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 arguments filed on 03/03/2025. Claims 1-19 and 21 are pending in the application and have been considered below.
Response to Applicant’s arguments
112b Rejection
Applicant submits, on page 11-12, that “the specification describes the limitations "densely"… and one of skill in the art would reasonably be apprised of the scope of the invention in light of the Applicant's specification.”
Examiner’s response:
Examiner respectfully disagrees. Examiner notes that the arguments and cited paragraphs describe how a densely populated graph is obtained but the degree or standard is not clear. As applicant stated, a densely populated region is where the nodes interact frequently. How frequent is frequent? Once a second or once a month etc. The distance at which it is considered highly correlated is not given a standard as well. At what distance is the nodes considered highly correlated. The term densely is not given a clear degree or standard making it indefinite.
101 Rejection
Applicant argues:
Applicant submits, on page 12-14, that “Applicant submits that the claim limitations, when interpreted as a whole, cannot practically be performed in the human mind. Therefore, the claimed limitations are not directed to mental processes.”
Examiner’s response:
Examiner respectfully disagrees. Examiner notes that the claims do not describe what occurs to the determining and combining step in a way that would differentiate this process from the way a person could mentally perform the community detection algorithm to determine and combine the edges together based on an evaluated commonality. Examiner interprets the processor performing these algorithms and steps as merely applying the abstract idea on a generic computer.
Applicant argues:
Applicant submits, on page 14-16, that “the claimed invention provides technical improvements as described in the specification and claims.”
Examiner’s response:
Examiner respectfully disagrees. Examiner interprets the processor, building and training the predictive model as merely applying the abstract idea on a generic computer. The receiving and transmitting steps are interpreted as extra solution activities of mere data gathering. Examiner notes the specification set forth an improvement but in a conclusory manner. The cited “improvements” do not show how the functioning of the model itself is improved.
102 Rejection
Applicant submits, on page 18-19, that “the Banerjee reference does not qualify as prior art based on the prior art exception under”
Examiner’s response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 11 the phrases "densely" render the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
The term “densely” in claims 1 and 11 is a relative term which renders the claim indefinite. The term “densely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Correction is required.
The dependent claims are rejected because they inherit the deficiency of the independent claims they depend upon.
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.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“detecting, based at least in part on a community detection algorithm, a plurality of communities from a topological graph, each community of the plurality of communities including a subset of nodes of the topological graph that are densely connected within a group, the topological graph being based on the new set of previous requests and a stored set of historical requests, the topological graph including nodes and edges connecting the nodes,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate a topological graph and perform an algorithm to detect a plurality of communities.
“inferring one or more edge connections between the nodes of the topological graph using an optimization algorithm, the one or more inferred edge connections reducing a cost function based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests, wherein the inferred edge connections are connections between nodes for which path information indicates that a relationship exists, despite a lack of factual edges representing the connections,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate a topological graph and perform an algorithm to infer edge connections to reduce a cost function.
“including the one or more inferred edge connections into the topological graph,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could mentally include/take note of the inferred edge on the graph.
“combine two or more paths of nodes and edges into a single path based on a commonality of the two or more paths to create a smoothed topological graph,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could combine two or more paths into a single path to create a smoothed topological graph.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“a system memory; one or more processors; and a computer readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new set of previous requests and results associated with the new set of previous requests,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application, stop the building of the predictive model when no new information is received,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new request from a client device in real time,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“apply the new request to the software application to obtain a request score,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and transmit the request score to the client device to make predictions for data in the new request.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“a system memory; one or more processors; and a computer readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new set of previous requests and results associated with the new set of previous requests,” (well-understood, routine, conventional MPEP 2106.05(d))
“build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application, stop the building of the predictive model when no new information is received,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new request from a client device in real time,” (well-understood, routine, conventional MPEP 2106.05(d))
“apply the new request to the software application to obtain a request score,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and transmit the request score to the client device to make predictions for data in the new request.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: generate a set of binary decision rules using [the predictive model] and the topological graph, the binary decision rules setting a threshold value for a continuous score determined by the predictive model.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“the predictive model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the predictive model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The computer system of claim 2, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: load the predictive model and the set of binary decision rules into the system memory.” (insignificant extra-solution activity Storing and retrieving information in memory MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The computer system of claim 2, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: load the predictive model and the set of binary decision rules into the system memory.” (well-understood, routine, conventional MPEP 2106.05(d) Storing and retrieving information in memory)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: evaluate a performance of the predictive model based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests, and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“update a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“update a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 5:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The computer system of claim 4, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: build a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The computer system of claim 4, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: build a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: determine that the plurality of communities are different from a stored plurality of communities associated with a stored model, wherein the determination of the one or more inferred edge connections is performed based on the determination that the plurality of communities are different from the stored plurality of communities.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 7:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: determine that the smoothed topological graph is different from a stored topological graph associated with a stored model, wherein the building of the predictive model is performed based on the determination that the smoothed topological graph is different from the stored topological graph.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 8:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: determine that the predictive model is different from a stored model, and generate a set of binary decision rules using the predictive model, the generation of the set of binary decision rules being performed based on the determination that the predictive model is different from the stored model.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 9:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“evaluate a performance of each of the plurality of candidate models based on the results associated with the new set of previous requests and the stored results associated with the stored set of historical requests, and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“select the predictive model to be used as an operational model based on the predictive model having a higher evaluated performance compared to other models of the plurality of candidate models.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: build a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The computer system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: build a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The computer system of claim 1, wherein the community detection algorithm is a K-means clustering algorithm, the optimization algorithm is an Ant Colony algorithm, and the supervised machine learning algorithm is a gradient boosting machine.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“detecting, based at least in part on a community detection algorithm, a plurality of communities from a topological graph, each community of the plurality of communities including a subset of nodes of the topological graph that are densely connected within a group, the topological graph being based on the new set of previous requests and a stored set of historical requests, the topological graph including nodes and edges connecting the nodes,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate a topological graph and perform an algorithm to detect a plurality of communities.
“inferring one or more edge connections between the nodes of the topological graph using an optimization algorithm, the one or more inferred edge connections reducing a cost function based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests, wherein the inferred edge connections are connections between nodes for which path information indicates that a relationship exists, despite a lack of factual edges representing the connections,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate a topological graph and perform an algorithm to infer edge connections to reduce a cost function.
“including the one or more inferred edge connections into the topological graph,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could mentally include/take note of the inferred edge on the graph.
“combine two or more paths of nodes and edges into a single path based on a commonality of the two or more paths to create a smoothed topological graph,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could combine two or more paths into a single path to create a smoothed topological graph.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“a system memory; one or more processors; and a computer readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new set of previous requests and results associated with the new set of previous requests,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application, stop the building of the predictive model when no new information is received,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new request from a client device in real time,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“apply the new request to the software application to obtain a request score,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and transmit the request score to the client device to make predictions for data in the new request.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“receive a new set of previous requests and results associated with the new set of previous requests,” (well-understood, routine, conventional MPEP 2106.05(d))
“build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application, stop the building of the predictive model when no new information is received,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive a new request from a client device in real time,” (well-understood, routine, conventional MPEP 2106.05(d))
“apply the new request to the software application to obtain a request score,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and transmit the request score to the client device to make predictions for data in the new request.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 12:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, further comprising: generating a set of binary decision rules using [the predictive model] and the topological graph, the binary decision rules setting a threshold value for a continuous score determined by the predictive model.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“the predictive model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the predictive model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 12, further comprising: loading the predictive model and the set of binary decision rules into the system memory.” (insignificant extra-solution activity Storing and retrieving information in memory MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 12, further comprising: loading the predictive model and the set of binary decision rules into the system memory.” (well-understood, routine, conventional MPEP 2106.05(d) Storing and retrieving information in memory)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 14:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, further comprising: evaluating a performance of the predictive model based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests, and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“updating a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“updating a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
No
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 14, further comprising: building a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 14, further comprising: a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, further comprising: determining that the plurality of communities are different from a stored plurality of communities associated with a stored model, wherein the determination of the one or more inferred edge connections is performed based on the determination that the plurality of communities are different from the stored plurality of communities.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 17:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, further comprising: determining that the smoothed topological graph is different from a stored topological graph associated with a stored model, wherein the building of the predictive model is performed based on the determination that the smoothed topological graph is different from the stored topological graph.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, further comprising: determining that the predictive model is different from a stored model, and generate a set of binary decision rules using the predictive model, the generation of the set of binary decision rules being performed based on the determination that the predictive model is different from the stored model.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 19:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“evaluating a performance of each of the plurality of candidate models based on the results associated with the new set of previous requests and the stored results associated with the stored set of historical requests, and” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
“selecting the predictive model to be used as an operational model based on the predictive model having a higher evaluated performance compared to other models of the plurality of candidate models.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 11, further comprising: building a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 11, further comprising: building a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model,” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 21:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, wherein the supervised machine learning algorithm is initialized using optimized parameters set by an optimized modeling behavior tree.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)).
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6, 8, 9, 11, 16, 19, and 19 are rejected under 35 U.S.C. 103 as being unpatentable Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge)
As to claim 1, Banerjee teaches a computer system for building machine learning models, the computer system comprising:
a system memory; one or more processors and a computer readable storage medium storing instruction that, when executed by the one or more processors, cause the one or more processors to: (Banerjee Fig 6 and Paragraph 0077; “This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media. This can also include nontangible computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information and which can be accessed by computer system 600.”);
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receive a new set of previous requests and results associated with the new set of previous requests (Banerjee Paragraph 0004; “such transaction data may include previous secured data transfers, and other multi-user transactions and interactions from communication systems, social networking systems, financial systems, digital asset systems, and the like.” Banerjee Paragraph 0020; “a transaction analyzer server may receive transaction requests including data identifying one or more entities associated with requested transactions.” Examiner notes that a new set of previous requests (transaction requests included previous secured data transfers) and results associated with the new set of previous requests (data identifying one or more entities associated with requested transactions));
the topological graph being based on the new set of previous requests and a stored set of historical requests, the topological graph including nodes and edges connecting the nodes (Banerjee Paragraph 0015; “FIG. 10 is an example graph-oriented data structure including graphical data representing a device and a plurality of related users and transfers, according to one or more embodiments of the disclosure.”);
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Banerjee fails to teach detecting, based at least in part on a community detection algorithm, a plurality of communities from a topological graph, each community of the plurality of communities including a subset of nodes of the topological graph that are densely connected within a group
However, Segura does teach detecting, based at least in part on a community detection algorithm, a plurality of communities from a topological graph, each community of the plurality of communities including a subset of nodes of the topological graph that are densely connected within a group (Segura Fig 3 and Paragraph 0023; “Applying community detection to a set of data or network before obtaining a behavioral parameter allows differentiating parameters depending on a community. As a difference with respect to the state of the art, the invention allows these parameters be not only obtained for a set of aggregated data but customized for every community comprised within the set of data. Community detection allows grouping nodes of a set of data into potentially overlapping heterogenic data such that each set of heterogenic data is densely connected. Communities may comprise pairs of set of heterogenic data are more likely to be connected if they are both members of the same community, and less likely to be connected if they do not share communities.” Segura Paragraph 0108; “FIG. 3 shows the results of applying several different community detection algorithms over a same graph. The results of community detection algorithm allow the emergence of communities of venues that are related in terms of shared clients. As it can be seen, different sub-communities may be obtained by applying different criteria of community detection.” Examiner notes that based at least in part on a community detection algorithm, a plurality of communities from a topological graph (Fig 3 shows topological graph that have been separated into communities), each community of the plurality of communities include a subset of nodes (sub-communities) of the topological graph that are densely connected),
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee and Segura. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. One of ordinary skill would have motivation to combine Banerjee and Segura to perform more accurate calculations “In a particular example further information about the items may be available, advantageously allowing to perform more accurate calculations” (Segura Paragraph 0093).
Banerjee in view of Segura does not teach Inferring one or more edge connections between the nodes of the topological graph using an optimization algorithm, the one or more inferred edge connections reducing a cost function based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests
wherein the inferred edge connections are connections between nodes for which path information indicates that a relationship exists, despite a lack of factual edges representing the connections, including the one or more inferred edge connections into the topological graph,
However, Train does teach Inferring one or more edge connections between the nodes of the topological graph using an optimization algorithm, the one or more inferred edge connections reducing a cost function based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests, (Train Column 15 Line 58; “Through this enable process, the RFS nodes connected to the RF may be utilized to inject and spread the information to other nodes that are immediately connected to the RFS nodes. This operation has the effect of reducing the diameter of the route distribution graph as routing data only has to travel the maximum number of hops through the existing routing protocol from any RFS node to a non-RFS node. Being able to diminish the diameter of the route distribution graph reduces the convergence time of the network as other routers can learn of the existence of a new edge or the removal of an edge faster through this process than if the message was propagated throughout the entire diameter of the network while being subject to hold timers and route processing times.” Examiner notes that one or more edge connections (new edge) is inferred/learn existence of between the nodes of the topological graph (RFS nodes) using an optimization algorithm (this enable process to reduce the diameter of the route distribution graph), the one or more inferred edge connections reduce a cost function (reducing the diameter of the route distribution graph) based on the results associated with the new set of previous requests (existence of new edge) and stored results associated with the stored set of historical requests (routing data through existing edges))
wherein the inferred edge connections are connections between nodes for which path information indicates that a relationship exists, despite a lack of factual edges representing the connections, including the one or more inferred edge connections into the topological graph, (Examiner refers to previous mapping and Train Column 20 Line 34; “If a new edge between networks is distributed by the RF, the local RFS node may be interested in determining if this addition of a new edge will create a shorter path than the ones that currently exist in the BGP routing database. Therefore, the local RFS node may perform an analysis of all the paths to all destination identifiers and determine if the addition of this edge creates a shorter path.” Examiner notes that the inferred edge connections are connections between nodes for which path information indications relationship exists (RF may be utilized to inject and spread the information to other nodes… learn of the existence of a new edge), despite lack of factual edges representing connections (adding a new edge means there was no factual/existing edge there before), including the one or more inferred edge connections into the graph (determining if this addition of a new edge will create a shorter path))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, and Train. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. One of ordinary skill would have motivation to combine Banerjee, Segura, and Train to reduce the transmission costs, propagation delays, and security vulnerabilities “To address the challenges discussed above, a novel paradigm is implemented in some embodiments of the present invention for reducing the transmission costs, propagation delays, and security vulnerabilities of existing hop-by-hop route propagation infrastructures by augmenting them with one-to-many broadcast-based route distribution mechanisms.” (Train Column 4 Line 65).
Baner in view of Segura in further view of Train do not teach combine two or more paths of nodes and edges into a single path based on a commonality of the two or more paths to create a smoothed topological graph
However, Bereg does teach combine two or more paths of nodes and edges into a single path based on a commonality of the two or more paths to create a smoothed topological graph (Bereg Abstract; “In general, the method creates clear and smooth edge routes giving an overview of the global graph structure, while still drawing each edge separately and thus enabling local analysis.” Bereg Page 4 Paragraph 1; “if an edge is used by several paths its length is counted only once. A set of paths sharing an edge of the graph is called a bundle.” Examiner notes that two or more paths of nodes and edges (set of paths sharing an edge) are combined/bundled based on a commonality of the two or more paths (sharing an edge) to create a smoothed topological graph (method creates clear and smooth edge routes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, and Bereg. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, and Bereg to create clear and smooth edge routes while enabling local analysis “the method creates clear and smooth edge routes giving an overview of the global graph structure, while still drawing each edge separately and thus enabling local analysis.” (Bereg Abstract).
Banerjee in view of Segura in further view of Train in further view of Bereg does not teach build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application,
stop the building of the predictive model when no new information is received
receive a new request from a client device in real time
apply the new request to the software application to obtain a request score, and
transmit the request score to the client device to make predictions for data
in the new request
However, Breckenridge does teach build and iteratively train a predictive model based on the smoothed topological graph using a supervised machine learning algorithm, the plurality of communities, the results associated with the new set of previous requests, data associated with the new set of previous requests, the stored results associated with the stored set of historical requests, and the data associated with the stored set of historical requests, thereby resulting in a software application, (Breckenridge Paragraph 0037; “Some examples of training functions that can be used to train a static predictive model… other machine learning training functions (e.g., Naive Bayes, k-nearest neighbors, Support Vector Machines, Perceptron).” Breckenridge Paragraph 0088; “FIG. 8 is a flowchart showing an example process 800 for maintaining an updated dynamic repository of trained predictive models. The repository of trained predictive models is dynamic in that new training data can be received and used to update the trained predictive models included in the repository by retraining the updateable trained predictive models and regenerating the static and updateable trained predictive models with updated training data.” Examiner notes that a predictive model is build and iteratively trained (flowchart shown in Fig 8) using a supervised machine learning algorithm (Naive Bayes, k-nearest neighbors, Support Vector Machines, Perceptron) based on training data, thereby resulting in a software application (trained predictive model))
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stop the building of the predictive model when no new information is received
(Examiner refers to previous mapping to show that the predicative model is stop being built (all conditions are met) when no new information is received (when all conditions are met, no new training data is received));
receive a new request from a client device in real time (Breckenridge Paragraph 0025; “The client computing system 104a can transmit prediction requests 108a over the network. The selected trained model executing in the data center 112 receives the prediction request, input data and request for a predictive output, and generates the predictive output 114. The predictive output 114 can be provided to the client computing system 104a, for example, over the network 102.” Examiner notes that a new request from a client device is received in real time (client computing system 104a can transmit prediction requests 108a over the network)),
apply the new request to the software application to obtain a request score, and
transmit the request score to the client device to make predictions for data
in the new request (Examiner notes that the request (prediction request) is applied to the software application (selected trained model) to obtain a request score (predictive output) and transmits the request score to the client device to make predictions for data in the request (The predictive output 114 can be provided to the client computing system)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, and Breckenridge. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, and Breckenridge to easily correct the trained predictive model based on erroneous predictive output “An updateable trained predictive model that gives an erroneous predictive output can be easily and quickly corrected, for example, by providing the correct output as an update training sample upon detecting the error in output.” (Breckenridge Paragraph 0010).
As to claim 6, which incorporates the rejection of claim 1, Banerjee does not teach
However, Segura does teach wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: determine that the plurality of communities are different from a stored plurality of communities associated with a stored model, (Segura Paragraph 0031; “Advantageously a parameter which is generated by a method according to the invention represents a tendency depending on a community. The community may coincide with the community a final system or user belongs to or may be a different one, if it is selected” Examiner notes that the plurality of communities are different (The community may coincide with the community a final system or user belongs to or may be a different one, if it is selected) from a stored plurality of communities associated with a stored model (final system)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee and Segura. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. One of ordinary skill would have motivation to combine Banerjee and Segura to perform more accurate calculations “In a particular example further information about the items may be available, advantageously allowing to perform more accurate calculations” (Segura Paragraph 0093).
Banerjee in view of Segura does not teach wherein the determination of the one or more inferred edge connections is performed based on the determination that the plurality of communities are different from the stored plurality of communities
However, Tran does teach wherein the determination of the one or more inferred edge connections is performed based on the determination that the plurality of communities are different from the stored plurality of communities (Train Column 15 Line 57; “the RFS nodes connected to the RF may be utilized to inject and spread the information to other nodes that are immediately connected to the RFS nodes… Being able to diminish the diameter of the route distribution graph reduces the convergence time of the network as other routers can learn of the existence of a new edge” Examiner notes that the determination of the one or more inferred edge connections (learn of the existence of a new edge) is based on the determination that the plurality of communities are different from the stored plurality of communities (utilized to inject and spread the information to other nodes that are immediately connected to the RFS nodes; the determination is information spread to the RFS nodes to learn the existence of a new edge))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, and Train. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. One of ordinary skill would have motivation to combine Banerjee, Segura, and Train to reduce the transmission costs, propagation delays, and security vulnerabilities “To address the challenges discussed above, a novel paradigm is implemented in some embodiments of the present invention for reducing the transmission costs, propagation delays, and security vulnerabilities of existing hop-by-hop route propagation infrastructures by augmenting them with one-to-many broadcast-based route distribution mechanisms.” (Train Column 4 Line 65).
As to claim 8, which incorporates the rejection of claim 1, Banerjee fails to explicitly teach the instructions, when executed by the one or more processors, further cause the one or more processors to:
determine that the predictive model is different from a stored model, and generate a set of binary decision rules using the predictive model, the generation of the set of binary decision rules being performed based on the determination that the predictive model is different from the stored model.
However, Breckenridge teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
determine that the predictive model is different from a stored model, and generate a set of binary decision rules using the predictive model, the generation of the set of binary decision rules being performed based on the determination that the predictive model is different from the stored model (paragraphs [0003]-[0008 The repository of trained predictive models is updated by storing one or more of the generated retrained predictive models. In response to a second condition being satisfied, multiple new trained predictive models are generated using the training data queue and at least some of the training data stored in the training data repository and training functions obtained from the repository of training functions. The new trained predictive models include static trained predictive models and updateable trained predictive models. The repository of trained predictive models is updated by storing at least some of the new trained predictive models; [0005]-[0008], [0080] and [0093], "rules for maintaining and deleting” wherein, Examiner interprets a data retention policy that defines rules for maintaining (.i.e. Yes (1)) and deleting (i.e. No (0)) as binary rules using the predictive 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 Banerjee, Segura, Train, Bereg, and Breckenridge. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, and Breckenridge to easily correct the trained predictive model based on erroneous predictive output “An updateable trained predictive model that gives an erroneous predictive output can be easily and quickly corrected, for example, by providing the correct output as an update training sample upon detecting the error in output.” (Breckenridge Paragraph 0010).
As to claim 9, which incorporates the rejection of claim 1, Banerjee fails to explicitly teach:
wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
build a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model;
evaluate a performance of each of the plurality of candidate models based on the results associated with the new set of previous requests and the stored results associated with the stored set of historical requests, and
select the predictive model to be used as an operational model based on the predictive model having a higher evaluated performance compared to other models of the plurality of candidate models.
Breckenridge, in combination with Banerjee, teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
build a plurality of candidate models based on the smoothed topological graph using the supervised machine learning algorithm, the plurality of candidate models including the predictive model (Breckenridge Paragraph 0037; “Some examples of training functions that can be used to train a static predictive model… other machine learning training functions (e.g., Naive Bayes, k-nearest neighbors, Support Vector Machines, Perceptron).” Breckenridge Paragraph 0088; “FIG. 8 is a flowchart showing an example process 800 for maintaining an updated dynamic repository of trained predictive models. The repository of trained predictive models is dynamic in that new training data can be received and used to update the trained predictive models included in the repository by retraining the updateable trained predictive models and regenerating the static and updateable trained predictive models with updated training data.” Examiner notes that a plurality of candidate models is built/trained based on the smoothed topological graph (training data) using the supervised machine learning algorithm (Naive Bayes, k-nearest neighbors, Support Vector Machines, Perceptron), the plurality of candidate models including the predictive model (static predictive model))
evaluate a performance of each of the plurality of candidate models based on the results associated with the new set of previous requests and the stored results associated with the stored set of historical requests, and (Breckenridge Paragraph 0043; “the effectiveness of each trained predictive model is estimated by performing cross-validation to generate a cross-validation score that is indicative of the accuracy of the trained predictive model, i.e., the number of exact matches of output data predicted by the trained model when compared to the output data included in the test sub-sample.” Examiner notes that a performance (effectiveness) is evaluated for each of the plurality of candidate models (each trained predictive model is estimated) based on the results associated with the new set of previous requests (output data predicted) and the stored results associated with the stored set of historical requests (output data included in the test sub-sample))
select the predictive model to be used as an operational model based on the predictive model having a higher evaluated performance compared to other models of the plurality of candidate models. (Breckenridge Paragraph 0067; “a trained predictive model can be selected to provide to the client computing system 202. For example, the effectiveness scores of the available trained predictive models can be compared, and the most effective trained predictive model selected.” Examiner notes that the predictive model (trained predictive model) is selected having a higher evaluated performance compared to other models (model is selected based on effectiveness))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, and Breckenridge. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, and Breckenridge to easily correct the trained predictive model based on erroneous predictive output “An updateable trained predictive model that gives an erroneous predictive output can be easily and quickly corrected, for example, by providing the correct output as an update training sample upon detecting the error in output.” (Breckenridge Paragraph 0010).
As to claim 11, Claim 11 is a method claim of system claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
As to claim 16, claim 16 has similar limitations as of claim 6, except it is a method claim, therefore it is rejected under the same rationale as claim 6.
As to claim 18, claim 18 has similar limitations as of claim 8, except it is a method claim, therefore it is rejected under the same rationale as claim 8.
As to claim 19, claim 19 has similar limitations as of claim 9, except it is a method claim, therefore it is rejected under the same rationale as claim 9.
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge) in further view of Dixit et al. (US 20180367652 A1, hereinafter referred to as Dixit).
As to claim 2, which incorporates the rejection of claim1, Banerjee fails to explicitly teach wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
generate a set of binary decision rules using the predictive model and the topological graph, the binary decision rules setting a threshold value for a continuous score determined by the predictive model.
However, Dixit teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
generate a set of binary decision rules using the predictive model and the topological graph, the binary decision rules setting a threshold value for a continuous score determined by the predictive model (Dixit Paragraph 0170; ” the network intent models can instead be represented as Reduced Ordered Binary Decision Diagrams (ROBDDs), where each ROBDD is canonical (unique) to the input rules and their priority ordering. Each network intent model is first converted to a flat list of priority ordered rules… These rules can be represented as Boolean functions, where each rule consists of an action (e.g. Permit, Permit-Log, Deny, Deny-Log) and a set of conditions that will trigger that action (e.g. various configurations of packet source, destination, port, header, etc.).” Examiner notes that a set of binary decision rules (rules made from ROBDDs) is generated/converted using the predictive model and the topological graph (model is represented as Reduced Ordered Binary Decision Diagrams (ROBDDs)), the binary decision rules setting a threshold value for a continuous score determined by the predictive model (rule consists …a set of conditions that will trigger that action))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Dixit. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Dixit teaches a method for event generation in response to network intent formal equivalence failures. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Dixit to quickly identify errors within the network to be able to fix them and improve availability uptime “data center operators can quickly see hardware errors that impact particular tenants or other logical entities, across the entire network fabric, and even drill down by other dimensions, such as endpoint groups, to see only those relevant hardware errors. These visualizations speed root cause analysis, improving data center and application availability metrics.” (Dixit Paragraph 0028).
As to claim 3, which incorporates the rejection of claim 2, Banerjee fails to explicitly teach wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: load the predictive model and the set of binary decision rules into the system memory.
However, Dixit, in combination with Banerjee teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: load the predictive model and the set of binary decision rules into the system memory (Dixit Paragraph 0111; “Hi_Model 276 is also a switch-level or switch-specific model for switch i, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actual configuration (e.g., rules) stored or rendered on the hardware or memory” Examiner notes that the predictive model (Hi_model) and set of binary rules (actual configuration (e.g., rules)) is loaded/stored into the system memory (hardware or memory))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Dixit. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Dixit teaches a method for event generation in response to network intent formal equivalence failures. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Dixit to quickly identify errors within the network to be able to fix them and improve availability uptime “data center operators can quickly see hardware errors that impact particular tenants or other logical entities, across the entire network fabric, and even drill down by other dimensions, such as endpoint groups, to see only those relevant hardware errors. These visualizations speed root cause analysis, improving data center and application availability metrics.” (Dixit Paragraph 0028).
As to claim 12, claim 12 has similar limitations as of claim 2, except it is a method claim, therefore it is rejected under the same rationale as claim 2.
As to claim 13, claim 13 has similar limitations as of claim 3, except it is a method claim, therefore it is rejected under the same rationale as claim 3.
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge) in further view of Ardis et al. (US 20170286854 A1, hereinafter referred to as Ardis), and further in view of JORDAN et al. (US 2020/0387832 A1, hereinafter referred to as JORDAN).
As to claim 4, which incorporates the rejection of claim 1, Banerjee fails to explicitly teach: wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
evaluate a performance of the predictive model based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests; and update a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.
Ardis, in combination with Banerjee, teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
evaluate a performance of the predictive model based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests (Ardis Paragraph 0013; “The evaluation can examine the latest data, error metrics, and/or compare the model predictions to real world recorded observations.” Ardis Paragraph 0015; “The impact to model performance can be diagnosed, step 115, by evaluating performance (and any degradation) in the model's predictive accuracy after adaptation is made to the parameters in the model.” Examiner notes that a performance (model performance) of the predictive model is evaluated based on the results associated with the new set of previous requests (model predictions using new set of previous request) and stored results associated with set of historical requests (real world recorded observations)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Ardis. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ardis teaches a method for automatic revision of a predicative damage model. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Ardis to improve the models predictive performance “provide automatic tracking of model performance and identification of alternate model parameters to improve the model's predictive performance utilizing available data.” (Ardis Paragraph 0006).
However, Banerjee fail to explicitly teach:
update a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm.
JORDAN, in combination with Banerjee, teaches update a modeling behavior tree to obtain an optimized modeling behavior tree based on the evaluated performance of the predictive model, modeling behavior tree setting parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm (Fig. 7, elements 708; paragraphs [0037], the automated modeling system 124 can execute a predictive response application 126 , which can utilize a tree - based machine learning model optimized (e.g. optimization algorithm); [0099], objective function optimized; [0082] tree building algorithm(.i.e. community detection algorithm; [0020]-[0021], automated modeling algorithms; [0042], machine learning algorithms; [0122]-[0125], iterate the process 1000 to generate additional decision trees).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ardis, and Jordan. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ardis teaches a method for automatic revision of a predicative damage model. Jordan teaches a method for training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ardis, and Jordan to leverage the performance improvements over existing models “tree-based machine-learning models can provide performance improvements as compared to existing models that quantify a response variable associated with individuals or other entities.” (Jordan Paragraph 0024).
As to claim 5, which incorporates the rejection of claim 4, Banerjee and Vasseur fail to explicitly teach wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
build a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.
However, JORDAN, in combination with Banerjee, teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
build a second predictive model using the optimized modeling behavior tree, wherein the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree (Jordan Fig 3 shows a second predictive model (a new tree-based learning model is generated after modified splitting step) using the optimized modeling behavior tree (adjusting one or more decision trees at step 308), wherein the algorithms are initialized using optimized parameters set by the optimized model behavior tree (modified rules are used as parameters for initialization)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ardis, and Jordan. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ardis teaches a method for automatic revision of a predicative damage model. Jordan teaches a method for training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ardis, and Jordan to leverage the performance improvements over existing models “tree-based machine-learning models can provide performance improvements as compared to existing models that quantify a response variable associated with individuals or other entities.” (Jordan Paragraph 0024).
As to claim 14, claim 14 has similar limitations as of claim 4, except it is a method claim, therefore it is rejected under the same rationale as claim 4.
As to claim 15, claim 15 has similar limitations as of claim 5, except it is a method claim, therefore it is rejected under the same rationale as claim 5.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge) in further view of Michalak et al. (US 9535902 B1, hereinafter referred to as Michalak).
As to claim 7, which incorporates the rejection of claim 1, Banerjee fails to explicitly teach wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
determine that the smoothed topological graph is different from a stored topological graph associated with a stored model, wherein the building of the predictive model is performed based on the determination that the smoothed topological graph is different from the stored topological graph.
However, Michalak, in combination with Banerjee, teaches wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
determine that the smoothed topological graph is different from a stored topological graph associated with a stored model, wherein the building of the predictive model is performed based on the determination that the smoothed topological graph is different from the stored topological graph (Michalak Column 17; “Having made the updates to the Knowledge Graph 508, these changes can be recorded in the change log 518 and back-propagated into new training data 520 for supervised model training processes at 504 and yield more accurate prediction from the output of Local Analytics 506.” Examiner notes that determining that the smoothed topological graph is different from a stored topological graph (recording changes to the knowledge graph shows that the graph is determined to be different from the stored graph) associated with a stored model (model previously trained on not updated graph), wherein the building/training of the predictive model is performed based on the determination (changes can be recorded in the change log 518 and back-propagated into new training data 520 for supervised model training processes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Michalak. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Michalak teaches a method for entity resolution using attributes from structured and unstructured data. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Michalak to understand entities and facts in relationship to determine decision making to not do unnecessary training “The Knowledge Graph, according to some embodiments, can provide for understanding entities and facts in relationships that can enable a user to quickly identify specific opportunities and risks to support crucial decision-making.” (Michalak Column 10 Line 21).
As to claim 17, claim 17 has similar limitations as of claim 7, except it is a method claim, therefore it is rejected under the same rationale as claim 7.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge) in further view of Ferreira et al. (“QK-Means: A Clustering Technique Based on Community Detection and K-Means for Deployment of Cluster Head Nodes”, hereinafter “Ferreria”) in further view of Dorigo et al (“Ant Colony Optimization: A New Meta-Heuristic”, hereinafter “Dorigo”) in further view of Natekin et al (“Gradient Boosting Machine, a tutorial” hereinafter “Natekin”)
As to claim 10, which incorporates the rejection of claim 1, Banerjee does not teach
However, Ferreira does teach The computer system of claim 1, wherein the community detection algorithm is a K-means clustering algorithm, (Ferreira Page 4 Paragraph 2; “This new algorithm takes advantage of community detection approach that is able to find clusters of different shapes and K Means that is a good clustering technique for cartesian points.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Ferreira. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ferreira teaches a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Ferreira to detect better cluster with decreased lost message rate and increased WSN coverage “This new approach takes advantage of both techniques in order to detect better clusters and allow a better deployment of cluster head nodes in large networks… Simulation results show that QK-Means detect communities and sub-communities and, therefore, the lost message rate is decreased and WSN coverage is increased.” (Ferreira Page 1 Paragraph 4).
Banerjee in view of Ferreira does not teach the optimization algorithm is an Ant Colony algorithm, (Dorigo Page 1 Paragraph 3; “The ACO meta-heuristic can be applied to discrete optimization problems characterized as follows.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ferreira, and Dorigo. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ferreira teaches a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique. Dorigo teaches the Ant Colony Optimization meta-heuristic. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ferreira, and Dorigo to leverage ant colony features for obtaining the shortest path often better than those obtained using other general purpose heuristics like evolutionary computation or simulated annealing “Results obtained by the application of ACO algorithms to the TSP are very encouraging (see (Stiitzle & Dorigo, 1999b) for an overview of applications of ACO algorithms to the TSP): they are often better than those obtained using other general purpose heuristics like evolutionary computation or simulated annealing. Also, when adding to ACO algorithms rather unsophisticated local search procedures based on 3- opt (Lin, 1965), the quality of the results obtained (Dorigo & Gambardella, 1997) is close to that obtainable by more sophisticated methods.” (Ferreira Page 6 Paragraph 9).
Banerjee in view of Ferreira in further view of Dorigo does not teach and the supervised machine learning algorithm is a gradient boosting machine.
However, Natekin does teach and the supervised machine learning algorithm is a gradient boosting machine. (Natekin Page 2 Paragraph 3; “In this section we present the basic methodology and learning algorithms of the GBMs”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ferreira, Dorigo, and Natekin. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Ferreira teaches a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique. Dorigo teaches the Ant Colony Optimization meta-heuristic. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, Ferreira, Dorigo, and Natekin to effectively capture complex non-linear function dependencies to apply with considerable success in practical applications “Gradient boosting machines are a powerful method that can effectively capture complex non-linear function dependencies. This family of models has shown considerable success in various practical applications. Moreover the GBMs are extremely flexible and can easily be customized to different practical needs.” (Natekin Page 19 Paragraph 7).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (US 20180196694 A1, hereinafter referred to as Banerjee), in view of Segura et al. (US 20180033077 A1, hereinafter referred to as Segura), in further view of Train et al. (US 8989046 B1, hereinafter referred to as Train), in further view of Bereg et al. (“Edge Routing with Ordered Bundles”, hereinafter referred to as Bereg), in further view of Breckenridge et al. (US 20120191631 A1, hereinafter referred to as Breckenridge) in further view of JORDAN et al. (US 2020/0387832 A1, hereinafter referred to as JORDAN).
As to claim 21, which incorporates the rejection of claim 11, Banerjee fails to explicitly teach wherein the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree.
However, JORDAN, in combination with Banerjee, teaches wherein the supervised machine learning algorithm are initialized using optimized parameters set by the optimized modeling behavior tree ([0020] training a tree- based machine - learning model used by automated modeling algorithms; [0046]- [0049], tree-based machine learning model for computing a predictive response value, such as a credit score; [0070], generate tree - based machine-learning models that comply with one or more constraints imposed by, for example, regulations, business policies, or other criteria used to generate risk evaluations or other predictive modeling outputs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Jordan. Banerjee teaches generating graph-oriented data structures based on cross-channel multi-user transaction and/or interaction data from one or more data sources. Segura teaches method for obtaining at least a behavioral pattern parameter in a client-server architecture. Train teaches an architecture for distributing routing updates via a broadcast channel. Bereg teaches a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. Breckenridge teaches a method for training and retraining predictive models. Jordan teaches a method for training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. One of ordinary skill would have motivation to combine Banerjee, Segura, Train, Bereg, Breckenridge, and Jordan to leverage the performance improvements over existing models “tree-based machine-learning models can provide performance improvements as compared to existing models that quantify a response variable associated with individuals or other entities.” (Jordan Paragraph 0024).
Conclusion
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/D.D.T./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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