DETAILED ACTION
Response to Amendment
This communication is in response to the amendment filed on 01/02/2026 for application 19/043,929. Claims 1-20 are pending in this application.
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 .
Response to Arguments
A double patenting rejection is maintained because terminal disclaimer has been disapproved by the office.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP § 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent Nos. 12,277,048 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of followings.
Present Application 19/043929
US 12,277,048 B2
1. A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: output, using two or more sub-models of a control limit prediction model and based on formatted historical data, model specific predicted control limits; weight, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjust existing control limits based on the overall predicted control limits to create actual control limits; identify, using an upper control limit of the actual control limits, a lower control limit of the actual control limits, and real time data, a deviation score for the real time data; and based on detecting that the deviation score breaches the actual control limits, send one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
2. The computing platform of claim 1, wherein the historical data comprises raw transaction data for a financial product, and wherein the historical data is not configured for processing by the control limit prediction model.
3. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive historical data;
format the historical data, for input into a control limit prediction model, to produce the formatted historical data; and input the formatted historical data into the control limit prediction model.
4. The computing platform of claim 3, wherein formatting the historical data for input into the control limit prediction model comprises one or more of: repairing missing values in the historical data, transforming categorical data of the historical data into numerical values, and scaling the historical data to a common scale.
5. The computing platform of claim 1, wherein the two or more sub-models comprise two or more of: an exponential smoothing model, a seasonal autoregressive integrated moving average model, a random forest model, a stochastic gradient descent model, a boosting model, and a human in the loop component.
6. The computing platform of claim 1, wherein the ensemble model comprises a stack lasso model.
7. The computing platform of claim 1, wherein identifying the deviation score for the real time data comprises applying the following formula: deviation score = ((2 * [value]) - ([lower control limit] + [upper control limit])) / ([upper control limit] - [lower control limit]), wherein: the value comprises the real time data, the lower control limit comprises a lower bound of the actual control limits, and the upper control limit comprises an upper bound of the actual control limits.
8. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, from the enterprise user device, a feedback message including user feedback indicating one or more of: the identified breach is acceptable, the identified breach is more severe than indicated in the indication of the breach, and the identified breach is an outlier data point; and update, based on the user feedback and through a human in the loop component of the control limit prediction model, the control limit prediction model.
9. The computing platform of claim 8, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is acceptable, increasing an acceptable range supported by the actual control limits.
10. The computing platform of claim 8, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is more severe than indicated in the indication of the breach, decreasing an acceptable range supported by the actual control limits.
11. The computing platform of claim 8, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is the outlier data point, flagging the identified breach as an outlier within the control limit prediction model without adjusting the actual control limits.
12. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive the real time data.
13. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: compare the deviation score to the actual control limits.
14. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory:
outputting, using two or more sub-models of a control limit prediction model and based on formatted historical data, model specific predicted control limits; weighting, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjusting existing control limits based on the overall predicted control limits to create actual control limits; identifying, using an upper control limit of the actual control limits, a lower control limit of the actual control limits and real time data, a deviation score for the real time data; and based on detecting that the deviation score breaches the actual control limits, sending one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
15. The method of claim 14, wherein the historical data comprises raw transaction data for a financial product, and wherein the historical data is not configured for processing by the control limit prediction model.
16. The method of claim 14, further comprising: receiving historical data; formatting the historical data, for input into a control limit prediction model, to produce the formatted historical data; and inputting the formatted historical data into the control limit prediction model.
17. The method of claim 16, wherein formatting the historical data for input into the control limit prediction model comprises one or more of: repairing missing values in the historical data, transforming categorical data of the historical data into numerical values, and scaling the historical data to a common scale.
18. The method of claim 14, wherein the two or more sub-models comprise two or more of: an exponential smoothing model, a seasonal autoregressive integrated moving average model, a random forest model, a stochastic gradient descent model, a boosting model, and a human in the loop component.
19. The method of claim 14, wherein the ensemble model comprises a stack lasso model.
20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: output, using two or more sub-models of a control limit prediction model and based on formatted historical data, model specific predicted control limits; weight, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjust existing control limits based on the overall predicted control limits to create actual control limits; identify, using an upper control limit of the actual control limits, a lower control limit of the actual control limits, and real time data, a deviation score for the real time data; and based on detecting that the deviation score breaches the actual control limits, send one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
1. A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive historical data; format the historical data, for input into a control limit prediction model, to produce formatted data; input the formatted data into the control limit prediction model; output, using two or more sub-models of the control limit prediction model, model specific predicted control limits; weight, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjust existing control limits based on the overall predicted control limits to create actual control limits; receive real time data; identify, using an upper control limit of the actual control limits, a lower control limit of the actual control limits, and the real time data, a deviation score for the real time data; compare the deviation score to the actual control limits; and based on detecting that the deviation score breaches the actual control limits, send one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
2. The computing platform of claim 1, wherein the historical data comprises raw transaction data for a financial product, and wherein the historical data is not configured for processing by the control limit prediction model.
3. The computing platform of claim 1, wherein formatting the historical data for input into the control limit prediction model comprises one or more of: repairing missing values in the historical data, transforming categorical data of the historical data into numerical values, and scaling the historical data to a common scale.
4. The computing platform of claim 1, wherein the two or more sub-models comprises two or more of: an exponential smoothing model, a seasonal autoregressive integrated moving average model, a random forest model, a stochastic gradient descent model, a boosting model, and a human in the loop component.
5. The computing platform of claim 1, wherein the ensemble model comprises a stack lasso model.
6. The computing platform of claim 1, wherein identifying the deviation score for the real time data comprises applying the following formula: deviation score=((2*[value])−([lower control limit]+[upper control limit]))/([upper control limit]−[lower control limit]), wherein: the value comprises the real time data, the lower control limit comprises a lower bound of the actual control limits, and the upper control limit comprises an upper bound of the actual control limits.
7. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, from the enterprise user device, a feedback message including user feedback indicating one or more of: the identified breach is acceptable, the identified breach is more severe than indicated in the indication of the breach, and the identified breach is an outlier data point; and update, based on the user feedback and through a human in the loop component of the control limit prediction model, the control limit prediction model.
8. The computing platform of claim 7, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is acceptable, increasing an acceptable range supported by the actual control limits.
9. The computing platform of claim 7, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is more severe than indicated in the indication of the breach, decreasing an acceptable range supported by the actual control limits.
10. The computing platform of claim 7, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is the outlier data point, flagging the identified breach as an outlier within the control limit prediction model without adjusting the actual control limits.
11. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory: receiving historical data; formatting the historical data, for input into a control limit prediction model, to produce formatted data; inputting the formatted data into the control limit prediction model; outputting, using two or more sub-models of the control limit prediction model, model specific predicted control limits; weighting, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjusting existing control limits based on the overall predicted control limits to create actual control limits; receiving real time data; identifying, using an upper control limit of the actual control limits, a lower control limit of the actual control limits and the real time data, a deviation score for the real time data; comparing the deviation score to the actual control limits; and based on detecting that the deviation score breaches the actual control limits, sending one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
12. The method of claim 11, wherein the historical data comprises raw transaction data for a financial product, and wherein the historical data is not configured for processing by the control limit prediction model.
13. The method of claim 11, wherein formatting the historical data for input into the control limit prediction model comprises one or more of: repairing missing values in the historical data, transforming categorical data of the historical data into numerical values, and scaling the historical data to a common scale.
14. The method of claim 11, wherein the two or more sub-model comprise two or more of: an exponential smoothing model, a seasonal autoregressive integrated moving average model, a random forest model, a stochastic gradient descent model, a boosting model, and a human in the loop component.
15. The method of claim 11, wherein the ensemble model comprises a stack lasso model.
16. The method of claim 11, wherein identifying the deviation score for the real time data comprises applying the following formula: deviation score=((2*[value])−([lower control limit]+[upper control limit]))/([upper control limit]−[lower control limit]), wherein: the value comprises the real time data, the lower control limit comprises a lower bound of the actual control limits, and the upper control limit comprises an upper bound of the actual control limits.
17. The method of claim 11, further comprising: receiving, from the enterprise user device, a feedback message including user feedback indicating one or more of: the identified breach is acceptable, the identified breach is more severe than indicated in the indication of the breach, and the identified breach is an outlier data point; and updating, based on the user feedback and through a human in the loop component of the control limit prediction model, the control limit prediction model.
18. The method of claim 17, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is acceptable, increasing an acceptable range supported by the actual control limits.
19. The method of claim 17, wherein updating the control limit prediction model comprises, based on identifying, based on the user feedback, that the identified breach is more severe than indicated in the indication of the breach, decreasing an acceptable range supported by the actual control limits.
20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: receive historical data; format the historical data, for input into a control limit prediction model, to produce formatted data; input the formatted data into the control limit prediction model; output, using two or more sub-models of the control limit prediction model, model specific predicted control limits; weight, using an ensemble model of the control limit prediction model, the outputs from the two or more sub-models to produce overall predicted control limits based on analysis performed by each of the two or more sub-models; adjust existing control limits based on the overall predicted control limits to create actual control limits; receive real time data; identify, using an upper control limit of the actual control limits, a lower control limit of the actual control limits, and the real time data, a deviation score for the real time data; compare the deviation score to the actual control limits; and based on detecting that the deviation score breaches the actual control limits, send one or more commands directing an enterprise user device to display an indication of the breach, wherein sending the one or more commands directing the enterprise user device to display the indication of the breach causes the enterprise user device to display the indication of the breach.
Rationales:
The subject matter claimed in the pending application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter. There are differences between the claims depicted in the bolded words and the underlined words. Pertaining the difference depicted in the bolded words, it appears to be using different wording but meaning is the same. It is therefore deemed obvious to those skilled in the art of claim drafting to draft claim in a later-filed patent application using different wording but same meaning from reading claims in an early- filed patent application issued into a patent. A reason for doing so is to seek a well- rounded protection for a disclose invention. Moreover and pertaining the difference depicted in the underlined words, it appears to be broadening claim by omitting limitations. Nevertheless, it has been held that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 186 USPQ 184(CCPA). Also note Ex Parte Rainu, 168 USPQ 375 (Bd. App. 1969); omission of a reference whose function is not needed would be an obvious variation.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Lowry et al discloses US 8751436 B1 Analyzing Data Quality.
Yeri et al discloses US 20130325674 A1 Trigger data quality monitor.
Mathew et al discloses US 11593100 B2 Autonomous Release Management In Distributed Computing Systems.
Wang discloses US 20240111319 A1 TEMPERATURE CONTROL SYSTEM FOR DEVICE AND TEMPERATURE CONTROL METHOD.
Cmielowski et al discloses US 20190050748 A1 PREDICTION QUALITY ASSESSMENT.
Conclusion
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 extension fee 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 Mohammad A Sana whose telephone number is (571)270-1753. The examiner can normally be reached Monday-Friday 9-5.
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/Mohammad A Sana/Primary Examiner, Art Unit 2166