Details
Claims 1-20 are pending.
Claims 1, 4-10, 13-16 and 19-20 are rejected.
Claims 2-3, 11-12 and 17-18 are objected to.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-10, 13-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Abraham et al (Pub. No.: US 2019/0324879 A1) in view of BOUILLET et al (Pub. No.: US 2018/0089582 A1) and Renzi et al (Pub. No.: US 2007/0180490 A1).
As per claim 1, Abraham discloses a method for managing at least one inference model, the method comprising: - obtaining input data (Abraham, paragraph 0061, wherein “The policy enforcer 440 receives the change information 432 which includes, for example, at least the configuration scenario 452, the old state, and the new state. When appropriate, additional contextual information may also be received as part of the change information 432”; wherein the change information can be the input data), (Abraham, paragraph 0029, 0038-0039, 0043-0044, 0047, wherein “Examples of change information 432 received by the policy enforcer 440 may include (but are not limited to): old value, new value”, “The policy enforcer 440 may receive both an old state and a new state of one or more parameters as a part of the change information 432”; thus, the information including the new state value indicate whether a state will occur in a future as claimed); - performing, based on a set of existing policies that define how one or more data processing systems are to operate, a state analysis process to determine whether the input data and/or the plurality of predictions indicate that a new policy is to be generated for the state (Abraham, paragraph 0047, wherein “The policy enforcer 440 analyzes an existing (old) state, as shown by block 208. If the policy enforcer 440 determines that the old state is non-compliant, the policy enforcer sends a PASS action signal to the change detector 430, as shown by block 210, and the BCA 420 applies the configuration change 405, as shown by block 212. If the policy enforcer 440 determines that the old state is compliant, the policy enforcer 440 analyses a new state that would result if the configuration change 405 is implemented, as shown by block 220. If the policy enforcer 440 determines the new state to be non-compliant, the BCA administrator 401 explains the reasons for the change, as shown by block 221. The policy enforcer 440 generates a response action, as shown by block 222. The response action may be an alert, a block, or an approval request 446”); - in an instance of the state analysis process in which the input data and/or the plurality of predictions indicate that the new policy is to be generated for the state: initiating generation of the new policy for the state (Abraham, paragraph 0047, wherein “If the analysis of a new state finds the new state compliant, a PASS signal is sent to the change detector 430, as shown by block 230 and the configuration change 405 is applied, as shown by block 232”); - performing, based on the new policy, an action set to update operation of the one or more data processing systems that are likely to be impacted by the state (Abraham, paragraph 0068, wherein “The approver 403 may approve the approval request 446 in at least two different ways. First, the approver 403 approves and applies the change via approver action path 476 between the workflow engine 470 and the policy enforcer 440. Here, the policy enforcer 440 sends an APPLY signal via the ACTION signals path 442 from the policy enforcer 440 to the change detector 430 associated with the corresponding change ID 434. The change is unlocked and automatically applied. The change ID 434 is discarded, and the change is logged. Alternatively, the approver 403 opens an allowance ticket via an allowance ticket path 472 from the workflow engine 470 to the policy enforcer 440 for the BCA administrator 401 to apply the change. Here, the policy enforcer 440 sends an “ALLOW” signal via the ACTION Signals path 442 from the policy enforcer 440 to the change detector 430 along with the allowance ticket. The allowance ticket is created by the workflow engine 470 via the allowance ticket path 472 from the workflow engine 470 to the policy enforcer 440”; wherein applying the changes to switch to the new state will, by inherence, update operation of one or more data processing systems); Abraham, thus does not explicitly disclose: - the input data being usable as ingest for the at least one inference model to generate a plurality of predictions. However, BOUOLLET discloses - the input data being usable as ingest for the at least one inference model to generate a plurality of predictions (BOUOLLET, paragraph 0014, wherein “The mechanisms of the present invention provide a solution for dynamic adjustment of ensemble models (e.g., predictive models) when a large number of models are used to generate predictions for target variables for streaming data applications. In one aspect, ensemble modeling may be the process of running two or more related, but different prediction or analytical models, and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate Abraham with BOUOLLET to achieve the claimed limitations because this would allow to take the advantage of machine learning techniques to automatically learn to recognize complex patterns and make intelligent decisions based on data which improves the speed and accuracy of the system.Abraham and BOUOLLET do not explicitly disclose - providing computer-implemented services based on the updated operation of the one or more data processing systems. However, Renzi discloses
- providing computer-implemented services based on the updated operation of the one or more data processing systems (Renzi, paragraph 0023, wherein “An embodiment of the invention provides a method for maintaining protection components, including: providing an incentive program for developing a new policy implementer (computer-implemented service); providing a rapid development process to produce the new policy implementer; and distributing the new policy implementer to a target system”).
Therefore, it would have it would have been obvious to one ordinary skill in the art before the effective filing date of the invention to incorporate Abraham and BOUOLLET with Renzi to achieve the claimed limitations because this would improve the security of the system by distributing new policy implementer to all needed nodes/devices.
As per claim 4, claim 1 is incorporated and BOUOLLET further discloses wherein performing the state analysis process comprises: analyzing the plurality of predictions to obtain a statistical characterization regarding agreement in the plurality of predictions; making a determination regarding whether the statistical characterization meets criteria; and in a first instance of the determination in which the statistical characterization does not meet the criteria: concluding that the plurality of predictions indicate that the new policy is to be generated for the state (BOUOLLET, paragraph 0063 “identifying model similarity for the set of ensemble models according to prediction accuracy of a plurality of conditions, and/or identifying one or more ensemble models from the set of ensemble models having model similarity based one or more parameters and a plurality of conditions. One or more of the plurality of policies may be dynamically adjusted for a first ensemble model according to a performance of a second ensemble model having model similarity features. The operations of 600 may also include mapping sampled predictive qualities between those of the set of ensemble models having model similarity features”);
As per claim 5, claim 4 is incorporated and Abraham further discloses wherein the statistical characterization comprises at least one quantity selected from a group consisting of: a mean; a median; a mode; and a standard deviation (BOUOLLET, paragraph 0055 “As one of ordinary skill in the art will appreciate, the policy manager module 460, the ensemble model module 440, and the policy module 450 may implement mathematical modeling, probability and statistical analysis or modeling, probabilistic logic, text data compression, or other data processing technologies to carry out the various mechanisms of the illustrated embodiments. In one aspect, calculations may be performed using various mathematical operations or functions that may involve one or more mathematical operations (e.g., using addition, subtraction, division, multiplication, standard deviations, means, averages, percentages, statistical modeling using statistical distributions, by finding minimums, maximums or similar thresholds for combined variables, etc.)”);
As per claim 6, claim 4 is incorporated and Abraham further discloses wherein performing the state analysis process further comprises: in a second instance of the determination in which the statistical characterization does not meet the criteria: performing, using the plurality of predictions and historical state data, an anomaly detection process to determine whether the state is anomalous; in an instance of the performing in which the state is anomalous: concluding that the plurality of predictions indicate that the new policy is to be generated for the state (Abraham, paragraph 0047, wherein “The policy enforcer 440 analyzes an existing (old) state, as shown by block 208. If the policy enforcer 440 determines that the old state is non-compliant, the policy enforcer sends a PASS action signal to the change detector 430, as shown by block 210, and the BCA 420 applies the configuration change 405, as shown by block 212. If the policy enforcer 440 determines that the old state is compliant, the policy enforcer 440 analyses a new state that would result if the configuration change 405 is implemented, as shown by block 220. If the policy enforcer 440 determines the new state to be non-compliant, the BCA administrator 401 explains the reasons for the change, as shown by block 221. The policy enforcer 440 generates a response action, as shown by block 222. The response action may be an alert, a block, or an approval request 446”);
As per claim 7, claim 1 is incorporated and Abraham further discloses wherein the new policy comprises an action set to be performed under a set of conditions corresponding to the input data and/or the plurality of predictions (Abraham, paragraph 0068, wherein “The approver 403 may approve the approval request 446 in at least two different ways. First, the approver 403 approves and applies the change via approver action path 476 between the workflow engine 470 and the policy enforcer 440. Here, the policy enforcer 440 sends an APPLY signal via the ACTION signals path 442 from the policy enforcer 440 to the change detector 430 associated with the corresponding change ID 434. The change is unlocked and automatically applied. The change ID 434 is discarded, and the change is logged. Alternatively, the approver 403 opens an allowance ticket via an allowance ticket path 472 from the workflow engine 470 to the policy enforcer 440 for the BCA administrator 401 to apply the change. Here, the policy enforcer 440 sends an “ALLOW” signal via the ACTION Signals path 442 from the policy enforcer 440 to the change detector 430 along with the allowance ticket. The allowance ticket is created by the workflow engine 470 via the allowance ticket path 472 from the workflow engine 470 to the policy enforcer 440”; wherein applying the changes to switch to the new state will, by inherence, update operation of one or more data processing systems);
As per claim 8, claim 7 is incorporated and BOUOLLET further discloses wherein initiating generation of the new policy for the state comprises providing the set of conditions corresponding to the input data and/or the plurality of predictions to a subject matter expert (SME) (BOUOLLET, paragraph 0060 “Additionally, the policy manager 506 may include logic to identify model similarity for the set of ensemble models according to prediction accuracy of a plurality of conditions. In a similar fashion, the policy manager 506 may use the model similarities to dynamically adjust the one or more of the plurality of policies for one or more of the ensemble models according to a performance of at least one alternative ensemble model(s), having model similarity features. That is, the policy manager 506 improves the prediction stream of one or more ensemble models based upon a prediction stream of another ensemble model in the family of ensemble models”);
As per claim 9, claim 1 is incorporated and BOUOLLET further discloses wherein each prediction of the plurality of predictions is substantially generated using the input data, the input data being generated by a plurality of data processing systems (BOUOLLET, paragraph 0059 “The policy manager 506, in communication with each one of the ensemble modules, may include logic to analyze each ingested streaming data received into the ensemble models from the model classes and also analyze the predicated target variables of the ensemble models. The policy manger 506 may identify one or more error states according to each prediction stream of the ensemble models. Thus, the policy manager 506 may derive a plurality of policies for the family of ensemble models to predict a plurality of target variables for the incoming, streaming data such that the plurality of policies enables dynamic adjustment of the prediction system”);
Claims 10, 13-16 and 19-20 are rejected under the same rationale as claims 1, and 4-9.
Allowable Subject Matter
Claims 2-3, 11-12 and 17-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/HAMZA N ALGIBHAH/Primary Examiner, Art Unit 2441
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