DETAILED ACTION
Response to Amendment
This communication is in response to the amendment filed on 11/24/2025 for application 18/884,902. Claims 1, 9 and 17 have been amended. Claims 13 and 20 have been canceled. Claims 1-12 and 14-19 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
In view of the applicant amendment filed on 11/24/2025 previous rejections 35 USC § 101 and 35 USC § 103 have been withdrawn.
A double patenting rejection is maintained because terminal disclaimer has not been filed by the applicant.
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 1-12, 14-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent Nos. 12,112,277 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because of followings.
Present Application 18/884902
US Pat. No: 12,112,277 B2
1. (Currently Amended) A system, comprising: a processor; and a non-transitory memory storing instructions which, when executed by the processor, causes the processor to: output a notification indicative of one or more software objects to a computing device associated with a user, wherein the one or more software objects are associated with financial products and comprise pre-approved features, wherein the one or more software objects are generated using a categorization machine model and an optimization model, and wherein an output of the categorization machine model indicative of a first aspect of the user profile is fed as an input to the optimization model to generate the one or more software objects; receive, from the computing device, a response indicative of an acceptance or a rejection of a software object from the one or more software objects; acquire a user profile of the user; and retrain the categorization machine model and the optimization machine learning model using based on the response received from the user and the user profile, wherein the optimization machine learning model is trained retrained to predict software objects based on the user profile, and wherein the software object is optimized with respect to at least one competitive interest between the user and an entity provider of the software object.
2. The system of claim 1, wherein the categorization machine learning model is a trained categorization or instance based machine learning model configured to predict one or more aspects of the user profile.
3. The system of claim 2, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
4. The system of claim 1, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
5. The system of claim 1, wherein the optimization machine learning model comprises a gradient boosting machine learning model, a random forest model, a bootstrap aggregation model, a stacked generalization model, a gradient boosted regression tree model, or a radial basis function network model.
6. The system of claim 1, wherein the categorization machine learning model is trained using historical data included in a plurality of user activity profiles.
7. The system of claim 1, wherein the notification is output in real time before the user applies for the software object.
8. The system of claim 1, wherein the notification includes one or more features associated with the software object.
9. (Currently Amended) A method, comprising: outputting, by one or more processors, a notification indicative of one or more software objects to a computing device associated with a user; receiving, by the one or more processors, a response from the computing device indicative of an acceptance or a rejection of a software object from the one or more software objects; acquiring, by the one or more processors, a user profile of the user; and retraining, by the one or more processors, a categorization machine model and an optimization machine learning model using the response from the user and the user profile, wherein the optimization machine learning model is trained to predict software objects based on the user profile, and wherein the software object is optimized with respect to at least one competitive interest between the user and an entity provider of the software object, wherein the optimization machine learning model comprises a gradient boosting machine learning model, a random forest model, a bootstrap aggregation model, a stacked generalization model, a gradient boosted regression tree model, or a radial basis function network model.
10. The method of claim 9, wherein the categorization machine learning model is a trained categorization or instance based machine learning model configured to predict one or more aspects of the user profile.
11. The method of claim 10, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
12. The method of claim 9, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
14. The method of claim 9, wherein the categorization machine learning model is trained using historical data included in a plurality of user activity profiles.
15. The method of claim 9, further comprising: outputting the notification in real time before the user applies for the software object.
16. The method of claim 9, wherein outputting the notification comprises: outputting one or more features associated with the software object.
17. (Currently Amended) A non-transitory computer readable medium comprising code which, when executed by a processor, causes the processor to: output a notification indicative of one or more software objects to a computing device associated with a user; receive a response from the computing device indicative of an acceptance or a rejection of a software object from the one or more software objects; acquire a user activity profile of the user; and retrain a categorization machine model and an optimization machine learning model using the response from the user and the user activity profile, wherein the optimization machine learning model is trained to predict software objects based on the user profile, and wherein the software object is optimized with respect to at least one competitive interest between the user and an entity provider of the software object, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
18. The non-transitory computer readable medium of claim 17, wherein the categorization machine learning model is a trained categorization or instance based machine learning model configured to predict one or more aspects of the user profile.
19. The non-transitory computer readable medium of claim 18, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
1. A system, comprising: a processor; and a non-transitory memory storing instructions which, when executed by the processor, causes the processor to: receive a user activity profile of a user; determine, using a categorization machine learning model, a first aspect of a user profile based on at least the user activity profile; determine, using a risk assessment model, a second aspect of the user profile based on at least the user activity profile; predict, using a trained optimization machine learning model, a set of software objects based on the first aspect and the second aspect, wherein a software object of the set of software objects is optimized with respect to at least one competitive interest between the user and an entity provider of the software object, and wherein the software object is associated with feature values; and output a notification indicative of the set of software objects and corresponding feature values to a computing device associated with the user, wherein the set of software objects and the corresponding features values are displayed on a user interface of the computing device associated with the user.
2. The system of claim 1, wherein the categorization machine learning model is a trained categorization or instance based machine learning model configured to predict one or more aspects of the user profile.
3. The system of claim 2, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
4. The system of claim 1, wherein the risk assessment model performs a risk assessment analysis using the user activity profile to generate a confidence value.
5. The system of claim 4, wherein the confidence value indicates a probability that an association between a type of the software object and the user will be approved by the entity provider.
6. The system of claim 1, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
7. The system of claim 1, wherein the trained optimization machine learning model comprises a gradient boosting machine learning model, a random forest model, a bootstrap aggregation model, a stacked generalization model, a gradient boosted regression tree model, or a radial basis function network model.
8. The system of claim 1, the processor is further configured to: wherein the categorization machine learning model is trained using historical data included in a plurality of user activity profiles.
9. The system of claim 1, the processor is configured to: assign a class to the user activity profile, wherein the class corresponds to a feature of the software object.
10. A method, comprising: receiving, by one or more processors, a user activity profile of a user; determining, by the one or more processors and using a categorization machine learning model, a first aspect of a user profile based on at least the user activity profile; determining, by the one or more processors and using a risk assessment model, a second aspect of the user profile based on at least the user activity profile; predicting, by the one or more processors and using a trained optimization machine learning model, a set of software objects based on the first aspect and the second aspect, wherein a software object of the set of software objects is optimized with respect to at least one competitive interest between the user and an entity provider of the software object, and wherein the software object is associated with feature values; and outputting, by the one or more processors, a notification indicative of the set software objects and corresponding feature values to a computing device associated with the user, wherein the set of software objects and the corresponding feature values are displayed on a user interface of the computing device associated with the user.
11. The method of claim 10, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
12. The method of claim 10, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
13. The method of claim 10, wherein the categorization machine learning model is trained using historical data included in a plurality of user activity profiles.
14. The method of claim 10, further comprising: assigning a class to the user activity profile, wherein the class corresponds to a feature of the software object.
15. A non-transitory computer readable medium comprising code which, when executed by a processor, causes the processor to: receive a user activity profile of a user; determine, using a categorization machine learning model, a first aspect of a user profile based on at least the user activity profile; determine, using a risk assessment model, a second aspect of the user profile based on at least the user activity profile; predict, using a trained optimization machine learning model, a set of software objects based on the first aspect and the second aspect, wherein a software object of the set of software objects is optimized with respect to at least one competitive interest between the user and an entity provider of the software object, and wherein the software object is associated with feature values; and output a notification indicative of the set of software objects and corresponding feature values to a computing device associated with the user, wherein the set of software objects and the corresponding feature values are displayed on a user interface of the computing device associated with the user.
16. The non-transitory computer readable medium of claim 15, wherein the categorization machine learning model comprises a k-nearest neighbor machine learning model, a learning vector quantization machine learning model, or a locally learning machine learning model.
17. The non-transitory computer readable medium of claim 15, wherein the at least one competitive interest comprises maximizing a profit of the entity provider and minimizing an interest rate associated with the software object, or maximizing a probability that the user accepts the software object and minimizing a risk of monetary loss by the entity provider associated with the software object.
18. The non-transitory computer readable medium of claim 15, wherein the trained optimization machine learning model comprises a gradient boosting machine learning model, a random forest model, a bootstrap aggregation model, a stacked generalization model, a gradient boosted regression tree model, or a radial basis function network model.
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.
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