DETAILED ACTION
This non-final office action is in response to Applicant’s submission filed September 30, 2024. Claims 1-20 are pending. Claims 1 and 9 are the independent claims.
The instant application is a continuation of application no. 17525675 now U.S. Patent No. 12118440. Application No. 17525675 is a continuation of application no. 17315122 now U.S. Patent No. 11205186.
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 .
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 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 12118440. Although the claims at issue are not identical, they are not patentably distinct from each other because it would be obvious to one skilled in the art to omit/replace, from the independent claims, one or more method step(s): replace the previously claimed iteratively performing, of the parent application with reperforming and omitting the retraining and updating of the neural network prediction model until the neural network prediction model is successfully validated present in the parent.
Additionally, it would have been obvious to one skilled in the art to limit the alternate data to include comprises credit card transaction information and debit card transaction information as such information is well-known, conventional and routine.
Applicant appears to be attempting to broaden the scope of the parent application/patent and capture scope which was forgone during prosecution of the parent application.
The table below maps the conflicting claims between the instant application and U.S. Patent No. 12118440.
18901447
USPN 12118440
1, 9
A computer-implemented method comprising:
receiving, by an online application associated with a user device, user preference information, and transmitting the user preference information to a server;
determining, by the server, one or more user settings based on the received user preference information, wherein the one or more user settings comprises a price limit associated with the user preference information;
training, by the server, a neural network prediction model using back propagation based on a plurality of alternate data inputs of different types associated with at least one company, and the plurality of alternate data comprises credit card transaction information and debit card transaction information;
applying, by the server, the neural network prediction model to new data from a plurality of data sources to generate a forecasted value of a critical indicator of the at least one company;
determining, by the server, a data condition based on the forecasted value of the critical indicator of the at least one company;
providing, by the server, an electronic notification indicative of the data condition to the user device based at least on the price limit of the one or more user settings, wherein the electronic notification includes a recommendation associated with the at least one company based on the data condition, and a user prompt to perform a user request in accordance with the recommendation; and
executing, by the server, the user request in response to a single user input to the user device to select the user prompt, wherein executing the user request comprises automatically submitting an order, in accordance with the recommendation, to a brokerage service without further input from the user,
wherein training the neural network prediction model using back propagation comprises:
receiving, by the server, the plurality of alternate data inputs of different types from a plurality of alternate data providers, each of the plurality of alternate data providers having different types of data;
standardizing, by the server, the plurality of alternate data inputs of different types received in real-time from the plurality of alternate data providers by performing filtering, de-duplication, normalization, and classification;
receiving, by a modelling system for the neural network prediction model executed by the server, the standardized plurality of alternate data inputs of different types and published information of the at least one company, the modeling system employing neural networks to generate, as an output, the forecasted value of the critical indicator of the at least one company;
receiving the output as the forecasted value of the critical indicator of the at least one company in real-time, comprising one or more revenue predictions;
performing, by the server, model validation using historical data associated with the at least one company using back propagation to periodically update the neural network prediction model; and
for the neural network prediction model being determined as not successfully validated, reperforming, by the server, modeling of the neural network prediction model.
1, 9
A computer-implemented method comprising:
receiving, by an online application associated with a user device, user preference information, and transmitting the user preference information to a server;
determining, by the server, one or more user settings based on the received user preference information, wherein the one or more user settings comprises a price limit associated with the user preference information;
training, by the server, a neural network prediction model using back propagation based on a plurality of alternate data inputs of different types associated with at least one company;
applying, by the server, the neural network prediction model to new data from a plurality of data sources to generate a forecasted value of a critical indicator of the at least one company;
determining, by the server, a data condition based on the forecasted value of the critical indicator of the at least one company;
providing, by the server, an electronic notification indicative of the data condition to the user device based at least on the price limit of the one or more user settings, wherein the electronic notification includes a recommendation associated with the at least one company based on the data condition, and a user prompt to perform a user request in accordance with the recommendation; and
executing, by the server, the user request in response to a single user input to the user device to select the user prompt, wherein executing the user request comprises automatically submitting an order, in accordance with the recommendation, to a brokerage service without further input from the user,
wherein training the neural network prediction model using back propagation comprises:
receiving, by the server, the plurality of alternate data inputs of different types from a plurality of alternate data providers, each of the plurality of alternate data providers having different types of data;
standardizing, by the server, the plurality of alternate data inputs of different types received in real-time from the plurality of alternate data providers by performing filtering, de-duplication, normalization, and classification;
receiving, by a modelling system for the neural network prediction model executed by the server, the standardized plurality of alternate data inputs of different types and published information of the at least one company, the modelling system employing neural networks to generate, as an output, the forecasted value of the critical indicator of the at least one company;
receiving the output as the forecasted value of the critical indicator of the at least one company in real-time, comprising one or more revenue predictions; and
performing, by the server, model validation using historical data associated with the at least one company using back propagation to periodically update the neural network prediction model;
for the neural network prediction model being determined as not successfully validated, iteratively performing, by the server, retraining and updating of the neural network prediction model until the neural network prediction model is successfully validated.
2
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3
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6
6
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8
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14
Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou et al., U.S. Patent Publication No. 20210089944 forecasting financial data utilizing machine learning models trained on historical financial data.
Banerjee, U.S. Patent Publication No. 20210125207 system and method for forecasting a critical indicator of a company (e.g. hotel revenue) comprising ingesting data from a variety of external data sources, preparing the data (cleaning, ETL, etc.), training and applying one or more machine learning models to predict/forecast at least one critical company indicator.
Aravala et al., U.S. Patent No. 10963799 system and method for predicting stock price movement utilizing artificial intelligence comprising determining an action to tack relative to the stock price movement, training AI utilizing historical financial data and applying the trained AI to perform predictive analysis
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
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