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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/18/2025 and 01/12/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pg. 1, filed 10/28/2025, with respect to claims 1-7 have been fully considered and are persuasive. The 112 rejection of 07/31/2025 has been withdrawn.
Applicant's arguments filed claims 1-20 have been fully considered but they are not persuasive. However, previously used prior art Lee and Modi have been remapped to teach the amended features.
Specifically:
receive interaction data for a group of users, wherein the interaction data comprises unclean interaction data and unvectorized interaction data that is slow to process using a classification machine learning model; (Lee, paragraph 0023, “In addressing these issues and providing such mechanisms, the illustrative embodiments, rather than using a classifier to identify an adversarial input, directly hinders proper gradient computation by adding noise [unclean interaction data and unvectorized interaction data], ]in the model itself, e.g., noise in the internal feature representations of the neural network itself [[slow to process using a classification machine learning model;] ], such that gradient computations are diverted.” And paragraph 0101, “The cognitive system 400 is configured to implement a request processing pipeline 408 that receive inputs from various sources [receive interaction data for a group of users]. The requests may be posed in the form of a natural language question, natural language request for information, natural language request for the performance of a cognitive operation, or the like. Alternatively, the "request" may simply be the input of data that is intended to be operated on by the cognitive system e.g., images, text, audio input, or the like, which is to be classified by the hardened model of the illustrative embodiments and then operated on by cognitive processes to generate a result of a cognitive operation.”) Lee explains that the model has noisy data which is interpreted as unclean and unvectorized data. Which this data is input received from users, making it slow to process.
wherein transforming the interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data so that an accessing classification machine learning model is able to process the transformed data more quickly than the accessing classification machine learning model is able to process the received interaction data; (Lee, paragraph 0004, “Neural network based deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Neural network based deep learning is based on the learning of multiple levels of features or representations of the data with higher level features being derived from lower level features to form a hierarchical representation.” And paragraph 0030, “Thus, after training the new protected model using these three sets of training data, an additional layer of nodes, referred to as the merge layer having merge nodes, is added to the new protected model to merge the first half and second half of the output nodes by joining the adversarially trained class, i.e., the classification output vector generated by the second set of output nodes, to its original class, i.e. the classification output vector generated by the first set of output nodes, so that the regular data samples in the first set of training data, and the adversarially generated data samples in the third set of training data, are correctly classified [interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data].”) Lee explains the process of transforming the data making it clean and faster to process.
process the extracted content from the transformed interaction data for the group of users using a classification algorithm configured to label the interaction data; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”) Lee explains taking this transformed data and labeling it which is being interpreted as classifying into groups.
receive a result from the classification algorithm, wherein the result comprises labeled interaction data; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”) Lee labels the data received.
correlate the labeled interaction data with the user information to create a correlated dataset; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”) After leabeling lee has correlated the data with patient information.
receive a second result, wherein the second result is one or more segments for each user of the group of users; (Lee, paragraph 0052, “labeled data set 240 is a set of output data generated by the trained neural network model 230 where the unlabeled input data is augmented with additional tags or labels of meaningful information for the particular cognitive operation for which the data is to be used. For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”) Lee is using the output data to have additional labels/results given.
process each user's segment by a second machine learning algorithm to make a prediction of future behavior, (Lee, paragraph 0055, “The labeled data 240 is then input to the cognitive system 250 for performance of cognitive operations on the labeled data 240. The particular cognitive operation performed by the cognitive system 250 depends on the cognitive system and may be any of a plethora of different types of cognitive operations. Examples of cognitive operations include various types of decision making operations or decision support operations, such as security system based operations for controlling access to facilities, data, or any other secure asset. Such security system cognitive operations may employ the labeled data 240 to perform facial recognition, voice print recognition, biometrics based decision making, or the like.” And paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior, examiner would like to point out that this predicting is happening with natural language which is being interpreted as the user segments.]”). Lee uses prediction of human behavior to process users behavior.
receive, for each user, a predicted future behavior from the second machine learning algorithm; (Lee, paragraph 0019, “Often times, such neural networks, and other types of machine learning or cognitive models, are utilized in or with cognitive systems to perform a classification operation upon which the cognitive system operates to perform a cognitive operation, e.g., classifying an input into one of a plurality of predetermined classifications (classes) which is then used to perform a more complex analysis or reasoning operation using cognitive system mechanisms.” And paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior, examiner would like to point out that this predicting is happening with natural language which is being interpreted as the user segments.]”). Lee uses predetermined classes to predict behavior.
extract content from the transformed interaction data; (Modi, paragraph 0175, “normalisation process is then used to transform the log files 10 (which may be in various different formats) into generic normalised metadata or log files 20. The normalization process operates by modelling any human interaction with the client system 100 by breaking it down into discrete events. These events are identified from the content of the log files 10. A schema for each data source used in the network 1000 is defined so that any log file 10 from a known data source in the network 1000 has an identifiable structure, and 'events' and other associated parameters (which may, for example, be metadata related to the events) may be easily identified and be transposed into the schema for the normalized log files 20.”) Modi teaches extracting normalized data from files.
Therefore the 35 USC 103 rejection is maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al (US Published Patent Application No. 20190130110, "Lee") [May 2, 2019], in view of Modi et al (US Published Patent Application No. 20180246797, "Modi") [Aug. 30, 2018].
In regard to claim 1, Lee teaches A system for using serially applied machine learning models to process labeled data to trigger concurrent transmission, thereby maximizing system processing efficiency, the system comprising: (Lee, paragraph 0006, “In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and a memory, the memory comprising instructions which are executed by the processor to specifically configure the processor to implement a hardened neural network. The method comprises configuring the hardened neural network executing in the data processing system to introduce noise in internal feature representations of the hardened neural network.”)
at least one processor; (Lee, paragraph 0006, In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and a memory, the memory comprising instructions which are executed by the processor to specifically configure the processor to implement a hardened neural network.”)
a communication interface communicatively coupled to the at least one processor; and (Lee, paragraph 0100, “The cognitive system 400 is implemented on one or more computing devices 304A-D ( comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network 402.”)
a memory device storing executable code that, when executed, causes the at least one processor to: (Lee, paragraph 0006, “In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and a memory, the memory comprising instructions which are executed by the processor to specifically configure the processor to implement a hardened neural network.”)
receive interaction data for a group of users, wherein the interaction data comprises unclean interaction data and unvectorized interaction data that is slow to process using a classification machine learning model; (Lee, paragraph 0023, “In addressing these issues and providing such mechanisms, the illustrative embodiments, rather than using a classifier to identify an adversarial input, directly hinders proper gradient computation by adding noise [unclean interaction data and unvectorized interaction data], ]in the model itself, e.g., noise in the internal feature representations of the neural network itself [[slow to process using a classification machine learning model;] ], such that gradient computations are diverted.” And paragraph 0101, “The cognitive system 400 is configured to implement a request processing pipeline 408 that receive inputs from various sources [receive interaction data for a group of users]. The requests may be posed in the form of a natural language question, natural language request for information, natural language request for the performance of a cognitive operation, or the like. Alternatively, the "request" may simply be the input of data that is intended to be operated on by the cognitive system e.g., images, text, audio input, or the like, which is to be classified by the hardened model of the illustrative embodiments and then operated on by cognitive processes to generate a result of a cognitive operation.”)
wherein transforming the interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data so that an accessing classification machine learning model is able to process the transformed data more quickly than the accessing classification machine learning model is able to process the received interaction data; (Lee, paragraph 0004, “Neural network based deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Neural network based deep learning is based on the learning of multiple levels of features or representations of the data with higher level features being derived from lower level features to form a hierarchical representation.” And paragraph 0030, “Thus, after training the new protected model using these three sets of training data, an additional layer of nodes, referred to as the merge layer having merge nodes, is added to the new protected model to merge the first half and second half of the output nodes by joining the adversarially trained class, i.e., the classification output vector generated by the second set of output nodes, to its original class, i.e. the classification output vector generated by the first set of output nodes, so that the regular data samples in the first set of training data, and the adversarially generated data samples in the third set of training data, are correctly classified [interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data].”)
process the extracted content from the transformed interaction data for the group of users using a classification algorithm configured to label the interaction data; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc b[the group of users using ]. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
receive a result from the classification algorithm, wherein the result comprises labeled interaction data; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
correlate the labeled interaction data with the user information to create a correlated dataset; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
process the correlated dataset by a first machine learning algorithm configured to segment users of the group of users into a plurality of segments; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc [the group of users using ]. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
receive a second result, wherein the second result is one or more segments for each user of the group of users; (Lee, paragraph 0052, “labeled data set 240 is a set of output data generated by the trained neural network model 230 where the unlabeled input data is augmented with additional tags or labels of meaningful information for the particular cognitive operation for which the data is to be used. For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc [the group of users using] In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
process each user's segment by a second machine learning algorithm to make a prediction of future behavior, (Lee, paragraph 0055, “The labeled data 240 is then input to the cognitive system 250 for performance of cognitive operations on the labeled data 240. The particular cognitive operation performed by the cognitive system 250 depends on the cognitive system and may be any of a plethora of different types of cognitive operations. Examples of cognitive operations include various types of decision making operations or decision support operations, such as security system based operations for controlling access to facilities, data, or any other secure asset. Such security system cognitive operations may employ the labeled data 240 to perform facial recognition, voice print recognition, biometrics based decision making, or the like.” And paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior, examiner would like to point out that this predicting is happening with natural language which is being interpreted as the user segments.]”).
receive, for each user, a predicted future behavior from the second machine learning algorithm; (Lee, paragraph 0019, “Often times, such neural networks, and other types of machine learning or cognitive models, are utilized in or with cognitive systems to perform a classification operation upon which the cognitive system operates to perform a cognitive operation, e.g., classifying an input into one of a plurality of predetermined classifications (classes) which is then used to perform a more complex analysis or reasoning operation using cognitive system mechanisms.” And paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior examiner would like to point out that this predicting is happening with natural language which is being interpreted as the user segments.]”)
Lee does not explicitly teach extract content from the transformed interaction data;
receive user information for the group of users;
based on the predicted future behavior, trigger a real-time communication to the user.
extract content from the transformed interaction data; (Modi, paragraph 0175, “normalisation process is then used to transform the log files 10 (which may be in various different formats) into generic normalised metadata or log files 20. The normalization process operates by modelling any human interaction with the client system 100 by breaking it down into discrete events. These events are identified from the content of the log files 10. A schema for each data source used in the network 1000 is defined so that any log file 10 from a known data source in the network 1000 has an identifiable structure, and 'events' and other associated parameters (which may, for example, be metadata related to the events) may be easily identified and be transposed into the schema for the normalized log files 20.”)
receive user information for the group of users; (Modi, paragraph 0005, “receiving metadata from one or more devices within the one or more monitored computer systems; identifying from the metadata events corresponding to a plurality of user interactions [user information for the group of users] with the monitored computer systems;”)
based on the predicted future behavior, trigger a real-time communication to the user. (Modi, paragraph 0019, “By combining historical and live data, meaningful insights can be generated close to real time.” And paragraph 0199, “This may be used to identify abnormal and/or malicious human interactions with the client system 100. The second layer of statistical learning may be provided by clustering users based on the data produced by the individual modules. Changes in the clusters may be detected, and/or associations can be made between clusters. The change in the data produced by the individual modules may be modelled over time. The data produced by the individual modules may also be dynamically weighted, and/or the data produced by the individual modules may be predicted [predicted future behavior].”) and paragraph 0223, “Where a particular event (or combination of events) is detected as it is occurring, the monitoring system 200 may be able to issue an alert via email, SMS, phone call or virtual assistant or another communication means. The system 200, if appropriately configured, may also be able to automatically implement one or more as actions in response to a particular event or combination of events.”)
Lee and Modi are related to the same field of endeavor (i.e. evaluating user behavior). In view of the teachings of Modi, it would have been obvious for a person with ordinary skill in the art to apply the teachings of Modi to Lee before the effective filing date of the claimed invention in order to have better accuracy. (Modi, paragraph 0032, “Updating the model in this way can enable the inclusion of further information into the probabilistic model for better accuracy.”)
In regard to claim 2 and analogous claim 15, Lee and Modi teach the system of claim 1.
Modi further teaches wherein the executable code further causes the processor to trigger a concurrent update to a user dashboard of the user based on the user's predicted future behavior. (Modi, paragraph 0154, “In an example, the monitoring system 200 also monitors changes in the behaviour of a single user or a group of users away from determined normal behaviour. Such changes in behaviour may come about in response to, for example, a policy change or training. New users may change their behaviour as they learn and progress. The 'normal behaviour' of users (such as new users) may be updated accordingly as their typical interactions with the client system 100 change over time. Furthermore the behaviour of a particular user or a user group can be evaluated to identify behaviour that might be particularly beneficial. Once beneficial behaviour is identified it can be encouraged appropriately.” And paragraph 0190, “…the graph database 224 and, optionally, the index database 222, and may produce outputs 30 which may be presented to an administrator via reports 120 or on a 'dashboard' web portal or application 110.”)
In regard to claim 3 and analogous claims 9 and 16, Lee and Modi teach the system of claim 2.
Modi further teaches wherein the user dashboard comprises educational material, tailored offers, and virtual assistants. (Modi, paragraph 0222, “Optionally, the monitoring system 200 may interface with an online dashboard 110, which may be available through a web portal or a mobile application, which may show reports (as previously described) and allow live monitoring of the events detected in the log files 10. This dashboard 110 may comprise a map/location-based view showing all activity or on a map, graphs showing relationships between objects, tables and data around identified graphs, details about events and timelines of related events, for example relating to the same user(s) or object(s). The dashboard 110 may provide the ability for an administrator to explore objects, actions and users connected to events in a global context.”)
In regard to claim 4 and analogous claims 10 and 17, Lee and Modi teach the system of claim 1.
Lee further teaches wherein the first algorithm comprises a classification method. (Lee, paragraph 0004, “The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).”)
In regard to claim 5 and analogous claims 11 and 18, Lee and Modi teach the system of claim 4.
Lee further teaches wherein the classification method is a supervised learning method. (Lee, paragraph 0004, “The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).”)
In regard to claim 6 and analogous claims 12 and 19, Lee and Modi teach the system of claim 4.
Lee further teaches wherein the classification method is an unsupervised learning method. (Lee, paragraph 0004, “The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised).”)
In regard to claim 7 and analogous claims 13 and 20, Lee and Modi teach the system of claim 1.
Modi further teaches wherein the second algorithm comprises a neural network configured to make predictions about a user's behavior. (Modi, paragraph 0198, “Although the algorithms may be unsupervised, they may be used in combination with supervised models such as neural networks. The supervised neural net [a neural network] may be trained to recognise patterns of events (based on examples, or feedback by the operator) which may indicate that the user is unexpectedly changing their behaviour or marks a long term in their normal behaviour (the saved data relating to a user's normal behaviour may then be updated accordingly) [configured to make predictions about a user's behavior.]. The algorithms as a whole may therefore be referred to as 'supervised-unsupervised'.”)
In regard to claim 8, Lee teaches A system for monitoring user behavior comprising: at least one processor, (Lee, paragraph 0006, In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and a memory, the memory comprising instructions which are executed by the processor to specifically configure the processor to implement a hardened neural network.”)
a communication interface communicatively coupled to the at least one processor, (Lee, paragraph 0100, “The cognitive system 400 is implemented on one or more computing devices 304A-D ( comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network 402.”)
a memory device storing executable code that, when executed, causes the at least one processor to: (Lee, paragraph 0006, “In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and a memory, the memory comprising instructions which are executed by the processor to specifically configure the processor to implement a hardened neural network.”)
receive labeled interaction data for a group of users, wherein the labeled interaction data is labeled using a classification method; (Lee, paragraph 0055, “The labeled data 240 is then input to the cognitive system 250 for performance of cognitive operations on the labeled data 240. The particular cognitive operation performed by the cognitive system 250 depends on the cognitive system and may be any of a plethora of different types of cognitive operations.”)
correlate the labeled interaction data with the user information to create a correlated dataset; apply a first algorithm to the correlated dataset, wherein the algorithm is configured to segment users of the group of users into a plurality of segments; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”).
receive a result from the first algorithm, wherein the result is one or more segments for each user of the group of users; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”).
receive a second result, wherein the second result is a predicted future behavior for the users; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".” And paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior examiner would like to point out that this predicting is happening with natural language which is being interpreted as the user segments.]”)
Lee does not specifically teach receive user information for the group of users;
based on each user's segment, apply a second algorithm, wherein the second algorithm is configured to make a prediction about the user's future behavior;
based on the user's predicted future behavior, trigger a concurrent communication to the user; and based on the user's predicted future behavior, trigger a concurrent update to the user's user dashboard.
However, Modi teaches receive user information for the group of users; (Modi, paragraph 0005, “receiving metadata from one or more devices within the one or more monitored computer systems; identifying from the metadata events corresponding to a plurality of user interactions with the monitored computer systems;”)
based on each user's segment, apply a second algorithm, wherein the second algorithm is configured to make a prediction about the user's future behavior; (Modi, paragraph 0199, “This may be used to identify abnormal and/or malicious human interactions with the client system 100. The second layer of statistical learning may be provided by clustering users based on the data produced by the individual modules. Changes in the clusters may be detected, and/or associations can be made between clusters. The change in the data produced by the individual modules may be modelled over time. The data produced by the individual modules may also be dynamically weighted, and/or the data produced by the individual modules may be predicted.” and paragraph 0223, “Where a particular event (or combination of events) is detected as it is occurring, the monitoring system 200 may be able to issue an alert via email, SMS, phone call or virtual assistant or another communication means. The system 200, if appropriately configured, may also be able to automatically implement one or more as actions in response to a particular event or combination of events.”))
based on the user's predicted future behavior, trigger a concurrent communication to the user; and (Modi, paragraph 0019, “By combining historical and live data, meaningful insights can be generated close to real time.” And paragraph 0199, “This may be used to identify abnormal and/or malicious human interactions with the client system 100. The second layer of statistical learning may be provided by clustering users based on the data produced by the individual modules. Changes in the clusters may be detected, and/or associations can be made between clusters. The change in the data produced by the individual modules may be modelled over time. The data produced by the individual modules may also be dynamically weighted, and/or the data produced by the individual modules may be predicted.” and paragraph 0223, “Where a particular event (or combination of events) is detected as it is occurring, the monitoring system 200 may be able to issue an alert via email, SMS, phone call or virtual assistant or another communication means. The system 200, if appropriately configured, may also be able to automatically implement one or more as actions in response to a particular event or combination of events.”)
based on the user's predicted future behavior, trigger a concurrent update to the user's user dashboard. (Modi, paragraph 0222, “Optionally, the monitoring system 200 may interface with an online dashboard 110, which may be available through a web portal or a mobile application, which may show reports (as previously described) and allow live monitoring of the events detected in the log files 10. This dashboard 110 may comprise a map/location-based view showing all activity or on a map, graphs showing relationships between objects, tables and data around identified graphs, details about events and timelines of related events, for example relating to the same user(s) or object(s).” and paragraph 0199, “This may be used to identify abnormal and/or malicious human interactions with the client system 100. The second layer of statistical learning may be provided by clustering users based on the data produced by the individual modules. Changes in the clusters may be detected, and/or associations can be made between clusters. The change in the data produced by the individual modules may be modelled over time. The data produced by the individual modules may also be dynamically weighted, and/or the data produced by the individual modules may be predicted.”)
Lee and Modi are combinable for the same rationale as set forth above with respect to claim 1.
In regard to claim 14, Lee teaches A method for employing artificial intelligence to initiate a concurrent reaction according to predicted behavior, the method comprising: (Lee, paragraph 0005, “According to a first aspect, there is provided a method for monitoring user interactions within one or more monitored computer systems, comprising the steps of:” and paragraph 0091, “Predict and sense with situational awareness that mimic human cognition based on experiences [prediction of future behavior]”)
receiving interaction data for a group of users, wherein the interaction data comprises unclean interaction data and unvectorized interaction data that is slow to process using a classification machine learning model; (Lee, paragraph 0023, “In addressing these issues and providing such mechanisms, the illustrative embodiments, rather than using a classifier to identify an adversarial input, directly hinders proper gradient computation by adding noise [unclean interaction data and unvectorized interaction data], ]in the model itself, e.g., noise in the internal feature representations of the neural network itself [[slow to process using a classification machine learning model;] ], such that gradient computations are diverted.” And paragraph 0101, “The cognitive system 400 is configured to implement a request processing pipeline 408 that receive inputs from various sources [receive interaction data for a group of users]. The requests may be posed in the form of a natural language question, natural language request for information, natural language request for the performance of a cognitive operation, or the like. Alternatively, the "request" may simply be the input of data that is intended to be operated on by the cognitive system e.g., images, text, audio input, or the like, which is to be classified by the hardened model of the illustrative embodiments and then operated on by cognitive processes to generate a result of a cognitive operation.”)
wherein transforming the interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data so that an accessing classification machine learning model is able to process the transformed data more quickly than the accessing classification machine learning model is able to process the received interaction data; (Lee, paragraph 0004, “Neural network based deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). Neural network based deep learning is based on the learning of multiple levels of features or representations of the data with higher level features being derived from lower level features to form a hierarchical representation.” And paragraph 0030, “Thus, after training the new protected model using these three sets of training data, an additional layer of nodes, referred to as the merge layer having merge nodes, is added to the new protected model to merge the first half and second half of the output nodes by joining the adversarially trained class, i.e., the classification output vector generated by the second set of output nodes, to its original class, i.e. the classification output vector generated by the first set of output nodes, so that the regular data samples in the first set of training data, and the adversarially generated data samples in the third set of training data, are correctly classified [interaction data comprises cleaning and vectorizing the data thereby improving the accessibility of the data].”)
processing the extracted content from the transformed interaction data for a the group of users using a classification algorithm configured to label the interaction data; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”)
receiving labeled interaction data for a group of users, (Lee, paragraph 0055, “The labeled data 240 is then input to the cognitive system 250 for performance of cognitive operations on the labeled data 240. The particular cognitive operation performed by the cognitive system 250 depends on the cognitive system and may be any of a plethora of different types of cognitive operations.”)
correlating the labeled interaction data with the user information to create a correlated dataset; applying a first algorithm to the correlated data, wherein the algorithm is configured to segment users into a plurality of segments; (Lee, paragraph 0052, “For example, in a patient treatment recommendation cognitive system, the labeled data may comprise labels, tags, or annotations that specify various medical concepts with which the data is associated, e.g., a disease, a treatment, a patient's age, a patient's gender, etc. In the depicted example, the operation of the neural network model 230 is to classify a portion of an input image specified in a set of input data 220 into one of 10 categories representing numerical values that the portion of the input image represents, e.g., classes "O" to "9".”).
Lee does not explicitly teach transforming the interaction data; wherein transforming the interaction data comprises cleaning and vectorising the data thereby improving the accessibility of the data so that an accessing classification machine learning model is able to process the transformed data more quickly than the accessing classification machine learning model is able to process the received interaction data;
extracting content from the transformed interaction data;
receiving one or more segments for each user of the group of users;
based on a user's one or more segments, processing the one or more segment using a second algorithm configured to make a prediction about the user's future behavior; and
receiving, for each user, a predicted future behavior;
based on the user's predicted future behavior, triggering a concurrent communication to the user.
However, Modi teaches extracting content from the transformed interaction data; (Modi, paragraph 0175, “normalisation process is then used to transform the log files 10 (which may be in various different formats) into generic normalised metadata or log files 20. The normalization process operates by modelling any human interaction with the client system 100 by breaking it down into discrete events. These events are identified from the content of the log files 10. A schema for each data source used in the network 1000 is defined so that any log file 10 from a known data source in the network 1000 has an identifiable structure, and 'events' and other associated parameters (which may, for example, be metadata related to the events) may be easily identified and be transposed into the schema for the normalized log files 20.”)
receiving one or more segments for each user of the group of users; (Modi, paragraph 0005, “receiving metadata from one or more devices within the one or more monitored computer systems; identifying from the metadata events corresponding to a plurality of user interactions with the monitored computer systems;”)
based on a user's one or more segments, processing the one or more segment using a second algorithm configured to make a prediction about the user's future behavior; and (Modi, paragraph paragraph 0222, “Optionally, the monitoring system 200 may interface with an online dashboard 110, which may be available through a web portal or a mobile application, which may show reports (as previously described) and allow live monitoring of the events detected in the log files 10. This dashboard 110 may comprise a map/location-based view showing all activity or on a map, graphs showing relationships between objects, tables and data around identified graphs, details about events and timelines of related events, for example relating to the same user(s) or object(s).” and paragraph 0199, “This may be used to identify abnormal and/or malicious human interactions with the client system 100. The second layer of statistical learning may be provided by clustering users based on the data produced by the individual modules. Changes in the clusters may be detected, and/or associations can be made between clusters. The change in the data produced by the individual modules may be modelled over time. The data produced by the individual modules may also be dynamically weighted, and/or the data produced by the individual modules may be predicted.”)receiving, for each user, a predicted future behavior; (Modi, paragraph 0019, “By combining historical and live data, meaningful insights can be generated close to real time.”)
based on the user's predicted future behavior, triggering a concurrent communication to the user. (Modi, paragraph 0019, “By combining historical and live data, meaningful insights can be generated close to real time.”)
Lee and Modi are combinable for the same rationale as set forth above with respect to claim 1.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SKYLAR K VANWORMER whose telephone number is (703)756-1571. The examiner can normally be reached M-F 6:00am to 3:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.K.V./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146