Notice of Pre-AIA or AIA Status
This Final communication is in response to Application No. 18/071,169 filed 11/29/2022.The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 12/29/2025 which provides amendments to claims 1-4, 6, 8-11, 13, 15-17 and 19 has been entered. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C § 101 filed 12/29/2025 have been fully considered but they are not persuasive.
Applicant argues the claimed invention does not recite an abstract idea (applicant’s arguments pages 10-11). The examiner respectfully disagrees. The independent claims recite “analyze the usage data” and “determine,…, a plurality of data trends”. Both of these given their broadest reasonable interpretations represent a mental process as they could be performed in the human mind.
Applicant also argues that the claimed invention is a technical improvement (pages 11-12). Applicant describes the claimed invention allows lower-powered devices to use machine learning engines that can leverage the computational power of a system-wide machine learning engine. It is unclear how using generic machine learning models to collect and analyze data relates to this improvement.
Applicant finally argues that the claimed invention amounts to significantly more than the judicial exception (pages 12-13). The additional elements of the independent claims are related to training a machine learning engine and instructing second machine learning engines to analyze data. Both of these are described in generic terms and are mere instructions to apply the judicial exception. Another additional element is monitoring usage data. Storing information is a well understood routine conventional activity.
Thus, the 101 rejection is maintained.
Applicant’s arguments with respect to 35 U.S.C § 103 filed 12/29/2025 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory categories (a process, machine, manufacture or composition of matter.) Claims 1-14 describe a machine and claims 15-20 describe a process.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
analyze the usage data (This is an abstract idea of a "Mental Process." The "analyze" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The analysis could be made manually by an individual.)
determine, based on an output of the first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determining could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
train a first machine learning engine to output a plurality of data trends associated with a network of network devices (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
receiving, via a data acquisition engine, a training dataset comprising historical network logs; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
executing a plurality of testing cycles using the converted training dataset; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
monitor usage data for a plurality of network devices associated with the network; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
using the trained first machine learning engine; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
instruct each of a plurality of second machine learning engine to analyze local data associated with each of the plurality of data trends, wherein each of the plurality of second machine learning engine is hosted on a different network device of the plurality of network devices. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receiving…”, “converting…” and “monitor…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “train a first…”, “executing…”“using the trained first machine learning engine” and “instruct…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept.
When considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
transmit, to each network device, historical usage data associated each data trend determined by the trained first machine learning engine. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive, from a first network device, an output of one of the second machine learning engine; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
and transmit instructions to the first network device. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity, which do not provide an inventive concept.
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
generate an output, wherein the output comprises an individual data trend; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
instruct a first network device to perform an action. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity, which do not provide an inventive concept.
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
usage data comprises at least one of: network traffic data, application usage data, user activity data, system error data, and historical query data. (this limitation merely limits the judicial exception to a particular field of use.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element merely limits the judicial exception to a particular field of use and also cannot provide an inventive concept (MPEP 2106.05(h)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
each data trend identifies at least one of: an application, an application category, a user, a user category, temporal data, and usage data. (this limitation merely limits the judicial exception to a particular field of use.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element merely limits the judicial exception to a particular field of use and also cannot provide an inventive concept (MPEP 2106.05(h)).
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 4, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the action comprises at least one of: updating notification content, updating notification frequency, transmitting a report, updating a user interface, and updating user access. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 7 is ineligible.
With respect to claim 8:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 8. Therefore, claim 8 is ineligible.
With respect to claim 9:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 9. Therefore, claim 9 is ineligible.
With respect to claim 10:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 10. Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
With respect to claim 19:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mitchko (US 20220215465 A1) in view of Nagaraju (US 20180032915 A1).
Regarding claim 1, Mitchko teaches:
A system for processing data using an optimized machine learning architecture, the system comprising: at least one non-transitory storage device; and at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to: ([0007] “In another embodiment, an apparatus may comprise one or more processors and memory storing instructions that, when executed by the one or more processors, cause the apparatus to monitor a bank account associated with a user.” And [0008] “In another embodiment, a non-transitory computer readable medium may store instructions that, when executed by one or more processors, cause a computing device to perform steps including monitoring a bank account associated with a user and training a predictive model (e.g., machine learning model), based on the monitoring, to define and refine a pattern of activity in the bank account through a plurality of iterations.”)
instruct each of a plurality of second machine learning engine to analyze local data associated with each of the plurality of data trends, wherein each of the plurality of second machine learning engine is hosted on a different network device of the plurality of network devices. ([0027] “the machine learning model may monitor a user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to determine whether the user will repay the transaction. Additionally, the machine learning model may monitor the user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to detect fraudulent transactions. By monitoring the user's cash flow and detecting possible fraudulent transactions, the machine learning model described herein may improve banking security by monitoring the user's bank account to detect irregularities in cash flow and/or transactions.”)
Mitchko does not teach:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises:
receiving, via a data acquisition engine, a training dataset comprising historical network logs;
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and
executing a plurality of testing cycles using the converted training dataset;
monitor usage data for a plurality of network devices associated with the network;
analyze the usage data using the trained first machine learning engine;
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices
However, Nagaraju does:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises: ([0134] “The server computer system 14 can apply at least a portion of the global training data 68 as inputs to one or more machine learning algorithms 20 (processes) for training a global model 22.” And [0132] “At least a portion of the raw data 66 from each edge device 12 is sent to the server computer system 14 as training data, and collectively forms global training data 68.”)
receiving, via a data acquisition engine, a training dataset comprising historical network logs; ([0111] “The data summary sent from each of the edge devices 12 then can form global training data to train the global model 22 on the server computer system 14.”)
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and ([0122] “The data pre-processing elements 96 specify one or more operations to be performed on data prior to being inputted into machine learning models”)
executing a plurality of testing cycles using the converted training dataset; ([0026] “The server computer system updates a global machine-learning model (“global model”) by training the global model using the global edge data from all (or at least more than one) of the edge devices.”)
monitor usage data for a plurality of network devices associated with the network; ([0095] “The edge device 12 may include one or more client applications 42 that may be configured to monitor or generate edge data in response to a trigger in the code of the client applications 42 or other triggering events, and to store the edge data on memory 52.”)
analyze the usage data using the trained first machine learning engine; ([0035] “Thus, the server computer system 14 could analyze the edge data generated by the edge devices 12. The edge devices 12 can perform actions based on the analyzed data, when returned over the network 16. In some cases, the server computer system 14 can instruct the edge devices 12 to perform actions based on an analysis performed at one or more of the edge devices 12 and/or at the server computer system 14.”)
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and ([0037] “Within the field of edge data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction. These analytical models allow for producing reliable, repeatable decisions and results, and can uncover hidden insights through learning from historical relationships and trends in data.”).
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices ([0025] “The edge devices can index and store data generated locally based on inputs to the edge devices. The edge devices can each process the generated data with a local model to perform local actions. The local model at each edge device can be updated according to a machine-learning process implemented globally across all edge devices and/or locally at each edge device in a given system.”)
Mitchko and Nagaraju are considered analogous art to the claimed invention because they are in the same field of endeavor being system monitoring. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the financial monitoring system of Mitchko with the global/local models of Nagaraju. One would want to do this to be able to analyze different aspects of the local models (Nagaraju [0005]).
Regarding claim 2, Mitchko in view of Nagaraju teaches claim 1 as outlined above. Nagaraju further teaches:
transmit, to each network device, historical usage data associated with each data trend determined by the trained first machine learning engine ([0115] “a block diagram illustrating an operation of an embodiment of the system to transmit model data to edge devices via a markup language document, including a server computer system connected to edge devices collectively implementing machine learning.”)
Regarding claim 3, Mitchko in view of Nagaraju teaches claim 1 as outlined above. Mitchko further teaches:
the at least one processor is further configured to: receive, from a first network device, an output of one of the second machine learning engine; and transmit instructions to the first network device. ([0045] “If a determination is made that the purchase is eligible for refinancing, then the device may cause one or more refinancing options to be presented (e.g., displayed) to the user in step 470. The device may send (e.g., transmit) the refinancing options to a mobile communication device associated with the user. The refinancing options may be sent (e.g., transmitted) to the user through a mobile application, at the time the user logins into their account via a website, via an electronic communication (e.g., text message, email, etc.), or an equivalent thereof.”)
Regarding claim 4, Mitchko in view of Nagaraju teaches claim 1 as outlined above. Mitchko further teaches:
each second machine learning engine is configured to: generate an output, wherein the output comprises an individual data trend; and instruct a first network device to perform an action. ([0046] “The above-described systems, devices, and methods may provide for a predictive model (e.g., machine learning model) that may determine a pattern of activity associated with a user account. Based on the pattern of activity, the predictive model (e.g., machine learning model) may be better able to forecast whether a user will be able to pay their bills in a timely manner and, when the user cannot, offer the user refinancing options to assist with the user's cashflow.”)
Regarding claim 5, Mitchko in view of Nagaraju teaches claim 1 as outlined above. Mitchko further teaches:
usage data comprises at least one of: network traffic data, application usage data, user activity data, system error data, and historical query data. ([0033] “The one or more inputs may comprise transaction data and/or historical datasets associated with a particular user.”)
Regarding claim 6, Mitchko in view of Nagaraju teaches claim 1 as outlined above. Mitchko further teaches:
each data trend identifies at least one of: an application, an application category, a user, a user category, temporal data, and usage data. ([0027] “In this regard, the machine learning model may be trained on a plurality of users' bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc.”)
Regarding claim 7, Mitchko in view of Nagaraju teaches claim 4 as outlined above. Mitchko further teaches:
the action comprises at least one of: updating notification content, updating notification frequency, transmitting a report, updating a user interface, and updating user access. ([0044] “The model may determine that the user is able to pay their credit card statement, but may not have an available credit limit to make additional purchases. In this caser, the device may determine that the user is eligible for one or more refinancing options to allow the user to make additional purchases. Additionally or alternatively, the device may send (e.g., transmit) an offer to increase the user's credit limit.”)
Regarding claim 8, Mitchko teaches:
A computer program product for processing data using an optimized machine learning architecture, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to: ([0007] “In another embodiment, an apparatus may comprise one or more processors and memory storing instructions that, when executed by the one or more processors, cause the apparatus to monitor a bank account associated with a user.” And [0008] “In another embodiment, a non-transitory computer readable medium may store instructions that, when executed by one or more processors, cause a computing device to perform steps including monitoring a bank account associated with a user and training a predictive model (e.g., machine learning model), based on the monitoring, to define and refine a pattern of activity in the bank account through a plurality of iterations.”)
instruct each of a plurality of second machine learning engine to analyze local data associated with each of the plurality of data trends, wherein each of the plurality of second machine learning engine is hosted on a different network device of the plurality of network devices. ([0027] “the machine learning model may monitor a user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to determine whether the user will repay the transaction. Additionally, the machine learning model may monitor the user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to detect fraudulent transactions. By monitoring the user's cash flow and detecting possible fraudulent transactions, the machine learning model described herein may improve banking security by monitoring the user's bank account to detect irregularities in cash flow and/or transactions.”)
Mitchko does not teach:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises:
receiving, via a data acquisition engine, a training dataset comprising historical network logs;
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and
executing a plurality of testing cycles using the converted training dataset;
monitor usage data for a plurality of network devices associated with the network;
analyze the usage data using the trained first machine learning engine;
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices
However, Nagaraju does:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises: ([0134] “The server computer system 14 can apply at least a portion of the global training data 68 as inputs to one or more machine learning algorithms 20 (processes) for training a global model 22.” And [0132] “At least a portion of the raw data 66 from each edge device 12 is sent to the server computer system 14 as training data, and collectively forms global training data 68.”)
receiving, via a data acquisition engine, a training dataset comprising historical network logs; ([0111] “The data summary sent from each of the edge devices 12 then can form global training data to train the global model 22 on the server computer system 14.”)
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and ([0122] “The data pre-processing elements 96 specify one or more operations to be performed on data prior to being inputted into machine learning models”)
executing a plurality of testing cycles using the converted training dataset; ([0026] “The server computer system updates a global machine-learning model (“global model”) by training the global model using the global edge data from all (or at least more than one) of the edge devices.”)
monitor usage data for a plurality of network devices associated with the network; ([0095] “The edge device 12 may include one or more client applications 42 that may be configured to monitor or generate edge data in response to a trigger in the code of the client applications 42 or other triggering events, and to store the edge data on memory 52.”)
analyze the usage data using the trained first machine learning engine; ([0035] “Thus, the server computer system 14 could analyze the edge data generated by the edge devices 12. The edge devices 12 can perform actions based on the analyzed data, when returned over the network 16. In some cases, the server computer system 14 can instruct the edge devices 12 to perform actions based on an analysis performed at one or more of the edge devices 12 and/or at the server computer system 14.”)
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and ([0037] “Within the field of edge data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction. These analytical models allow for producing reliable, repeatable decisions and results, and can uncover hidden insights through learning from historical relationships and trends in data.”).
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices ([0025] “The edge devices can index and store data generated locally based on inputs to the edge devices. The edge devices can each process the generated data with a local model to perform local actions. The local model at each edge device can be updated according to a machine-learning process implemented globally across all edge devices and/or locally at each edge device in a given system.”)
Mitchko and Nagaraju are considered analogous art to the claimed invention because they are in the same field of endeavor being system monitoring. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the financial monitoring system of Mitchko with the global/local models of Nagaraju. One would want to do this to be able to analyze different aspects of the local models (Nagaraju [0005]).
Regarding claim 9, Mitchko in view of Nagaraju teaches claim 8 as outlined above. Nagaraju further teaches:
transmit, to each network device, historical usage data associated with each data trend determined by the trained first machine learning engine ([0115] “a block diagram illustrating an operation of an embodiment of the system to transmit model data to edge devices via a markup language document, including a server computer system connected to edge devices collectively implementing machine learning.”)
Regarding claim 10, Mitchko in view of Nagaraju teaches claim 8 as outlined above. Mitchko further teaches:
receive, from a first network device, an output of one of the second machine learning engine; and transmit instructions to the first network device. ([0045] “If a determination is made that the purchase is eligible for refinancing, then the device may cause one or more refinancing options to be presented (e.g., displayed) to the user in step 470. The device may send (e.g., transmit) the refinancing options to a mobile communication device associated with the user. The refinancing options may be sent (e.g., transmitted) to the user through a mobile application, at the time the user logins into their account via a website, via an electronic communication (e.g., text message, email, etc.), or an equivalent thereof.”)
Regarding claim 11, Mitchko in view of Nagaraju teaches claim 8 as outlined above. Mitchko further teaches:
each second machine learning engine is configured to: generate an output, wherein the output comprises an individual data trend; and instruct a first network device to perform an action. ([0046] “The above-described systems, devices, and methods may provide for a predictive model (e.g., machine learning model) that may determine a pattern of activity associated with a user account. Based on the pattern of activity, the predictive model (e.g., machine learning model) may be better able to forecast whether a user will be able to pay their bills in a timely manner and, when the user cannot, offer the user refinancing options to assist with the user's cashflow.”)
Regarding claim 12, Mitchko in view of Nagaraju teaches claim 8 as outlined above. Mitchko further teaches:
usage data comprises at least one of: network traffic data, application usage data, user activity data, system error data, and historical query data. ([0033] “The one or more inputs may comprise transaction data and/or historical datasets associated with a particular user.”)
Regarding claim 13, Mitchko in view of Nagaraju teaches claim 8 as outlined above. Mitchko further teaches:
each data trend identifies at least one of: an application, an application category, a user, a user category, temporal data, and usage data. ([0027] “In this regard, the machine learning model may be trained on a plurality of users' bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc.”)
Regarding claim 14, Mitchko in view of Nagaraju teaches claim 11 as outlined above. Mitchko further teaches:
the action comprises at least one of: updating notification content, updating notification frequency, transmitting a report, updating a user interface, and updating user access. ([0044] “The model may determine that the user is able to pay their credit card statement, but may not have an available credit limit to make additional purchases. In this caser, the device may determine that the user is eligible for one or more refinancing options to allow the user to make additional purchases. Additionally or alternatively, the device may send (e.g., transmit) an offer to increase the user's credit limit.”)
Regarding claim 15, Mitchko teaches:
A method for processing data using an optimized machine learning architecture, the method comprising: [0004] “Given the foregoing, what is needed is an automated system and method to monitor a user's bank account balance over time to train a predictive model (e.g., machine learning model) to define a pattern of activity in the user's bank account such that when a user uses a credit card to make a purchase the predictive model (e.g., machine learning model) may predict whether the user will be able to pay for the purchase in full when payment is due.”)
instruct each of a plurality of second machine learning engine to analyze local data associated with each of the plurality of data trends, wherein each of the plurality of second machine learning engine is hosted on a different network device of the plurality of network devices. ([0027] “the machine learning model may monitor a user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to determine whether the user will repay the transaction. Additionally, the machine learning model may monitor the user's bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc. to detect fraudulent transactions. By monitoring the user's cash flow and detecting possible fraudulent transactions, the machine learning model described herein may improve banking security by monitoring the user's bank account to detect irregularities in cash flow and/or transactions.”)
Mitchko does not teach:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises:
receiving, via a data acquisition engine, a training dataset comprising historical network logs;
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and
executing a plurality of testing cycles using the converted training dataset;
monitor usage data for a plurality of network devices associated with the network;
analyze the usage data using the trained first machine learning engine;
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices
However, Nagaraju does:
train a first machine learning engine to output a plurality of data trends associated with a network of network devices, wherein training the first machine learning engine comprises: ([0134] “The server computer system 14 can apply at least a portion of the global training data 68 as inputs to one or more machine learning algorithms 20 (processes) for training a global model 22.” And [0132] “At least a portion of the raw data 66 from each edge device 12 is sent to the server computer system 14 as training data, and collectively forms global training data 68.”)
receiving, via a data acquisition engine, a training dataset comprising historical network logs; ([0111] “The data summary sent from each of the edge devices 12 then can form global training data to train the global model 22 on the server computer system 14.”)
converting, via a data pre-processing engine, the training dataset from a non-standardized format to a standardized format; and ([0122] “The data pre-processing elements 96 specify one or more operations to be performed on data prior to being inputted into machine learning models”)
executing a plurality of testing cycles using the converted training dataset; ([0026] “The server computer system updates a global machine-learning model (“global model”) by training the global model using the global edge data from all (or at least more than one) of the edge devices.”)
monitor usage data for a plurality of network devices associated with the network; ([0095] “The edge device 12 may include one or more client applications 42 that may be configured to monitor or generate edge data in response to a trigger in the code of the client applications 42 or other triggering events, and to store the edge data on memory 52.”)
analyze the usage data using the trained first machine learning engine; ([0035] “Thus, the server computer system 14 could analyze the edge data generated by the edge devices 12. The edge devices 12 can perform actions based on the analyzed data, when returned over the network 16. In some cases, the server computer system 14 can instruct the edge devices 12 to perform actions based on an analysis performed at one or more of the edge devices 12 and/or at the server computer system 14.”)
determine, based on an output of the trained first machine learning engine, a plurality of data trends, wherein each data trend is associated with a network device of the plurality of network devices; and ([0037] “Within the field of edge data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction. These analytical models allow for producing reliable, repeatable decisions and results, and can uncover hidden insights through learning from historical relationships and trends in data.”).
plurality of second machine learning engine is hosted on a different network device of the plurality of network devices ([0025] “The edge devices can index and store data generated locally based on inputs to the edge devices. The edge devices can each process the generated data with a local model to perform local actions. The local model at each edge device can be updated according to a machine-learning process implemented globally across all edge devices and/or locally at each edge device in a given system.”)
Mitchko and Nagaraju are considered analogous art to the claimed invention because they are in the same field of endeavor being system monitoring. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the financial monitoring system of Mitchko with the global/local models of Nagaraju. One would want to do this to be able to analyze different aspects of the local models (Nagaraju [0005]).
Regarding claim 16, Mitchko in view of Nagaraju teaches claim 15 as outlined above. Mitchko further teaches:
receive, from a first network device, an output of one of the second machine learning engine; and transmit instructions to the first network device. ([0045] “If a determination is made that the purchase is eligible for refinancing, then the device may cause one or more refinancing options to be presented (e.g., displayed) to the user in step 470. The device may send (e.g., transmit) the refinancing options to a mobile communication device associated with the user. The refinancing options may be sent (e.g., transmitted) to the user through a mobile application, at the time the user logins into their account via a website, via an electronic communication (e.g., text message, email, etc.), or an equivalent thereof.”)
Regarding claim 17, Mitchko in view of Nagaraju teaches claim 15 as outlined above. Mitchko further teaches:
each second machine learning engine is configured to: generate an output, wherein the output comprises an individual data trend; and instruct a first network device to perform an action. ([0046] “The above-described systems, devices, and methods may provide for a predictive model (e.g., machine learning model) that may determine a pattern of activity associated with a user account. Based on the pattern of activity, the predictive model (e.g., machine learning model) may be better able to forecast whether a user will be able to pay their bills in a timely manner and, when the user cannot, offer the user refinancing options to assist with the user's cashflow.”)
Regarding claim 18, Mitchko in view of Nagaraju teaches claim 15 as outlined above. Mitchko further teaches:
usage data comprises at least one of: network traffic data, application usage data, user activity data, system error data, and historical query data. ([0033] “The one or more inputs may comprise transaction data and/or historical datasets associated with a particular user.”)
Regarding claim 19, Mitchko in view of Nagaraju teaches claim 15 as outlined above. Mitchko further teaches:
each data trend identifies at least one of: an application, an application category, a user, a user category, temporal data, and usage data. ([0027] “In this regard, the machine learning model may be trained on a plurality of users' bank account activity, history of credit purchases to define a pattern of credit purchases, a pattern of the user's bank account balances, etc.”)
Regarding claim 20, Mitchko in view of Nagaraju teaches claim 17 as outlined above. Mitchko further teaches:
the action comprises at least one of: updating notification content, updating notification frequency, transmitting a report, updating a user interface, and updating user access. ([0044] “The model may determine that the user is able to pay their credit card statement, but may not have an available credit limit to make additional purchases. In this caser, the device may determine that the user is eligible for one or more refinancing options to allow the user to make additional purchases. Additionally or alternatively, the device may send (e.g., transmit) an offer to increase the user's credit limit.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121