in network provisioning or port assignment and instead the claim recites a process of sending model from one system, after creation, to another system in the network to perform the claimed technical tasks the model was created to execute. The examiner does not find this a to be technical improvement and therefore fails to integrate the claimed invention as a whole into a practical application.
Finally, the applicant argues that the current claims recite a technical process which would integrate any abstract concepts in to a practical application. The application points to the last two limitations in the independent claims including 8 and 15. Further the application argues that these claims recite a specific technical process of generating a new machine learning model from extracting building blocks from other trained models. As stated above, the examiner has found the remaining elements of the independent claims to recite mere instructions to implement an abstract concept on a generic computing system and well understood routine and/or conventional processes.
For clarity of the record, the examiner will explicitly disclose the evaluation of the additional claim limitations of claim 1 under Steps 2A, Prong 2.
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)).
The current claim limitation is interpreted by the examiner using the BRI as disclosed above and by the MPEP. The BRI of this claim is: Extracting a building block pattern, block of data, from one ML model where the extracted block is identified after the block meets a designated pattern frequency threshold. The claim recites generic elements such as the “first building block pattern function”, “first pattern function” which have been interpreted to be blocks of data. These element have also been identified in prior limitations be abstract concepts in Step 2A, Prong 1. The limitation alone fails to disclose an improvement to machine learning or a technical field. As a whole the claim merely recites a process using a computing system to automatically extract data after a frequency pattern occurs. Therefore, the examiner believes the claim recites instructions to implement an abstract ideas on a computer and is similar to 2106.05(f) as it fails to integrate the concepts into a practical application.
"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)).
The current claim limitation is interpreted by the examiner using the BRI as disclosed above and by the MPEP. The BRI of this claim is: based on a trigger, create a new machine learning model by combining the identified pattern functions without training a new machine learning model. As stated above, the concept of model generation is not novel and there are examples of this process. Therefore, the claim is required to further discloses the improvement beyond non novel concepts. When revieing the claims as a whole the claim uses the generated mature patterns and combines them using a generic process. The claims fail to list further technical processes that occur, merely the automatic generation of a model using automatically generated patterns, which are interpreted to be abstract concepts. Therefore, the claim merely recites instructions, i.e. create a model, to implement abstract ideas, i.e. the identified patterns in prior limitations, on a generic computing system.
"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.
The current claim limitation is interpreted by the examiner using the BRI as disclosed above and by the MPEP. The BRI of this claim is: the created model is sent to a network server of network which causes the networks server to perform actions such as provisioning functionality, which is related the patterns noted from one pattern function, and port assignment functions, which is related to the patterns noted from another pattern function. The claim recites a process of sending a generated model over a network to another system so the model can perform the function it was generated to execute. Taken alone, this claim fails to recite any technical improvements and merely claims a well understood, routine and/conventional process of transmitting data over a network. Therefore, further evaluation of this claim under Step 2B is required. Next, taken with the claims as a whole, this limitation fails to recite a technical improvement to machine learning or a technical field.
As stated above, the current claims have been evaluated for subject matter eligibility using the Alice/Mayo test per the MPEP. After reevaluation of the current claims the examiner has found that the claims do not recite patent eligible subject matter and therefore are still rejected under 35 U.S.C. 101. The examiner notes previous office actions as support of this decision as well as the remarks above and the 101 rejection below.
Regarding Claim Rejections – 35 U.S.C. 103 Rejection
The applicant argues that he current proposed arts of Chen and Khan fail to teach key elements of the claims. The applicant states the arts fail to teach, “identifying ... 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.” The applicant argues that the arts fail to teach the pattern promotion from a pattern to a mature pattern as disclosed in the claims.
First, the applicant discloses an overview of Chen and states Chen is unable to teach the cited claimed limitation. The examiner would like to note that Chen is not relied upon to teach the cited claimed limitation and therefore agrees with the applicant. Chen fails to explicitly teach the cited limitation.
Next, the applicant gives an overview of Khan and states Khan fails to teach the identification of patters in training data as well as promote pattern to mature patters based on a threshold. The examiner would like to note the claims are evaluated using the BRI. The cited claim limitation discloses a process of identifying mature patterns based on frequencies of these pattern and thresholds. Khan discloses a process which contains an ensemble of trees or classifications based on identified patterns. The different predictions are used to generate trees of similar patterns or variables. The system will, after a threshold event is met, generate a new subtree and split and from main or other trees in the network. The subtrees are interpreted to disclose the different pattern functions and these trees grow over time to produce more mature and reliable predictions trees. Next the applicant argues that the this process of pruning or gating fails to reflect a frequency based process as claimed. As stated above the BRI of the claims was used to map the claim to the proposed art. Khan discloses a process that splits trees into an ensemble of trees but it is also able to determine, after evaluation, optimal trees able to vote in an ensemble system. This evaluation uses an algorithm, i.e. Brier score, which evaluates the cases and uses the total # of test instances, i.e. frequency, of certain values. Therefore, the examiner believes the current proposed art teaches a system which is able to identify mature patterns, the optimal trees, in the first data, i.e. the prediction nodes connected to the trees and subtrees, based on a frequency threshold, i.e. see equation
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on pp. 100 of Khan. The examiner believes this also follows the pattern to mature pattern scheme disclosed in the claims. Khan discloses a system able to generate subtrees, i.e. pattern functions, and over time promote certain frequent patterns to more mature patterns, i.e. optimal trees.
Next, the applicant states that the combination of Khan and Chen fails to teach the claimed elements for the reasons stated above and would require hindsight reconstructions or overly broad generalizations. As stated in the previous office action, the examiner believes that one of ordinary skill would be motived to combine the teaches and different elements of the Khan and Chen to disclose the claimed invention. One of ordinary skill in the art would have motivate to review and research similar clustering concepts such as fuzzy clustering, as proposed in Chen, as well as common regression trees as disclosed in Khan. One could have motivation to combine these arts to improve classification and/or identification and manipulation of patterns in data. The concepts in both articles can be used in conjunction with one another and/or share similar core concepts.
Finally, after consideration of the claims, remarks, specification and proposed prior arts, the examiner believes the proposed arts Khan and Chen teach the claimed subject matter. Further, as stated above, the examiner believes one of ordinary skill in the art would have motivation review similar cluster concepts of Khan and Chen to disclose the claimed invention prior to the effective filing date of this application. Therefore, the current rejection under 35 U.S.C. 103 has been 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 M PEP§ 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 § 2106.05(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 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 M PEP 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 data points 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. model would be able to evaluate data and instruct the resource allocation of a cloud center.)
Chen fails to explicitly 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;"
"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;"
"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;"
"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;"
"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"
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 nonoverlapping 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 nonoverlapping 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 fora 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 laaS 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:
"identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;"
"identifying a second MP in the second data based on the second pattern and based on a second MP frequency threshold usage;"
"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;"
"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;"
"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"
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 nonoverlapping 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 nonoverlapping 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 data points 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 data points 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 modeI, as seen in figure 3, the modeI 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 laaS 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:
"identifying a first mature pattern (MP) in the first data based on the first pattern and based on a first MP frequency threshold usage;"
"identifying a second MIP in the second data based on the second pattern and based on a second MP frequency threshold usage;"
"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;"
"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;"
"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"
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 nonoverlapping 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 nonoverlapping 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 Page 72 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 fora 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
a
s
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 Lambda Rank [6] to design our rewards. In LambdaRank, there is a matrix Δ where each element
λ
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 Mode I, pp. 2301; "Different from those supervised learning models which calculate the matrix Δ 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
a
s
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 Lambda Rank [6] to design our rewards. In LambdaRank, there is a matrix Δ where each element
λ
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 Mode I, pp. 2301; "Different from those supervised learning models which calculate the matrix Δ 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
a
s
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 Lambda Rank [6] to design our rewards. In LambdaRank, there is a matrix Δ where each element
λ
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/HASSAN MRABI/Primary Examiner, Art Unit 2147
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