Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
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 on 05/26/2026 has been entered and considered by the examiner. By the
amendment, claims 1-3, 8-10, 13 and 15 are amended and claims 16-20 are newly added. In view of the amendment made, the previous 101 rejection is still maintained, and the prior rejection is modified. The 112 rejection is withdrawn in view of the amendment made. See office action for detail.
Applicant 101 arguments
The amended claims do not recite a judicial exception.
Examiner response
Examiner respectfully disagrees. The amended claims still recite the mental process because the claims recite the abstract idea of collecting and analyzing information regarding an environment to predict operating indicator, evaluate alternative actions and select an action in response to deviation. The additional elements of a system comprising: an environment data database, an action data database, a data processing module comprising at least one processor, an operator indicator module, an alerting module, an action triggering module, an action reporting module, a prediction engine, a simulation engine and a recommendation engine which are executed by the at least one processor are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); See MPEP 2106.05(f)(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Claim do not provide a technological improvement or meaningful technological application beyond generic computer components to perform the abstract analysis.
Applicant arguments
The amended claims integrate any alleged abstract idea into a practical application by improving how computing systems detect, forecast, simulate, and respond to disruptions. The claimed action-triggering and feedback loop further demonstrates integration into a practical application. The claim amendments are also directed to technical improvements in data storage and processing.
Examiner response
Examiner respectfully disagrees. Although Applicant contends that claim 1 improves how computing systems detect, forecast, simulate, and respond to disruptions through action triggering, a feedback loop and improved data storage and processing, the claim does not recite a specific improvement to computer functionality or another technology. Rather, the recited modules and engines use generic processors and databases to perform information processing functions, including aggregating, correlating, extracting and deleting data; calculating an operating indicator; forecasting its development based on historical data; generating alerts; simulating the effect of possible actions; selecting an action; and triggering the selected action. Merely triggering an action selected based on simulated operating indicator does not establish a technological improvement because the claim does not specify a particular technological operation or machine that is controlled or improved by the targeted action. Accoringdly, considering the claim as a whole, the additional elements merely employ generic computer components to implement the abstract information analysis and decision-making process and therefore do not integrate the judicial exception into a practical application.
Applicant arguments
Moreover, even assuming arguendo that the present claims are considered to be directed to a judicial exception, Applicant submits that the claims remain patent eligible because they recite an inventive concept that satisfies the "significantly more" test under BASCOM Global Internet Services Inc. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016). In BASCOM, the Federal Circuit found that an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces. Similar to the claims held patent eligible by the Federal Circuit in BASCOM, none of the rejected claims merely applies a judicial exception to generic computer components. Rather, each rejected claim contains an inventive concept that may at least be found in a non-conventional and non-generic arrangement of operations performed in a specified manner to provide computerized disruption handling in an environment.
Examiner response
Examiner respectfully disagrees. In BASCOM, the Federal Circuit found that an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces and found significance in the particular arrangement of installing filtering tool at a specific remote location while providing individualized filtering, which constitutes a specific technological implementation rather than merely performing the abstract idea using generic components. However, the instant claim 1 is very distinguished from BASCOM since the additional elements of a system comprising: an environment data database, an action data database, a data processing module comprising at least one processor, an operator indicator module, an alerting module, an action triggering module, an action reporting module, a prediction engine, a simulation engine and a recommendation engine which are executed by the at least one processor are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); Thus, the claim does not recite a specific non-conventional technological arrangement comparable to the particular network architecture in BASCOM.
Response to Applicant 103 arguments
Following Applicants arguments and amendments, the 103 rejections of the claims is Modified. New reference Singh is added, and the Zhang reference is withdrawn. See updated 103 below that is necessitated by applicant’s amendment.
Claim Rejections - 35 USC §101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-7 and 16-20 are directed to apparatus or machine that falls on one of statutory category.
Claims: 8-14 are directed to method or process that falls on one of statutory category.
Claim: 15 is directed to a non-transitory computer-readable storage medium that falls on one of statutory category that is manufacture.
Step 2A Prong 1
Claim 1, 8 and 15 recites
wherein processing the live data comprises at least one of aggregating live data from different sources, correlating different kinds of live data, extracting meaningful parts of the live data, and deleting irrelevant parts of the live data; (this is the mental process for example a person looking at data and can aggregate, correlate, extract meaningful parts and delete irrelevant parts on that data by mental evaluation, which are classic human mental processes. See MPEP 2106.04(a)(2)(III))
calculate a current operating indicator from the live data stored in the environment data database, the operating indicator being a technical indicator relating to parameters of the environment; (This claim is directed to the abstract idea of analyzing data (a mental process) because the steps of calculating and evaluating can be performed mentally, or with pen and paper, and are recited at a high level of generality. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls under the “Mental Process” of abstract idea. Also, the calculation itself is a mathematical algorithm, so it also falls under the “Mathematical Concepts” of abstract ideas.)
forecast a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption; (this is the mental process for example a person looking at historical data and can predicts a future trend which are cognitive, observational, and evaluative, which are classic human mental processes. See MPEP 2106.04(a)(2)(III))
create real-time alerts in response to determining a deviation of the current operating indicator and/or the forecasted operating indicator caused by a disruption in the environment; (The step-receiving data (input), comparing against a threshold (analysis), determining a deviation (judgment), and triggering an alert (action)—map directly to human cognitive processes. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls under the “Mental Process” of abstract idea. It simply uses a computer as a tool to implement the mental process.)
simulate a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts; (Developing an operating indicator for an action by analyzing historical databases is a mental process, as it involves evaluating past environment conditions and action impacts, which can be done with pen and paper. This process also constitutes an abstract idea—a method of organizing information to make a decision—which can be performed mentally or with paper.)
determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern; (A person reviews a simulated, forecasted, or current indicator (e.g., "expected 20% spike in demand next week" or "current stock at 10%") and decides to place an order for more inventory. This decision-making process is fundamentally cognitive. A person can review data, calculate the necessary increase, and make a decision to act without a computer, relying on memory, experience, or manual, simple arithmetic.)
trigger an action selected from the at least one possible action; and to generate additional action data in response to the triggered action. (The steps of "triggering an action" (evaluating a condition) and "generating additional data in response" are mental activities that can be performed in the human mind. The entire process (selecting an action based on criteria and updating a record) can be performed using pen and paper, such as a person reviewing a report, deciding to initiate a task, and writing down the result in a ledger.)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, claim 1, 8 and 15 recites the additional elements of receive live data from an environment, to process the live data, and to store the processed live data in the environment data database” it is simply "a computer receives, processes, and stores data" without adding "meaningful limitations" (e.g., a specific improvement to the functioning of the computer, a specific algorithm that is not conventional, or a specific, non-generic data source), it is merely using a computer as a tool to perform the abstract process faster. See MPEP 2106.05(f)(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The additional elements of a system comprising: an environment data database, an action data database, a data processing module, an operator indicator module, an alerting module, an action triggering module, an action reporting module, a prediction engine, a simulation engine and a recommendation engine which are executed by the at least one processor are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of store the action data in the action data database and store the processed live data in the environment data database is an insignificant extra-solution activity, as it is merely a repository for the data generated by the mental process. The additional elements of a at a computing device comprising at least one processor in claim 8 and a non-transitory computer-readable storage medium comprising computer-readable instructions that, upon execution by a processor of a computing device in claim 15 is mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception,
i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim In particular, claim 1, 8 and 15 recites the additional elements of receive live data from an environment, to process the live data, and to store the processed live data in the environment data database” it is simply "a computer receives, processes, and stores data" without adding "meaningful limitations" (e.g., a specific improvement to the functioning of the computer, a specific algorithm that is not conventional, or a specific, non-generic data source), it is merely using a computer as a tool to perform the abstract process faster. See MPEP 2106.05(f)(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The additional elements of a system comprising: an environment data database, an action data database, a data processing module, an operator indicator module, an alerting module, an action triggering module, an action reporting module, a prediction engine, a simulation engine and a recommendation engine which are executed by the at least one processor are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The additional elements of store the action data in the action data database and store the processed live data in the environment data database is an insignificant extra-solution activity, as it is merely a repository for the data generated by the mental process and are basic computer functions that is well‐understood, routine, and conventional functions see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; The additional elements of a at a computing device comprising at least one processor in claim 8 and a non-transitory computer-readable storage medium comprising computer-readable instructions that, upon execution by a processor of a computing device in claim 15 is mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim is directed to an abstract idea. Thus, claim 1, 8 and 15 are not patent eligible.
Claim 2 and 9 further recites a user interface configured to visualize at least one of the current operating indicator, the forecasted development of the operating indicator, the simulated development of the operating indicator, and the real-time alerts, and to display the at least one possible action and at least one selectable button for the at least one possible action, wherein the action triggering module is further configured to receive the selected action from the user interface in response to a selection of the respective selectable button. The claim element describes a user interface for visualizing indicators (current, forecasted, simulated, alerts) and displaying actions via selectable buttons. This is generally considered a method of organizing and presenting information, which falls into a judicial exception as an abstract idea (specifically, a method of organizing human activity or a form of information presentation). The additional elements of "user interface configured to visualize" and "display the at least one possible action and at least one selectable button" describes a generic computer component performing an insignificant activity. Simply using a user interface or a button to present information and receive input is a well-understood, routine, and conventional computer function. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3 and 10 further recites wherein the recommendation engine is further configured to select an action from the at least one possible action based on the simulated operating indicator, and the action triggering module is further configured to receive the selected action from the recommendation engine. "Selecting an action" based on simulated data is a cognitive process that can be performed mentally or with basic tools. It falls under "Mental Processes" (MPEP 2106.04(a)(2)(III)). Simply having a module "receive" a selection an action is often considered "insignificant extra-solution activity" or "mere instructions to apply an exception". It does not necessarily transform the abstraction into a specific, inventive, and practical, technology-based solution. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 4 and 11 further recites a rule database configured to store deviation scenario indicators, wherein the alerting module is configured to determine the deviation by comparing the current operating indicator and/or the forecasted operating indicator with at least one of the deviation scenario indicators. "Comparing" data points to identify a discrepancy is a form of mathematical analysis or mental process often performable in the human mind or by a generic computer (MPEP 2106.04(a)(2)(III)). The additional elements of a rule database configured to store deviation scenario indicators is an insignificant extra-solution activity, as it is merely a repository for the data generated by the mental process and are basic computer functions that is well‐understood, routine, and conventional functions see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5 and 12 further recites wherein the action triggering module is further configured to trigger the selected action on an external computing platform through a dedicated interface. Claim recites "trigger the selected action on an external computing platform" describes a functional result—an outcome—rather than the specific technological means for achieving that outcome. It is a concept similar to "sending data" or "executing a command remotely" without describing how the action is triggered. This limitation constitutes a "mere instruction to apply" (See MPEP 2106.05(f)) an exception because it merely tells the computer to use a tool (the dedicated interface) to accomplish a result (trigger an action) without specifying how the tool is used to solve a technical problem. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6 further recites wherein the data processing module is further configured to differentiate normal operation trends of the historical data from trends of the historical data resulting from actions and create a link of the historical data with the respective action data. This is a data analysis step directed to comparing data sets to detect anomalies or patterns. The process of separating "normal" data from "action-based" data is a form of intellectual, analytical, or mental comparison. Humans can, and do, look at logs or graphs and mentally distinguish between routine behavior and a change caused by a specific intervention. Creating a link between historical data and action data is a method of organizing, associating, or classifying information. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 13 further recites wherein the data processing module is further configured to differentiate normal operation trends resulting when no action is performed of the historical data from trends of the historical data resulting from actions for simulating or forecasting the development. This is a data analysis step directed to comparing data sets to detect anomalies or patterns. The process of separating "normal" data from "action-based" data is a form of intellectual, analytical, or mental comparison. Humans can, and do, look at logs or graphs and mentally distinguish between routine behavior and a change caused by a specific intervention. Creating a link between historical data and action data is a method of organizing, associating, or classifying information. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7 and 14 further recites wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator. Identifying a relationship or "dependency" between two operating indicators can be performed in the human mind by an analyst observing data trends. Forecasting is the act of predicting future events based on past data and relationships, which is a mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 16 further recites wherein the environment refers to either a flight network or a cellular network. It is generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 17 further recites wherein the operating indicator is one of a number of misconnections, number of delayed flights, averaged delay time, number of disconnected calls, and number of data transfer interruptions. This claim is directed to the abstract idea of analyzing data (a mental process) because the steps of calculating operating indicator and evaluating it all can be performed mentally, or with pen and paper, and are recited at a high level of generality. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls under the “Mental Process” of abstract idea. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 18 further recites a user interface configured to visualize at least one of the current operating indicator, the forecasted development of the operating indicator, the at least one simulated development of the operating indicator, and the real-time alerts, and to display the determined at least one possible action and at least one selectable button for the determined at least one possible action, wherein the action triggering module is further configured to receive the selected action from the user interface in response to a selection of the respective selectable button. The claim element describes a user interface for visualizing indicators (current, forecasted, simulated, alerts) and displaying actions via selectable buttons. This is generally considered a method of organizing and presenting information, which falls into a judicial exception as an abstract idea (specifically, a method of organizing human activity or a form of information presentation). The additional elements of "user interface configured to visualize" and "display the at least one possible action and at least one selectable button" describes a generic computer component performing an insignificant activity. Simply using a user interface or a button to present information and receive input is a well-understood, routine, and conventional computer function. (see MPEP 2106.05(g)iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 19 further recites wherein the data processing module is further configured to differentiate normal operation trends of the historical data from trends of the historical data resulting from actions and create a link of the historical data with the respective action data. This is a data analysis step directed to comparing data sets to detect anomalies or patterns. The process of separating "normal" data from "action-based" data is a form of intellectual, analytical, or mental comparison. Humans can, and do, look at logs or graphs and mentally distinguish between routine behavior and a change caused by a specific intervention. Creating a link between historical data and action data is a method of organizing, associating, or classifying information. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 20 further recites wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator. Identifying a relationship or "dependency" between two operating indicators can be performed in the human mind by an analyst observing data trends. Forecasting is the act of predicting future events based on past data and relationships, which is a mental process. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
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.
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 nonobviousness.
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.
5. Claim(s) 1-15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Andrabi et al. (PUB NO: US20230007023A1) in view of Singh et al. "(PUB NO: US20220300881A1).
Regarding claim 1
Andrabi teaches a system comprising (see fig 12):
an environment data database; (see para 54-Additionally, as shown in FIG. 1, the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104. In particular, the multiple sources of data can include data such as, but not limited to, user information for users of the content management system 104, stored digital content, digital action event information for action events that occur within the content management system 104. In one or more embodiments, the anomalous-event-detection system 106 utilizes the data from the multiple sources of the databases 110 to generate a knowledge graph that connects the data components utilized by the anomaly-detection model to detect anomalous events.)
an action data database; (see para 54-Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104. In particular, the multiple sources of data can include data such as, but not limited to, user information for users of the content management system 104, stored digital content, digital action event information for action events that occur within the content management system 104. In one or more embodiments, the anomalous-event-detection system 106 utilizes the data from the multiple sources of the databases 110 to generate a knowledge graph that connects the data components utilized by the anomaly-detection model to detect anomalous events. See para 66-More specifically, the anomalous-event-detection system 106 can include digital actions, such as digital content deletions, digital content modifications, digital content creations)
a data processing module comprising at least one processor configured to receive live data from an environment, to process the live data, and to store the processed live data in the environment data database; (see para 48- In particular, the anomalous-event-detection system 106 can receive digital actions (in association with digital content) from the client devices 112 a-112 n via the network 108. In particular, the anomalous-event-detection system 106 can receive digital actions (in association with digital content) from the client devices 112 a-112 n via the network 108. See para 54-61- Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104. In particular, the multiple sources of data can include data such as, but not limited to, user information for users of the content management system 104, stored digital content, digital action event information for action events that occur within the content management system 104. As also shown in the act 202 of FIG. 2 , the anomalous-event-detection system 106 further identifies parameters for the digital action, such as an action type, number of files, user location, time, and user role for the identified digital action (e.g., as described in greater detail below in FIG. 3 ).As previously mentioned, in some embodiments, the anomalous-event-detection system 106 can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time to identify digital actions and other data corresponding to the digital actions. See also para 66- For example, the anomalous-event-detection system 106 can utilize a machine-learning model to extract features of digital actions taken by users that represent latent features of the action sequence in which users take digital actions and/or other features of the digital action.)
wherein processing the live data comprises at least one of aggregating live data from different sources, correlating different kinds of live data, (see para 69- As shown in FIG. 3 , in one or more embodiments, the anomalous-event-detection system 106 utilizes a knowledge graph that includes an aggregation of data or interconnected data for users and digital content on the content management system 104. For instance, in reference to FIG. 3 , the anomalous-event-detection system 106 can utilize the knowledge graph 304 that is generated from various combinations of the above-mentioned data (e.g., from the data sources 302 a-302 n). As shown in FIG. 3 , in one or more embodiments, the anomalous-event-detection system 106 integrates the above-mentioned data into an interlinked data structure (or graph) that represents relationships between objects, events, and other concepts from the above-mentioned data to generate or update the knowledge graph 304.)
extracting meaningful parts of the live data and deleting irrelevant parts of the live data;(see para 73- To illustrate, the anomalous-event-detection system 106 can traverse the knowledge graph 304 to identify a digital action and utilize edges corresponding to the node of the digital action within the knowledge graph to extract one or more parameters of the knowledge graph 304. See also para 30- Also, in some instances, the anomalous-event-detection system can modify the anomaly-detection model to emphasize (or bolster) detection of a particular type of file deletion (e.g., mass deletion above a threshold number for a user) as an anomalous action when the administrator device indicates a selection to recover the particular file deletion or confirms an automatic remedial action taken for the detected anomalous action.)
an operating indicator module executed by the at least one processor configured to calculate a current operating indicator from the live data stored in the environment data database; (see para 58-As further shown in act 204 of FIG. 2 , the anomalous-event-detection system 106 detects an anomalous action using an anomaly-detection model. In particular, based on parameters of the digital action, the anomalous-event-detection system 106 can utilize the anomaly detection model to generate an anomaly indicator. As shown in FIG. 2 , the anomaly indicator includes a confidence score that indicates a likelihood of the digital action being an anomalous action. In describing FIGS. 4 and 5 below, this disclosure describes the anomalous-event-detection system 106 utilizing an anomaly-detection model to generate an anomaly indicator based on parameters of a digital action. See para 39- Additionally, as used herein, the term “anomaly indicator” refers to a data object that includes metrics or text to identify or indicate a probability for whether a digital action is anomalous.) the operating indicator being a technical indicator relating to parameters of the environment; (see para 007- Based on the input parameters, the anomaly-detection model can generate an anomaly indicator predicting whether the digital action is anomalous. In some embodiments, for instance, the disclosed systems monitor (and input into the anomaly-detection model) parameters that indicate the type of digital action, a number of affected files, file sizes, a location of the acting user, a time of the digital action, collaborator data corresponding to the user, user-device type, user-account type, and/or a user role of the user.)
an alerting module executed by the at least one processor configured to create real-time alerts in response to determining a deviation of the current operating indicator and/or the forecasted operating indicator caused by a disruption in the environment; (see para 146-In particular, the anomalous-event-detection system 106 can utilize one or both of a sensitivity level 720 and a severity level 722 to determine whether the alert threshold 718 is satisfied for the anomaly indicator 708 and anomalous action type. If the alert threshold 718 is satisfied by the anomaly indicator 708 and anomalous action type, the anomalous-event-detection system 106 can provide the electronic communication including the anomalous action alert to the administrator device 710 and/or perform a remedial action 724. See para 40- As used herein, the term “anomalous action” refers to a digital action that is inconsistent with (or an outlier with respect to) a normal dataset (e.g., normal behavioral data) for a set of digital actions.)
an action triggering module configured to trigger an action selected from the at least one possible action; (see para 26-27-Upon detecting an anomalous action and in response to the anomalous action, in some embodiments, the anomalous-event-detection system automatically performs (or provides selectable options to an administrator device for performing) a remedial action to neutralize or contain an anomalous action. As also part of the electronic communication, the anomalous-event-detection system can provide selectable options to respond to the anomalous action (e.g., select a remedial action to perform. See also fig 9 and para 165)
and an action reporting module executed by the at least one processor configured to generate additional action data in response to the triggered action and to store the action data in the action data database. (see para 29- In addition to alerts or remedial actions, in some embodiments, the anomalous-event-detection system utilizes the data received from an administrator device to modify the anomaly-detection model. More specifically, in one or more embodiments, the anomalous-event-detection system receives indications of which selectable options (as the data) were selected from the administrator device. Based on the received selections to respond or ignore the detected anomalous action, the anomalous-event-detection system can modify the anomaly-detection model (e.g., adjust settings of the model, adjust machine learning parameters of the model). In particular, in one or more embodiments, the anomalous-event-detection system utilizes data (e.g., interactions) received from administrator devices responding to detected anomalous actions as training data (e.g., labels for the training data) for the anomaly-detection model. When the administrator device disregards the anomalous action alert or cancels a remedial action taken for the detected anomalous action…when the administrator device indicates a selection to recover the particular file deletion or confirms an automatic remedial action. See para 54- Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12 ). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104.)
Andrabi does not teach a prediction engine executed by the at least one processor and configured to forecast a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption; a simulation engine executed by the at least one processor and configured to simulate a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts; a recommendation engine executed by the at least one processor and configured to determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern.
In the related field of invention, Singh teaches a prediction engine executed by the at least one processor and configured to forecast a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption; (See para 0043 and fig 5- The data status provider 320 may correspond to a component for forecasting a future value of the KPI. see para 41 and fig 6H-For example, as illustrated in FIG. 6H, the current KPI values may be received as indicated by the second last row including “Actual”, the forecasted KPI values is based on multivariate forecasting mentioned previously and indicated as “Forecast” be received under different intervals indicated by “Sep/2018,” “Oct/2018,” “Nov/2018,” “Dec/2018,” “Jan/2019,” and “Feb/2019.” See para 0071- At step 806, a future value of the KPI is forecasted. In one embodiment, the project value predictor 150 may forecast a future value of the KPI based on the historical data 335. In the project value predictor 150, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI using the first trained data model 345-1, which may be trained using the historical data 335 and/or attributes/features of the relevant KPI cluster related to the KPI. The future value may pertain to a forecasted KPI value at the end of the KPI period or the project closure date. See also step 806 fig 8 para 71)
Examiner note: Before recalibration refers to no action provided or without KPI improvement.
a simulation engine executed by the at least one processor and configured to simulate a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts;(See para 57- 59-In another example, the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. For instance, an entity (e.g., a person, device, or AI system) operating as a subject matter expert may assist to provide a standard set of recommendations to start new initiatives/take additional actions which could help improve the KPI performance based on the KPI name, project type etc. In still another instance, the recommender 325 may provide a recommendation C based on a user input to adjust the target data. For instance, an entity (e.g., a person, device, or AI system) may assist to provide a recommendation to adjust the −KPI target value in scenarios where the KPI may be classified as failure primarily because the target values may be not in-line with expected improvements, i.e., the target value being different from a historical target value set for the same KPI in the KPI cluster. In a further example, the recommender 325 may provide a recommendation D based inputs from an entity (e.g., a person, device, or AI system) on what corrective actions were taken in other similar historical projects where that KPI or similar KPI(s) may be classified as failure but was eventually updated to success during the project period or at the project closure date. The recalibrator 330 may then calculate a future performance of the KPI based on the recalibrated parameter (e.g., related initiative, KPI period, target KPI value, project closure date, etc.) and select a final recommendation from the received recommendations based on the final recommendation providing the highest project value or the highest future performance of the KPI (or future KPI performance. SEE para 77- The final recommendation for recalibration may be selected based on the one that provides the highest project value or the highest future performance of the KPI. In some examples, the selected final recommendation may be assigned a weight based on (i) providing the highest change in the future performance as compared to other recommendations, the corresponding future performance being positive, (iii) the corresponding future performance being greater than a predefined threshold value or a historical performance for a similar project type. In some other examples, based on the assigned weight to the final recommendation, another appropriate weight may be assigned to each other remaining recommendations. See also step 818 and 820 fig 8 para 77-78))
a recommendation engine executed by the at least one processor and configured to determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern; (See para 57-59 and see fig 8 -In another example, the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. The recalibrator 330 may then calculate a future performance of the KPI based on the recalibrated parameter (e.g., related initiative, KPI period, target KPI value, project closure date, etc.) and select a final recommendation from the received recommendations based on the final recommendation providing the highest project value or the highest future performance of the KPI (or future KPI performance). SEE PARA 78- The final recommendation for recalibration may be selected based on the one that provides the highest project value or the highest future performance of the KPI. In some examples, the selected final recommendation may be assigned a weight based on (i) providing the highest change in the future performance as compared to other recommendations, the corresponding future performance being positive, (iii) the corresponding future performance being greater than a predefined threshold value or a historical performance for a similar project type. In some other examples, based on the assigned weight to the final recommendation, another appropriate weight may be assigned to each other remaining recommendations. See also step 818 and 820 fig 8 para 77-78)))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include a prediction engine executed by the at least one processor and configured to forecast a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption; a simulation engine executed by the at least one processor and configured to simulate a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts; a recommendation engine executed by the at least one processor and configured to determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
Regarding claim 8
Andrabi teaches a method comprising: at a computing device at least one processor; (see fig 12):
to receive live data from an environment, to process the live data, and to store the processed live data in the environment data database; (see para 48- In particular, the anomalous-event-detection system 106 can receive digital actions (in association with digital content) from the client devices 112 a-112 n via the network 108. In particular, the anomalous-event-detection system 106 can receive digital actions (in association with digital content) from the client devices 112 a-112 n via the network 108. See para 54-61- Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12 ). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104. In particular, the multiple sources of data can include data such as, but not limited to, user information for users of the content management system 104, stored digital content, digital action event information for action events that occur within the content management system 104. As also shown in the act 202 of FIG. 2 , the anomalous-event-detection system 106 further identifies parameters for the digital action, such as an action type, number of files, user location, time, and user role for the identified digital action (e.g., as described in greater detail below in FIG. 3 ).As previously mentioned, in some embodiments, the anomalous-event-detection system 106 can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time to identify digital actions and other data corresponding to the digital actions. See also para 66- For example, the anomalous-event-detection system 106 can utilize a machine-learning model to extract features of digital actions taken by users that represent latent features of the action sequence in which users take digital actions and/or other features of the digital action.)
wherein processing the live data comprises at least one of aggregating live data from different sources, correlating different kinds of live data, (see para 69- As shown in FIG. 3 , in one or more embodiments, the anomalous-event-detection system 106 utilizes a knowledge graph that includes an aggregation of data or interconnected data for users and digital content on the content management system 104. For instance, in reference to FIG. 3 , the anomalous-event-detection system 106 can utilize the knowledge graph 304 that is generated from various combinations of the above-mentioned data (e.g., from the data sources 302 a-302 n). As shown in FIG. 3 , in one or more embodiments, the anomalous-event-detection system 106 integrates the above-mentioned data into an interlinked data structure (or graph) that represents relationships between objects, events, and other concepts from the above-mentioned data to generate or update the knowledge graph 304.)
extracting meaningful parts of the live data and deleting irrelevant parts of the live data;(see para 73- To illustrate, the anomalous-event-detection system 106 can traverse the knowledge graph 304 to identify a digital action and utilize edges corresponding to the node of the digital action within the knowledge graph to extract one or more parameters of the knowledge graph 304. See also para 30- Also, in some instances, the anomalous-event-detection system can modify the anomaly-detection model to emphasize (or bolster) detection of a particular type of file deletion (e.g., mass deletion above a threshold number for a user) as an anomalous action when the administrator device indicates a selection to recover the particular file deletion or confirms an automatic remedial action taken for the detected anomalous action.)
calculate a current operating indicator from the live data stored in the environment data database; (see para 58-As further shown in act 204 of FIG. 2 , the anomalous-event-detection system 106 detects an anomalous action using an anomaly-detection model. In particular, based on parameters of the digital action, the anomalous-event-detection system 106 can utilize the anomaly detection model to generate an anomaly indicator. As shown in FIG. 2 , the anomaly indicator includes a confidence score that indicates a likelihood of the digital action being an anomalous action. In describing FIGS. 4 and 5 below, this disclosure describes the anomalous-event-detection system 106 utilizing an anomaly-detection model to generate an anomaly indicator based on parameters of a digital action. See para 39- Additionally, as used herein, the term “anomaly indicator” refers to a data object that includes metrics or text to identify or indicate a probability for whether a digital action is anomalous.) the operating indicator being a technical indicator relating to parameters of the environment; (see para 007- Based on the input parameters, the anomaly-detection model can generate an anomaly indicator predicting whether the digital action is anomalous. In some embodiments, for instance, the disclosed systems monitor (and input into the anomaly-detection model) parameters that indicate the type of digital action, a number of affected files, file sizes, a location of the acting user, a time of the digital action, collaborator data corresponding to the user, user-device type, user-account type, and/or a user role of the user.)
create real-time alerts in response to determining a deviation of the current operating indicator and/or the forecasted operating indicator caused by a disruption in the environment; (see para 146-In particular, the anomalous-event-detection system 106 can utilize one or both of a sensitivity level 720 and a severity level 722 to determine whether the alert threshold 718 is satisfied for the anomaly indicator 708 and anomalous action type. If the alert threshold 718 is satisfied by the anomaly indicator 708 and anomalous action type, the anomalous-event-detection system 106 can provide the electronic communication including the anomalous action alert to the administrator device 710 and/or perform a remedial action 724. See para 40- As used herein, the term “anomalous action” refers to a digital action that is inconsistent with (or an outlier with respect to) a normal dataset (e.g., normal behavioral data) for a set of digital actions. )
trigger an action selected from the determined at least one possible action; (see para 26-27-Upon detecting an anomalous action and in response to the anomalous action, in some embodiments, the anomalous-event-detection system automatically performs (or provides selectable options to an administrator device for performing) a remedial action to neutralize or contain an anomalous action. As also part of the electronic communication, the anomalous-event-detection system can provide selectable options to respond to the anomalous action (e.g., select a remedial action to perform. See also fig 9 and para 165)
generate additional action data in response to the triggered action and to store the action data in the action data database. (see para 29- In addition to alerts or remedial actions, in some embodiments, the anomalous-event-detection system utilizes the data received from an administrator device to modify the anomaly-detection model. More specifically, in one or more embodiments, the anomalous-event-detection system receives indications of which selectable options (as the data) were selected from the administrator device. Based on the received selections to respond or ignore the detected anomalous action, the anomalous-event-detection system can modify the anomaly-detection model (e.g., adjust settings of the model, adjust machine learning parameters of the model). In particular, in one or more embodiments, the anomalous-event-detection system utilizes data (e.g., interactions) received from administrator devices responding to detected anomalous actions as training data (e.g., labels for the training data) for the anomaly-detection model. When the administrator device disregards the anomalous action alert or cancels a remedial action taken for the detected anomalous action…when the administrator device indicates a selection to recover the particular file deletion or confirms an automatic remedial action. See para 54- Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12 ). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104.)
Andrabi does not teach forecasting a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption, simulating a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts; determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern defined by or more deviation scenario indicators.
In the related field of invention, Singh teaches forecasting a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption; (See para 0043 and fig 5- The data status provider 320 may correspond to a component for forecasting a future value of the KPI. see para 41 and fig 6H-For example, as illustrated in FIG. 6H, the current KPI values may be received as indicated by the second last row including “Actual”, the forecasted KPI values is based on multivariate forecasting mentioned previously and indicated as “Forecast” be received under different intervals indicated by “Sep/2018,” “Oct/2018,” “Nov/2018,” “Dec/2018,” “Jan/2019,” and “Feb/2019.” See para 0071- At step 806, a future value of the KPI is forecasted. In one embodiment, the project value predictor 150 may forecast a future value of the KPI based on the historical data 335. In the project value predictor 150, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI using the first trained data model 345-1, which may be trained using the historical data 335 and/or attributes/features of the relevant KPI cluster related to the KPI. The future value may pertain to a forecasted KPI value at the end of the KPI period or the project closure date. See also step 806 fig 8 para 71)
Examiner note: Before recalibration refers to no action provided or without KPI improvement.
simulating a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts;(See para 57- 59-In another example, the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. For instance, an entity (e.g., a person, device, or AI system) operating as a subject matter expert may assist to provide a standard set of recommendations to start new initiatives/take additional actions which could help improve the KPI performance based on the KPI name, project type etc. In still another instance, the recommender 325 may provide a recommendation C based on a user input to adjust the target data. For instance, an entity (e.g., a person, device, or AI system) may assist to provide a recommendation to adjust the −KPI target value in scenarios where the KPI may be classified as failure primarily because the target values may be not in-line with expected improvements, i.e., the target value being different from a historical target value set for the same KPI in the KPI cluster. In a further example, the recommender 325 may provide a recommendation D based inputs from an entity (e.g., a person, device, or AI system) on what corrective actions were taken in other similar historical projects where that KPI or similar KPI(s) may be classified as failure but was eventually updated to success during the project period or at the project closure date. The recalibrator 330 may then calculate a future performance of the KPI based on the recalibrated parameter (e.g., related initiative, KPI period, target KPI value, project closure date, etc.) and select a final recommendation from the received recommendations based on the final recommendation providing the highest project value or the highest future performance of the KPI (or future KPI performance. SEE para 77- The final recommendation for recalibration may be selected based on the one that provides the highest project value or the highest future performance of the KPI. In some examples, the selected final recommendation may be assigned a weight based on (i) providing the highest change in the future performance as compared to other recommendations, the corresponding future performance being positive, (iii) the corresponding future performance being greater than a predefined threshold value or a historical performance for a similar project type. In some other examples, based on the assigned weight to the final recommendation, another appropriate weight may be assigned to each other remaining recommendations. See also step 818 and 820 fig 8 para 77-78))
determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern defined by or more deviation scenario indicators (See para 57-59 and see fig 8 -In another example, the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. The recalibrator 330 may then calculate a future performance of the KPI based on the recalibrated parameter (e.g., related initiative, KPI period, target KPI value, project closure date, etc.) and select a final recommendation from the received recommendations based on the final recommendation providing the highest project value or the highest future performance of the KPI (or future KPI performance). See para 78- The final recommendation for recalibration may be selected based on the one that provides the highest project value or the highest future performance of the KPI. In some examples, the selected final recommendation may be assigned a weight based on (i) providing the highest change in the future performance as compared to other recommendations, the corresponding future performance being positive, (iii) the corresponding future performance being greater than a predefined threshold value or a historical performance for a similar project type. In some other examples, based on the assigned weight to the final recommendation, another appropriate weight may be assigned to each other remaining recommendations. See also step 818 and 820 fig 8 para 77-78)))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include a forecasting a development of the operating indicator for no action being performed based on historical data stored in the environment data database, wherein the historical data allows prediction of how the operating indicator will evolve when no action is performed to improve a situation caused by a disruption, simulating a development of the operating indicator for at least one action being performed based on the historical data stored in the environment data database and action data stored in the action data database, wherein the at least one action is simulated based on the created real-time alerts; determine at least one possible action based on the simulated operating indicator, wherein the at least one possible action is at least one action for which the simulation has shown to bring the operating indicator back to a desired pattern defined by or more deviation scenario indicators as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
Regarding claim 15
Andrabi teaches a non-transitory computer-readable storage medium comprising computer-readable instructions that, upon execution by a processor of a computing device, cause the computing device to (see fig 12)
The rest of the claim 15 is very similar to claim 8 and thus rejected for the same reason as claim 8.
Regarding claim 2
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi further teaches a user interface configured to visualize at least one of the current operating indicators, the forecasted development of the operating indicator, the simulated development of the operating indicator, and the real-time alerts, (see para 23- Based on the anomaly indicator, the anomalous-event-detection system can display (e.g., on an administrator device) an electronic communication that indicates the digital action as anomalous. See para 156-Based on the anomaly indicator, the anomalous-event-detection system can display (e.g., on an administrator device) an electronic communication that indicates the digital action as anomalous. see para 159-In addition to such alert options, the anomalous-event-detection system 106 can also provide, for display within a graphical user interface of an administrator device, a severity label for an anomalous alert. For example, the anomalous-event-detection system 106 can utilize a severity score (or level) determined for an anomalous alert to label the anomalous alert with a severity label. To illustrate, the severity label can indicate whether the anomalous alert is a high severity alert and/or a low severity alert. and
to display the at least one possible action and at least one selectable button for the at least one possible action, (see para 161-165- and fig 9-As further suggested above, in one or more embodiments, the anomalous-event-detection system 106 receives setting configurations from an administrator device. For example, FIG. 9 illustrates the anomalous-event-detection system 106 providing, for display within a graphical user interface 904 of an administrator device 902, selectable options to configure one or more settings of the anomalous-event-detection system 106. The anomalous-event-detection system 106 can utilize selections indicated on the graphical user interface 904 to configure how remedial actions are performed and/or how alerts are taken in response to detected anomalous actions. n addition to severity or sensitivity options, in one or more embodiments, the anomalous-event-detection system 106 can provide, for display within the graphical user interface 904 of the administrator device 902, selectable options 910 to toggle remedial actions that can be automatically performed upon detecting an anomalous action.)
wherein the action triggering module is further configured to receive the selected action from the user interface in response to a selection of the respective selectable button. (see para 29- More specifically, in one or more embodiments, the anomalous-event-detection system receives indications of which selectable options (as the data) were selected from the administrator device. See para 120- In addition to remedial actions, in one or more embodiments, the anomalous-event-detection system 106 provides, for display on a graphical user interface of an administrator device, an electronic communication indicating ransomware based on a sequence of server-side digital actions. See also para 179- In certain instances, the act 1140 can include providing, for display on a graphical user interface of an administrator device, a selectable option for a remedial action in response to the digital action)
Claim 9 is substantially like claim 2 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 3
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi further teaches the action triggering module is further configured to receive the selected action from the recommendation engine. (see para 26-27-Upon detecting an anomalous action and in response to the anomalous action, in some embodiments, the anomalous-event-detection system automatically performs (or provides selectable options to an administrator device for performing) a remedial action to neutralize or contain an anomalous action. As also part of the electronic communication, the anomalous-event-detection system can provide selectable options to respond to the anomalous action (e.g., select a remedial action to perform. see para 165 and fig 9- In addition to severity or sensitivity options, in one or more embodiments, the anomalous-event-detection system 106 can provide, for display within the graphical user interface 904 of the administrator device 902, selectable options 910 to toggle remedial actions that can be automatically performed upon detecting an anomalous action. For example, upon receiving a selection of the “recover files” option from the selectable options 910, the anomalous-event-detection system 106 can automatically perform a file recovery upon detecting an anomalous file deletion and/or file transfer. see para 175-178 and fig 11- As further shown in FIG. 11 , the series of acts 1100 include an act 1130 of utilizing an anomaly-detection model to generate an anomaly indicator. Additionally, the act 1140 can include performing a remedial action within a content management system in response to an anomalous action. Moreover, the act 1140 can include performing a remedial action by automatically recovering one or more deleted digital content items, restricting a user account corresponding to a digital action from performing additional digital actions, or modifying a user permission of a user account.)
Andrabi does not explicitly teach wherein the recommendation engine is further configured to select an action from the at least one possible action based on the simulated operating indicator.
However, Singh further teaches wherein the recommendation engine is further configured to select an action from the at least one possible action based on the simulated operating indicator;(See para 57-59 and see fig 8 -In another example, the recommender 325 may provide a recommendation B based on a user input to undertake initiatives/additional actions that would help meet KPI target values. The recalibrator 330 may then calculate a future performance of the KPI based on the recalibrated parameter (e.g., related initiative, KPI period, target KPI value, project closure date, etc.) and select a final recommendation from the received recommendations based on the final recommendation providing the highest project value or the highest future performance of the KPI (or future KPI performance). SEE PARA 78- The final recommendation for recalibration may be selected based on the one that provides the highest project value or the highest future performance of the KPI. In some examples, the selected final recommendation may be assigned a weight based on (i) providing the highest change in the future performance as compared to other recommendations, the corresponding future performance being positive, (iii) the corresponding future performance being greater than a predefined threshold value or a historical performance for a similar project type. In some other examples, based on the assigned weight to the final recommendation, another appropriate weight may be assigned to each other remaining recommendations. See also step 818 and 820 fig 8 para 77-78)))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the recommendation engine is further configured to select an action from the at least one possible action based on the simulated operating indicator as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
Claim 10 is substantially like claim 3 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 4
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi further teaches a rule database configured to store deviation scenario indicators, (see para 28-To determine whether to transmit an alert about a detected anomalous action, in some embodiments, the anomalous-event-detection system determines and compares a severity level and a sensitivity level corresponding to an anomaly indicator to an alert threshold. See para 164-In particular, the anomalous-event-detection system 106 can utilize a machine-learning model that is trained to determine a sensitivity threshold based on characteristics of the digital action, a user account, historical reactions to anomalous actions, and/or an organization corresponding to the user account. See para 54- Additionally, as shown in FIG. 1 , the system 100 includes the databases 110. The databases 110 can include, but are not limited to, server devices, cloud service computing devices, or any other types of computing devices (including those explained below with reference to FIG. 12 ). In one or more embodiments, the databases 110 can include various stored data of the content management system 104. For example, the databases 110 can include multiple sources of data that manage various aspects (or components) of the content management system 104.)
wherein the alerting module is configured to determine the deviation by comparing the current operating indicator and/or the forecasted operating indicator with at least one of the deviation scenario indicators. (See para 28- To determine whether to transmit an alert about a detected anomalous action, in some embodiments, the anomalous-event-detection system determines and compares a severity level and a sensitivity level corresponding to an anomaly indicator to an alert threshold. For example, the anomalous-event-detection system can determine a severity level indicating the importance (e.g., the impact or harmfulness) of an anomalous action based on historical data with alerts for similarly detected anomalous actions. Moreover, the anomalous-event-detection system can determine a sensitivity level indicating if the anomaly-detection model detected an anomalous action with a threshold confidence prior to transmitting an anomalous action alert or performing a remedial action. See also para 39-40-Additionally, as used herein, the term “anomaly indicator” refers to a data object that includes metrics or text to identify or indicate a probability for whether a digital action is anomalous. As used herein, the term “anomalous action” refers to a digital action that is inconsistent with (or an outlier with respect to) a normal dataset (e.g., normal behavioral data) for a set of digital actions.)
Claim 11 is substantially like claim 4 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 5
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi further teaches wherein the action triggering module is further configured to trigger the selected action on an external computing platform through a dedicated interface. (see para 60- the anomalous-event-detection system 106 can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time to identify digital actions and other data corresponding to the digital actions. See para 176-Furthermore, as shown in FIG. 11, the series of acts 1100 include an act 1140 of performing an action based on the anomaly indicator. See para 194-Communication interface 1210 can include hardware, software, or both. In any event, communication interface 1210 can provide one or more interfaces for communication (such as, for example, packet-based communication) between computing device 1200 and one or more other computing devices or networks.)
Claim 12 is substantially like claim 5 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 6
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi further teaches wherein the data processing module is further configured to differentiate normal operation trends of the historical data from trends of the historical data resulting from actions and create a link of the historical data with the respective action data. (see para 40-41-As used herein, the term “anomalous action” refers to a digital action that is inconsistent with (or an outlier with respect to) a normal dataset (e.g., normal behavioral data) for a set of digital actions. As also used herein, the term “context for identifying a digital action as anomalous” refers to information explaining or providing a reason for classifying or identifying a digital action as anomalous or explaining the circumstances of the digital action identified as anomalous. To illustrate, context can include information of a user account corresponding to the anomalous action, a time of the anomalous action, a reason for identifying the digital action as anomalous, or information describing or identifying historical behavior of the user account (e.g., historically normal behavior of a user). See para 69- As shown in FIG. 3 , in one or more embodiments, the anomalous-event-detection system 106 integrates the above-mentioned data into an interlinked data structure (or graph) that represents relationships between objects, events, and other concepts from the above-mentioned data to generate or update the knowledge graph 304. See para 125- In particular, the anomalous-event-detection system 106 can compare parameters corresponding to a digital action (e.g., a number of digital content items affected by a digital action and/or a size of the files affected by the digital action) to a statistical model of historical digital actions to determine whether the digital action is anomalous (e.g., an outlier action).
Claim 13 is substantially like claim 6 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 7
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi does not teach wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator.
However, Singh further teaches wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator. (see para 30-31- In one embodiment, the KPI data may include a set of KPI clusters. Each KPI cluster may be a collection of one or more KPIs having common or shared attributes or features. For example, a KPI cluster may include a set of KPIs such as “no. of new customers invoiced in a quarter” and “no. of new customers acquired in a quarter” related to sales and have “25” as a cluster target value. For example, the data retriever 130 may extract features (including attributes) from each of the KPIs using any of the feature extraction techniques known in the art. See para 43- In some instances, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI. The data status provider 320 may use the first trained data model 345-1 to forecast the future value of the KPI.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
Claim 14 is substantially like claim 7 and is rejected with the same rationale in view of the rejection of claim 8, mutatis mutandis.
Regarding claim 13
The combination of Andrabi and Singh teaches the system of claim 1. Andrabi does not teach wherein the data processing module is further configured to differentiate normal operation trends resulting when no action is performed of the historical data from trends of the historical data for simulating or forecasting the development.
However, Singh further teaches wherein the data processing module is further configured to differentiate normal operation trends resulting when no action is performed of the historical data from trends of the historical data for simulating or forecasting the development. (See para 32-Further, in the historical data 335, each KPI may be associated with an outcome category or label indicating an impact of such KPI on the corresponding historical project. For example, the KPI cluster may include a KPI being associated a “success” category or a “failure” category. In one example, the “success” category—may indicate that the actual KPI values and the related KPI benefit value for the project met or exceeded a predefined or threshold target value, or a historical KPI benefit value associated with the same KPI in a relevant KPI cluster. Similarly, the “failure” category may indicate that the KPI value and the related KPI benefit value for the project is less than or equal to a predefined or threshold target value, or a historical KPI benefit value associated with the same KPI in a relevant KPI cluster. See para 43-The data retriever 130 may send the received input data, i.e., the live data 340, to the data analyzer 315 for processing. The data analyzer 315 may correspond to a component for identifying a relevant KPI cluster for each KPI received in the live data 340 based on the historical data 335. See para 77-At step 818, a recommendation is provided in response to both the KPI and the initiative being categorized as failure. The recommendation may initiate a process to optimize the KPI performance, where the initiated process may recalibrate, a parameter including at least one of the KPI, the initiative, the KPI period, the target KPI value, and the predefined closure date. In another example, a recommendation B may be provided based on an input from an entity (e.g., person, device, AI system) to undertake initiatives/additional actions that may assist to meet KPI target values)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the data processing module is further configured to differentiate normal operation trends resulting when no action is performed of the historical data from trends of the historical data for simulating or forecasting the development as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
Regarding claim 18
Andrabi further teaches a user interface configured to visualize at least one of the current operating indicators, the forecasted development of the operating indicator, the simulated development of the operating indicator, and the real-time alerts, (see para 23- Based on the anomaly indicator, the anomalous-event-detection system can display (e.g., on an administrator device) an electronic communication that indicates the digital action as anomalous. See para 156-Based on the anomaly indicator, the anomalous-event-detection system can display (e.g., on an administrator device) an electronic communication that indicates the digital action as anomalous. see para 159-In addition to such alert options, the anomalous-event-detection system 106 can also provide, for display within a graphical user interface of an administrator device, a severity label for an anomalous alert. For example, the anomalous-event-detection system 106 can utilize a severity score (or level) determined for an anomalous alert to label the anomalous alert with a severity label. To illustrate, the severity label can indicate whether the anomalous alert is a high severity alert and/or a low severity alert. and
to display the at least one possible action and at least one selectable button for the at least one possible action, (see para 161-165- and fig 9-As further suggested above, in one or more embodiments, the anomalous-event-detection system 106 receives setting configurations from an administrator device. For example, FIG. 9 illustrates the anomalous-event-detection system 106 providing, for display within a graphical user interface 904 of an administrator device 902, selectable options to configure one or more settings of the anomalous-event-detection system 106. The anomalous-event-detection system 106 can utilize selections indicated on the graphical user interface 904 to configure how remedial actions are performed and/or how alerts are taken in response to detected anomalous actions. n addition to severity or sensitivity options, in one or more embodiments, the anomalous-event-detection system 106 can provide, for display within the graphical user interface 904 of the administrator device 902, selectable options 910 to toggle remedial actions that can be automatically performed upon detecting an anomalous action.)
wherein the action triggering module is further configured to receive the selected action from the user interface in response to a selection of the respective selectable button. (see para 29- More specifically, in one or more embodiments, the anomalous-event-detection system receives indications of which selectable options (as the data) were selected from the administrator device. See para 120- In addition to remedial actions, in one or more embodiments, the anomalous-event-detection system 106 provides, for display on a graphical user interface of an administrator device, an electronic communication indicating ransomware based on a sequence of server-side digital actions. See also para 179- In certain instances, the act 1140 can include providing, for display on a graphical user interface of an administrator device, a selectable option for a remedial action in response to the digital action)
Regarding claim 19
Andrabi further teaches wherein the data processing module is further configured to differentiate normal operation trends of the historical data from trends of the historical data resulting from actions and create a link of the historical data with the respective action data. (see para 40-41-As used herein, the term “anomalous action” refers to a digital action that is inconsistent with (or an outlier with respect to) a normal dataset (e.g., normal behavioral data) for a set of digital actions. As also used herein, the term “context for identifying a digital action as anomalous” refers to information explaining or providing a reason for classifying or identifying a digital action as anomalous or explaining the circumstances of the digital action identified as anomalous. To illustrate, context can include information of a user account corresponding to the anomalous action, a time of the anomalous action, a reason for identifying the digital action as anomalous, or information describing or identifying historical behavior of the user account (e.g., historically normal behavior of a user). See para 125- In particular, the anomalous-event-detection system 106 can compare parameters corresponding to a digital action (e.g., a number of digital content items affected by a digital action and/or a size of the files affected by the digital action) to a statistical model of historical digital actions to determine whether the digital action is anomalous (e.g., an outlier action).
Regarding claim 20
Andrabi does not teach wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator.
However, Singh further teaches wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator. (see para 30-31- In one embodiment, the KPI data may include a set of KPI clusters. Each KPI cluster may be a collection of one or more KPIs having common or shared attributes or features. For example, a KPI cluster may include a set of KPIs such as “no. of new customers invoiced in a quarter” and “no. of new customers acquired in a quarter” related to sales and have “25” as a cluster target value. For example, the data retriever 130 may extract features (including attributes) from each of the KPIs using any of the feature extraction techniques known in the art. See para 43- In some instances, the data status provider 320 may forecast the future value of the KPI using the current value of the KPI. The data status provider 320 may use the first trained data model 345-1 to forecast the future value of the KPI..)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the prediction engine is configured to determine a dependency between a first operating indicator and a second operating indicator of a plurality of operating indicators for forecasting and/or simulating the development of the first operating indicator and/or the second operating indicator as taught by Singh in the system of Andrabi in order to help in identification of the efficacy of the Initiatives and the impact they have on the KPIs and their improvement. Another motivation is to help initiate processes for recalibrating project inputs to optimize financial benefits obtainable over an intended period before a violation of established metrics or incurring a financial loss. (see para [0014-0015], Singh)
12. Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Andrabi et al. (PUB NO: US20230007023A1) in view of Singh et al. "(PUB NO: US20220300881A1) and further in view of Lamoureux et al. (PUB NO: US20160125327A1)
Regarding claim 16
The combination of Andrabi and Singh teaches the system of claim 1. The combination of Andrabi and Singh does not teach wherein the environment refers to either a flight network or a cellular network.
In the related field of invention, Lamoureux teaches wherein the environment refers to either a flight network or a cellular network. (see para 23-Referring now to FIG. 1, an operating environment 10 in accordance with an embodiment of the invention may include a Global Distribution System (GDS) 11, and may also or alternatively include one or more travel service provider systems, such as a schedule system 13, a departure control system 12, an inventory system 14, a reservation system 16, a compensation system 18, a ticketing system 20, a customer relationship management system 22, a services inventory 23, and a disruption dynamic packaging system 60.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the environment refers to either a flight network or a cellular network as taught by Lamoureux in the system of Andrabi and Singh for to cure a disruption in a passenger's itinerary and can detract from a travel carrier's image. Correct disruption handling is used to maintain passenger satisfaction with the travel carrier and to retain its customers. (see para [0004], Lamoureux)
Regarding claim 17
The combination of Andrabi and Singh teaches the system of claim 1. The combination of Andrabi and Singh does not teach wherein the operating indicator is one of a number of misconnections, number of delayed flights, average delay time, number of disconnected calls, and number of data transfer interruptions.
In the related field of invention, Lamoureux teaches wherein the operating indicator is one of a number of misconnections, number of delayed flights, average delay time, number of disconnected calls, and number of data transfer interruptions. (See para 003- . With respect to an airline as a travel carrier, an operational disruption of a flight may occur when a mechanical issue or weather-related problems force the airline to delay or cancel the flight. See para 31- In operation, notification module 62 receives disruption data characterizing a disruption of a segment in an underlying travel industry or travel carrier associated with packaging system 60, for example, an airline industry. See para 42- . Another package building policy 59B include rules 61B directed to searching for flights back home rather than to initial destination if the passenger is already past the first leg of a trip and the impact of the disruption will be a cancellation or a delay of greater than ten hours at the current location)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting anomalous digital actions utilizing an anomalous-detection model as disclosed by Andrabi to include wherein the operating indicator is one of a number of misconnections, number of delayed flights, average delay time, number of disconnected calls, and number of data transfer interruptions as taught by Lamoureux in the system of Andrabi and Singh for to cure a disruption in a passenger's itinerary and can detract from a travel carrier's image. Correct disruption handling is used to maintain passenger satisfaction with the travel carrier and to retain its customers. (see para [0004], Lamoureux)
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bulusu et al. US 10853867 B1
ii. Discussing the method for providing action recommendations to a user that are likely to result in performance of a high-value action. The user is compared to one or more other users in order to identify high-value actions for that user. Once at least one high-value action has been identified, a sequence of actions may be generated to include that high-value action using prediction model data that includes probability information. The sequence of actions is then assessed to determine a gateway action within the sequence of actions that is likely to be performed by the user and has a high likelihood of resulting in subsequent performance of the high-value action.
13. All claims 1-20 are rejected.
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.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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