Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action is in response to the amendment filed 2/25/2026 for application 18/717,263.
Claims 1-20 have been examined and are pending. Claims 1-3, 8-10, and 15-17 have been amended. Claims 1, 8, and 15 are independent claims. This Action is made FINAL.
Response to Arguments
The rejection of claims 1-20 under 35 U.S.C. 101 is withdrawn and the claims have been amended and applicants’ arguments are found persuasive.
Applicants’ arguments in the instant Amendment, filed on 2/25/2026, with respect to limitations listed below, have been fully considered but they are not persuasive.
Applicant argues as follows: Without conceding the propriety of the rejection, and in the interest of expediting prosecution, the independent claims are amended. For example, claim 1 is amended to recite "generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data." Independent claims 8 and 15 are amended to recite similar features. The Applicant respectfully submits that the cited references, alone or in combination, fail to teach or suggest at least this feature of the amended independent claims. For example, with respect to claim 3, the Office Action alleged that Enguehard discloses a family of linear transformations that enforce a recency bias on the time-series data. See Office Action, page 16. In particular, the Office Action cites paragraph [0085] of Enguehard for allegedly teaching recency bias. See Office Action, page 16. But, paragraph [0085] of Enguehard merely mentions recency bias as a limitation of recurrent neural networks (RNNs). For example, Enguehard states that "the recency bias of recurrent neural networks (RNNs) makes it difficult for them to model non-sequential dependencies." See Enguehard, para. [0085]. Enguehard therefore fails to teach, disclose, or suggest purposeful application of a first family of transformations that enforces a recency bias on time-series data to generate a first set of transformed time-series data instances. Instead, Enguehard teaches away from the application of transformations that enforce recency bias by describing recency bias as an undesirable artifact of RNNs that motivates a switch to transformer models that are better at capturing long-term dependencies. See Enguehard, paras. [0085] - [0086].
Examiner respectfully submits that the independent claims are properly rejected by Turner, Zhang, and newly cited reference Gomez. Regarding claim 1, Turner discloses, paragraphs 0036, 0026, 0035, a method that includes one or more processing devices performing operations comprising: receiving a risk assessment query for a target entity from a remote computing device by disclosing automated modeling system can execute a risk assessment; data samples provided by one or more computing devices 102a-c; automated modeling systems 124 receive data from computing devices 102a-c); paragraph 0014, determining a risk indicator for the target entity by disclosing risk indicator provider by the neural network; paragraph 0118, indicating a level of risk associated with the target entity by inputting at least the first set of transformed time-series data instances and the second set of transformed time-series data instances into a machine learning model, wherein the machine learning model determines the risk indicator based on transformed time-series data instances by disclosing neural network, transforming variables, risk indicator, predictor variables; paragraph 0005, such that a monotonic relationship exists between each transformed time-series data instance and the risk indicator by disclosing model development system can identify predictor variables in which a monotonic relationship exists; paragraph 0029, transmitting, to a remote computing device, a responsive message including the risk indicator that is usable for controlling access to one or more interactive computing environments by the target entity by disclosing computing devices 102a-c interact with computing environment 106 with one or more networks 104. Zhang discloses, paragraphs 0005, 0006, 0047, 0004, 0069, accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points by disclosing transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; first transformed time series data set, second transformed time series data set; operations involved in transformed include addition, subtraction, multiplication, division prediction data points; paragraph 0005, 0006, 0047, 0070, generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data by disclosing transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; first transformed time series data set, second transformed time series data set; operations involved in transformed include addition, subtraction, multiplication, division; first set of transformation operations and parameters; paragraph 0005, 0006, 0047, 0070, generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data by disclosing transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; first transformed time series data set, second transformed time series data set; operations involved in transformed include addition, subtraction, multiplication, division; first set of transformation operations and parameters. Turner and Zhang discloses receiving a risk assessment query for a target entity from a remote computing device, generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity, but do not explicitly disclose receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator. Gomez discloses, paragraphs 0043, 0057, 0023, receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment by disclosing request to grant access to restricted information; anchoring bias; score indicative of the riskiness or likelihood of an attack; paragraphs 0043, 0057, 0023, generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data by disclosing request to grant access to restricted information; anchoring bias; score indicative of the riskiness or likelihood of an attack; paragraphs 0043, 0057, 0023, paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack); paragraphs 0043, 0057, 0023, transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator by disclosing request to grant access to restricted information; anchoring bias; score indicative of the riskiness or likelihood of an attack.
The Examiner respectfully suggests that the claim be further amended and details in the specification be incorporated to distinguish the claimed invention over prior art of record. Should the Applicant desire an interview to further clarify the claim interpretation/rejections, please contact the Examiner at (571) 272-5368 to schedule an interview.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-5, 7-9, 11-12, 14-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Turner (US20190340526), PCT filed November 7, 2016, in view of Zhang (US20190171833), filed December 5, 2017, and Gomez (US20230046392), filed August 13, 2021.
Regarding claim 1, Turner discloses a method that includes one or more processing devices performing operations comprising:
receiving a risk assessment query for a target entity from a remote computing device (Turner, paragraph 0036, automated modeling system can execute a risk assessment; paragraph 0026, data samples provided by one or more computing devices 102a-c; paragraph 0035, automated modeling systems 124 receive data from computing devices 102a-c);
determining a risk indicator for the target entity (Turner, paragraph 0014, risk indicator provider by the neural network);
indicating a level of risk associated with the target entity by inputting at least the first set of transformed time-series data instances and the second set of transformed time-series data instances into a machine learning model, wherein the machine learning model determines the risk indicator based on transformed time-series data instances (Turner, paragraph 0118, neural network, transforming variables, risk indicator, predictor variables);
such that a monotonic relationship exists between each transformed time-series data instance and the risk indicator (Turner, paragraph 0005, model development system can identify predictor variables in which a monotonic relationship exists); and
transmitting, to a remote computing device, a responsive message including the risk indicator that is usable for controlling access to one or more interactive computing environments by the target entity (Turner, paragraph 0029, computing devices 102a-c interact with computing environment 106 with one or more networks 104).
Turner does not explicitly disclose accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data.
However, in an analogous art, Zhang discloses accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division, paragraph 0004, prediction paragraph 0069, data points);
generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, first set of transformation operations and parameters);
generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, second set of transformation operations and parameters).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Zhang with the method/ system/ non-transitory computer-readable storage medium of Turner to include accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data to provide users with the benefits of protecting confidential user data against unauthorized access (Zhang: paragraph 0001).
Turner and Zhang discloses receiving a risk assessment query for a target entity from a remote computing device, generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity, but do not explicitly disclose receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator.
However, in an analogous art, Gomez discloses receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Gomez with the method/ system/ non-transitory computer-readable storage medium of Turner and Zhang to include receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator to provide users with the benefits of determining a social engineering attack score to determine appropriate action to deal with an attack if it transgresses a threshold (Gomez: abstract).
Regarding claim 2, Turner, Zhang, and Gomez discloses the method of claim 1. Turner, Zhang, and Gomez disclose wherein the second family of transformations comprises: a family of linear transformations or a family of non- linear transformations (Zhang, paragraph 0054, line transformation; paragraph 0063, linear transformation).
Regarding claim 4, Turner, Zhang, and Gomez discloses the method of claim 1. Turner, Zhang, and Gomez disclose wherein the operations further comprise: generating, for the target entity, explanatory data using the machine learning model indicating relationships between changes in the risk indicator and changes in at least some transformed time-series data instances of the first set of transformed time-series data instances or the second set of transformed time-series data instances (Turner, paragraph 0121, explanatory data, risk indicator; paragraph 0118, transforming predictor variables, risk indicator, explanatory data; paragraph 0073, variance).
Regarding claim 5, Turner, Zhang, and Gomez discloses the method of claim 4. Turner, Zhang, and Gomez disclose wherein the explanatory data is generated by using a points- below-max algorithm or an integrated gradients algorithm (Turner, paragraph 0084, points below max approach).
Regarding claim 7, Turner, Zhang, and Gomez discloses the method of claim 1. Turner, Zhang, and Gomez disclose wherein the machine learning model is a neural network model; the first set of transformed time-series data instances are fed into a first hidden node in a first hidden layer of the neural network model; and the second set of transformed time- series data instances are fed into a second hidden node in the first hidden layer of the neural network model (Turner, paragraph 0117, hidden layers, hidden nodes, neural network, paragraph 0128, hidden nodes, hidden layer, neural network).
Regarding claim 8, Turner discloses a system comprising:
a processing device (Turner, paragraph 0028, processor);
a memory device in which instructions executable by the processing device are stored for causing the processing device to perform operations comprising (Turner, paragraph 0028, machine readable storage medium, memory, storage);
receiving a risk assessment query for a target entity from a remote computing device (Turner, paragraph 0036, automated modeling system can execute a risk assessment; paragraph 0026, data samples provided by one or more computing devices 102a-c; paragraph 0035, automated modeling systems 124 receive data from computing devices 102a-c);
determining a risk indicator for the target entity (Turner, paragraph 0014, risk indicator provider by the neural network);
indicating a level of risk associated with the target entity by inputting at least the first set of transformed time- series data instances and the second set of transformed time-series data instances into a machine learning model, wherein the machine learning model is configured to determine the risk indicator based on transformed time-series data instances (Turner, paragraph 0118, neural network, transforming variables, risk indicator, predictor variables);
such that a monotonic relationship exists between each transformed time-series data instance and the risk indicator (Turner, paragraph 0005, model development system can identify predictor variables in which a monotonic relationship exists); and
transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity (Turner, paragraph 0029, computing devices 102a-c interact with computing environment 106 with one or more networks 104)..
Turner does not explicitly disclose accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data.
However, in an analogous art, Zhang discloses accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division, paragraph 0004, prediction paragraph 0069, data points);
generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, first set of transformation operations and parameters);
generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, second set of transformation operations and parameters).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Zhang with the method/ system/ non-transitory computer-readable storage medium of Turner to include accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data to provide users with the benefits of protecting confidential user data against unauthorized access (Zhang: paragraph 0001).
Turner and Zhang discloses receiving a risk assessment query for a target entity from a remote computing device, generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity, but do not explicitly disclose receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator.
However, in an analogous art, Gomez discloses receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Gomez with the method/ system/ non-transitory computer-readable storage medium of Turner and Zhang to include receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator to provide users with the benefits of determining a social engineering attack score to determine appropriate action to deal with an attack if it transgresses a threshold (Gomez: abstract).
Regarding claim 9, Turner, Zhang, and Gomez disclose the system of claim 8. Turner, Zhang, and Gomez disclose wherein the second family of transformations comprises: a family of linear transformations or a family of non- linear transformations (Zhang, paragraph 0054, line transformation; paragraph 0063, linear transformation).
Regarding claim 11, Turner, Zhang, and Gomez disclose the system of claim 8. Turner, Zhang, and Gomez disclose wherein the operations further comprise: generating, for the target entity, explanatory data using the machine learning model indicating relationships between changes in the risk indicator and changes in at least some transformed time-series data instances of the first set of transformed time-series data instances or the second set of transformed time-series data instances (Turner, paragraph 0121, explanatory data, risk indicator; paragraph 0118, transforming predictor variables, risk indicator, explanatory data; paragraph 0073, variance).
Regarding claim 12, Turner, Zhang, and Gomez disclose the system of claim 11. Turner, Zhang, and Gomez disclose wherein the explanatory data is generated by using a points- below-max algorithm or an integrated gradients algorithm (Turner, paragraph 0084, points below max approach).
Regarding claim 14, Turner, Zhang, and Gomez disclose the system of claim 8. Turner, Zhang, and Gomez disclose wherein the machine learning model is a neural network model having a first hidden layer comprising: a first hidden node configured to receive the first set of transformed time-series data instances; and (Turner, paragraph 0117, hidden layers, hidden nodes, neural network, paragraph 0128, hidden nodes, hidden layer, neural network)
a second hidden node configured to receive the second set of transformed time-series data instances (Turner, paragraph 0117, hidden layers, hidden nodes, neural network, paragraph 0128, hidden nodes, hidden layer, neural network).
Regarding claim 15, Turner discloses a non-transitory computer-readable storage medium having program code that is executable by a processor device to cause a computing device to perform operations, the operations comprising (Turner, paragraph 0028, machine readable storage medium, memory, storage, processor):
receiving a risk assessment query for a target entity from a remote computing device (Turner, paragraph 0036, automated modeling system can execute a risk assessment; paragraph 0026, data samples provided by one or more computing devices 102a-c; paragraph 0035, automated modeling systems 124 receive data from computing devices 102a-c);
determining a risk indicator for the target entity (Turner, paragraph 0014, risk indicator provider by the neural network);
indicating a level of risk associated with the target entity by inputting at least the first set of transformed time-series data instances and the second set of transformed time-series data instances into a machine learning model, wherein the machine learning model is configured to determine the risk indicator based on transformed time-series data instances (Turner, paragraph 0118, neural network, transforming variables, risk indicator, predictor variables);
such that a monotonic relationship exists between each transformed time-series data instance and the risk indicator (Turner, paragraph 0005, model development system can identify predictor variables in which a monotonic relationship exists); and
transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity (Turner, paragraph 0029, computing devices 102a-c interact with computing environment 106 with one or more networks 104).
Turner does not explicitly disclose accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data.
However, in an analogous art, Zhang discloses accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division, paragraph 0004, prediction paragraph 0069, data points);
generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, first set of transformation operations and parameters);
generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data (Zhang, paragraph 0005, transform first time series data to obtain a first transformed time series data set, transform time series data to obtain a second transformed time series data set; paragraph 0006, first transformed time series data set, second transformed time series data set; paragraph 0047, operations involved in transformed include addition, subtraction, multiplication, division; paragraph 0070, second set of transformation operations and parameters).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Zhang with the method/ system/ non-transitory computer-readable storage medium of Turner to include accessing time-series data of a predictor variable associated with a target entity, the time-series data comprising data instances of the predictor variable at a sequence of time points; generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; generating a second set of transformed time-series data instances by applying a second family of transformations on the time-series data to provide users with the benefits of protecting confidential user data against unauthorized access (Zhang: paragraph 0001).
Turner and Zhang discloses receiving a risk assessment query for a target entity from a remote computing device, generating a first set of transformed time-series data instances by applying a first family of transformations on the time-series data; transmitting, to a remote computing device, a responsive message including the risk indicator, wherein the risk indicator is usable for controlling access to one or more interactive computing environments by the target entity, but do not explicitly disclose receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator.
However, in an analogous art, Gomez discloses receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack);
transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator (Gomez, paragraph 0043, request to grant access to restricted information; paragraph 0057, anchoring bias; paragraph 0023, score indicative of the riskiness or likelihood of an attack).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Gomez with the method/ system/ non-transitory computer-readable storage medium of Turner and Zhang to include receiving a risk assessment query for a target entity from a remote computing device, the risk assessment query being associated with a request for the target entity to access an interactive computing environment; generating a first set of transformed time-series data instances by applying a first family of transformations that enforces a recency bias on the time-series data; transmitting, to the remote computing device, a responsive message including the risk indicator to cause the remote computing device to determine whether to grant or deny the request for the target entity to access the interactive computing environment based on the risk indicator to provide users with the benefits of determining a social engineering attack score to determine appropriate action to deal with an attack if it transgresses a threshold (Gomez: abstract).
Regarding claim 16, Turner, Zhang, and Gomez disclose the non-transitory computer-readable storage medium of claim 15. Turner, Zhang, and Gomez disclose wherein the second family of transformations comprises: a family of linear transformations or a family of non-linear transformations (Zhang, paragraph 0054, line transformation; paragraph 0063, linear transformation).
Regarding claim 18, Turner, Zhang, and Gomez disclose the non-transitory computer-readable storage medium of claim 15. Turner, Zhang, and Gomez disclose wherein the operations further comprise: generating, for the target entity, explanatory data using the machine learning model indicating relationships between changes in the risk indicator and changes in at least some transformed time-series data instances of the first set of transformed time-series data instances or the second set of transformed time-series data instances (Turner, paragraph 0121, explanatory data, risk indicator; paragraph 0118, transforming predictor variables, risk indicator, explanatory data; paragraph 0073, variance).
Regarding claim 19, Turner, Zhang, and Gomez disclose the non-transitory computer-readable storage medium of claim 18. Turner, Zhang, and Gomez disclose wherein the explanatory data is configured to be generated by using a points-below-max algorithm or an integrated gradients algorithm (Turner, paragraph 0084, points below max approach).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Turner (US20190340526), PCT filed November 7, 2016, in view of Zhang (US20190171833), filed December 5, 2017, and Gomez (US20230046392), filed August 13, 2021, and further in view of Enguehard (US20210374517), filed May 27, 2020.
Regarding claim 3, Turner, Zhang, and Gomez disclose the method of claim 2.
Turner, Zhang, and Gomez do not explicitly disclose wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations.
However, in an analogous art, Enguehard discloses wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations (Enguehard, paragraph 0085, recency bias; paragraph 0129, linear transformations, neural network).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Enguehard with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations to provide users with the benefits of making predications based on electronic health records (Enguehard: abstract).
Regarding claim 10, Turner, Zhang, and Gomez disclose the system of claim 9.
Turner, Zhang, and Gomez do not explicitly disclose wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations.
However, in an analogous art, Enguehard discloses wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations (Enguehard, paragraph 0085, recency bias; paragraph 0129, linear transformations, neural network)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Enguehard with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations to provide users with the benefits of making predications based on electronic health records (Enguehard: abstract).
Regarding claim 17, Turner, Zhang, and Gomez disclose the non-transitory computer-readable storage medium of claim 16.
Turner, Zhang, and Gomez do not explicitly disclose wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations.
However, in an analogous art, Enguehard discloses wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations (Enguehard, paragraph 0085, recency bias; paragraph 0129, linear transformations, neural network).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Enguehard with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the family of linear transformations comprises a family of transformations to obtain trends or projections in the time-series data based on a linear regression; and wherein the family of non-linear transformations comprises a family of variance, volatility, or mean squared change transformations to provide users with the benefits of making predications based on electronic health records (Enguehard: abstract).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Turner (US20190340526), PCT filed November 7, 2016, in view of Zhang (US20190171833), filed December 5, 2017, and Gomez (US20230046392), filed August 13, 2021, and further in view of McCool (US20230118782), filed October 20, 2021.
Regarding claim 6, Turner, Zhang, and Gomez disclose the method of claim 1.
Turner, Zhang, and Gomez do not explicitly disclose wherein the machine learning model is trained by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations.
However, in an analogous art, McCool discloses wherein the machine learning model is trained by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations. (McCool, paragraph 0024, regularization, least absolute shrinkage and selection operator, time series)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of McCool with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the machine learning model is trained by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations to provide users with the benefits of processing artifacts of distributed entities (McCool: abstract).
Regarding claim 13, Turner, Zhang, and Gomez disclose the system of claim 8.
Turner, Zhang, and Gomez do not explicitly disclose wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations.
However, in an analogous art, McCool discloses wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations (McCool, paragraph 0024, regularization, least absolute shrinkage and selection operator, time series)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of McCool with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and the second set of transformed time-series data instances by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations to provide users with the benefits of processing artifacts of distributed entities (McCool: abstract).
Regarding claim 20, Turner, Zhang, and Gomez disclose the non-transitory computer-readable storage medium of claim 15.
Turner, Zhang, and Gomez do not explicitly disclose wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and a second set of transformed time-series data instances generated by applying a second family of transformations on the time-series data by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations.
However, in an analogous art, McCool discloses wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and a second set of transformed time-series data instances generated by applying a second family of transformations on the time-series data by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations (McCool, paragraph 0024, regularization, least absolute shrinkage and selection operator, time series)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of McCool with the method/ system/ non-transitory computer-readable storage medium of Turner, Zhang, and Gomez to include wherein the machine learning model is trainable by a training process comprising: reducing correlation among the first set of transformed time-series data instances and a second set of transformed time-series data instances generated by applying a second family of transformations on the time-series data by performing correlation analysis, regularization, or group least absolute shrinkage and selection operator (LASSO) on at least the first family of transformations and the second family of transformations to provide users with the benefits of processing artifacts of distributed entities (McCool: abstract).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/W.J.M/Examiner, Art Unit 2439
/LUU T PHAM/Supervisory Patent Examiner, Art Unit 2439
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