DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the response filed 9 December 2025.
Claims 1-20 are pending. Claims 1, 12, and 20 are independent claims.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 remain rejected under 35 U.S.C. 103 as being unpatentable over Domenikos et al. (US 2008/0103798, published 1 May 2008, hereafter Domenikos) and further in view of Cox et al. (US 2017/0286622, published 5 October 2017, hereafter Cox) and further in view of Meyer et al. (US 2019/0378619, published 12 December 2019, hereafter Meyer) and further in view of Forman (US 2008/0101689, published 1 May 2008).
As per independent claim 1, Domenikos discloses a computing platform comprising:
at least one processor (paragraph 0111)
a communication interface communicatively coupled to the at least one processor (paragraph 0111)
memory storing computer-readable instructions that, when executed by the at least one processor (paragraph 0111), cause the computing platform to:
receive new event information (paragraph 0065)
input the new event information into the machine learning model, wherein inputting the new event information into the machine learning model causes the machine learning model to output the identity health information (Figure 5; paragraph 0071)
send, to a client device, the identity health information and one or more commands directing the client device to display an identity health information, wherein sending the identity health information and one or more commands directing the client device to display the identity health interface causes the client device to display the identity health interface (Figure 8; paragraph 0090)
Domenikos fails to specifically disclose:
train a machine learning model using historical event information, wherein training the machine learning model comprises:
classifying the historical event information using logical regression
after classifying the historical event information, performing time series calibration on the classified historical event information, wherein training the machine learning model configures the machine learning model to output identity health information
However, Cox, which is analogous to the claimed invention because it is directed toward using machine learning to perform a patient risk assessment, discloses:
train a machine learning model using historical event information, wherein training the machine learning model comprises:
classifying the historical event information using logical regression (Figure 16; paragraph 0246)
after classifying the historical event information, performing time series calibration on the classified historical event information, wherein training the machine learning model configures the machine learning model to output identity health information (Figure 16; paragraph 0246)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s expected filing date to have combined Cox with Domenikos, with a reasonable expectation of success, as it would have allowed for updating a patient health risk assessment via a machine learning model based upon new information. This would have provided the user with flexible and updatable analysis of a user’s health data.
Additionally, Domenikos fails to specifically disclose:
wherein classifying the historical even information comprises training a time static classifier to distinguish between positive and negative cases in the historical event information separately from time series data
wherein performing time series calibration comprises inserting the trained time static classifier into the time series data
However, Meyer, which is analogous to the claimed invention because it is directed toward using machine learning to predict health conditions, discloses:
wherein classifying the historical even information comprises training a time static classifier to distinguish between positive and negative cases in the historical event information separately from time series data (paragraphs 0004-0005 and 0020: Here, a first set of static data comprising features of health data and a second set of dynamic data comprising features of health data are received. The data is processed absent time data and assigning the values for the features to predict the health conditions of the patient and time series data based on the first and second set)
wherein performing time series calibration comprises inserting the trained time static classifier into the time series data (paragraphs 0006 and 0020: Here, the assigned values for features and associated time series data are provided to a trained machine learning model to predict the likelihood of a health condition to occur at a future time)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Meyer with Domenikos-Cox, with a reasonable expectation of success, as it would have allowed for improved training and prediction by a neural network for identifying health conditions.
Finally, Domenikos fails to specifically disclose:
selecting a time static classifier model based on the historical event information
creating, from the historical event information, positive and negative cases for training the selected time classifier model
However, Forman, which is analogous to the claimed invention because it is directed toward training discloses creating, from the historical event information, positive and negative cases for training the selected time classifier model (paragraphs 0033-0035 and 0050: Here, a model is trained using historical training samples that are relatively static over time (paragraph 0050). This includes positive training cases (paragraph 0032) and negative training cases (paragraph 0034)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Forman with Domenikos-Cox-Meyer, with a reasonable expectation of success, as it would have allowed for improved training based upon the training data set (Forman: paragraphs 0012-0014).
Additionally, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date to select a classifier based on historical information. This would have allowed for selecting the classifier based upon received data in order to improve training and classification. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Domenikos-Cox-Meyer-Forman, with a reasonable expectation of success, as it would have allowed for selecting the classifier based upon the received data in order to improve training and classification of data.
As per dependent claim 2, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Domenikos discloses wherein performing the time series calibration comprises performing one or more of alert compounding, N-day window alert capture, or tri-label encoding (paragraph 0104).
As per dependent claim 3, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Domenikos discloses wherein performing the alert compounding comprises considering an alert type a number of times that it appears within a capture window (paragraph 0077).
As per dependent claim 4, Domenikos, Cox, Meyer, and Forman discloses wherein performing the N-day window alert capture comprises considering historical event information from N-days prior to a current data up (paragraph 0077).
As per dependent claim 5, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Domenikos discloses wherein the historical event information comprises identity threat alerts (paragraph 0104).
As per dependent claim 6, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 5, and the same rejection is incorporated herein. Domenikos discloses wherein performing the tri-label encoding comprises labeling the historical event information based on user input indicating that an identity thread alert correctly identified a threat or incorrectly identified a threat or labeling the historical event information to indicate that user input was not received for the corresponding alert (paragraph 0156).
As per dependent claim 7, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Domenikos discloses wherein classifying the historical event information comprises graphing alert data and fraud event data against time and deriving, based on the graph, identity health score data (paragraphs 0156 and 0158).
As per dependent claim 8, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Domenikos discloses wherein displaying the identity health interface comprises displaying the graph (paragraphs 0156 and 0158).
As per dependent claim 9, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Domenikos discloses wherein displaying the identity health interface comprises displaying a color coded scale and a user’s position on the color coded scale, wherein the user’s position on the color coded scale indicates an identity health status for the user (Figure 30; paragraphs 0173-0174).
As per dependent claim 10, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Domenikos discloses wherein the machine learning model is configured to provide real time identify health scoring information (paragraphs 0156 and 0158).
As per dependent claim 11, Domenikos, Cox, Meyer, and Forman disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Domenikos discloses wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, further cause the computing platform to train, using neural network regression, a predictive identity health scoring model configured to predict identity threat events (Figure 19; paragraph 0270).
With respect to claims 12-19, the applicant discloses the limitations substantially similar to those in claims 1-8, respectively. Claims 12-19 are similarly rejected.
With respect to claim 20, the applicant discloses the limitations substantially similar to those in claim 1. Claim 20 is similarly rejected.
Response to Arguments
Applicant's arguments filed 9 December 2025 have been fully considered but they are not persuasive.
The applicant’s arguments are based upon the belief that the prior art fails to disclose or suggest “inserting the trained time static classifier model into the time series data (pages 8-11).” The applicant argues that Meyer merely discloses providing input to a trained machine learning model but not inserting the trained time static classifier model into the time series data (pages 9-10). The examiner respectfully disagrees.
First, it is noted that Cox discloses training a machine learning model using historical event information, wherein training the machine learning model comprises: classifying the historical event information using logical regression (Figure 16; paragraph 0246) and after classifying the historical event information, performing time series calibration on the classified historical event information, wherein training the machine learning model configures the machine learning model to output identity health information (Figure 16; paragraph 0246).
Additionally, Meyer, which is analogous to the claimed invention because it is directed toward using machine learning to predict health conditions, discloses:
wherein classifying the historical even information comprises training a time static classifier to distinguish between positive and negative cases in the historical event information separately from time series data (paragraphs 0004-0005 and 0020: Here, a first set of static data comprising features of health data and a second set of dynamic data comprising features of health data are received. The data is processed absent time data and assigning the values for the features to predict the health conditions of the patient and time series data based on the first and second set)
wherein performing time series calibration comprises inserting the trained time static classifier into the time series data (paragraphs 0006 and 0020: Here, the assigned values for features and associated time series data are provided to a trained machine learning model to predict the likelihood of a health condition to occur at a future time)
The examiner interprets this second data set, having bias values applied to the first data set, as time series data having an inserted trained time static classifier (paragraph 0004).
While the applicant indicates a plurality of examples of inserting the trained time static classifier into the time series data (page 9), it is noted that the applicant does not recite the specific details regarding the insertion within the claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). For these reasons, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Matei et al. (US 2021/0065065): Discloses including a partial model with time series data to improve the time required for training a model (paragraphs 0089 and 0096)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128