Detailed Office Action
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- 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 20 of U.S. Patent No. 12,232,075.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patents make obvious the claims of the pending application in that the limitations claimed in the pending application are found in the issued patents even though they are not necessarily presented in the same order as those in the issued patents. “A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent make obvious the claims of the pending application in that the limitations claimed in the pending application are similarly claimed in the issued patents and the claims are directed to substantially the same subject matter as the parent case though not necessarily presented in the same sequential order or the same claim numbering as shown in the table below.
US Application Number 18/934,825
US Patent Number 12,232,075
(Claim 1)
A computer-implemented method for improving compression of predictive models, the computer-implemented method comprising: providing a labeled data set, wherein the labeled data set comprises: one or more first fact sets; and one or more labels; and training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises: (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model; (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions; and (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
(Claim 1)
A computer-implemented method for improving compression of predictive models, the computer-implemented method comprising: generating an unlabeled simulated data set by expanding an initial data set, wherein the initial data set includes a first plurality of fact sets and wherein the unlabeled simulated data set includes a second plurality of fact sets; generating a labeled data set, at least by predicting the unlabeled simulated data set using a complex model to output a plurality of labels, wherein the labeled data set includes the second plurality of fact sets and the plurality of labels, and wherein each fact set of the second plurality of fact sets corresponds to a respective one of the plurality of labels; and training, using the labeled data set, a neural network model associated with a plurality of training parameters, wherein training the neural network model includes: (i) generating a plurality of intermediate predictions, at least by predicting the second plurality of fact sets using the neural network model, (ii) comparing the plurality of labels to the plurality of intermediate predictions to produce a measure of accuracy, and (iii) modifying, based on the measure of accuracy, at least one of the plurality of training parameters of the neural network model.
(Claim 2)
The computer-implemented method of claim 1 wherein (i), (ii), or (iii) are iteratively repeated until the measure of accuracy is within a predetermined threshold.
(Claim 2)
The computer-implemented method of claim 1 wherein (i), (ii), and (iii) are iteratively repeated until the measure of accuracy is within a predetermined threshold.
(Claim 3)
The computer-implemented method of claim 1, further comprising: providing an unlabeled simulated data set by expanding an initial data set, wherein: the unlabeled simulated data set comprises the one or more first fact sets; and the initial data set comprises one or more second fact sets.
(Claim 3)
The computer-implemented method of claim 1, wherein the first plurality of fact sets and the second plurality of fact sets both include a plurality of fact types, and wherein generating the unlabeled simulated data set by expanding the initial data set includes generating the unlabeled simulated data set such that a distribution of the plurality of fact types within the second plurality of fact sets is skewed as compared to a distribution of the plurality of fact types within the first plurality of fact sets.
(Claim 4)
The computer-implemented method of claim 3, wherein: the one or more first fact sets and the one or more second fact sets comprise one or more fact types; and providing the unlabeled simulated data set comprises providing the unlabeled simulated data set such that a distribution of the one or more fact types of the one or more first fact sets is skewed as compared to a distribution of the one or more fact types of the one or more second fact sets
(Claim 4)
The computer-implemented method of claim 1, further comprising: generating a graphical depiction of the neural network model, as trained.
(Claim 5)
The computer-implemented method of claim 1, further comprising: receiving, from an electronic database, a definition of the one or more training parameters of the neural network model.
(Claim 5)
The computer-implemented method of claim 1, further comprising: accessing a remote electronic database; and obtaining, from the remote electronic database, a definition of the plurality of training parameters of the neural network model.
(Claim 6)
The computer-implemented method of claim 1, wherein generating the one or more intermediate predictions comprises: dividing the one or more first fact sets into one or more fact subsets; receiving, at one or more networked computing devices, the one or more fact subsets; generating, by the one or more networked computing devices, a respective intermediate prediction; and receiving, at the one or more networked computing devices, the respective intermediate prediction.
(Claim 6)
The computer-implemented method of claim 1, wherein generating the plurality of intermediate predictions includes: dividing the second plurality of fact sets into fact subsets; receiving, at each of a plurality of networked computing devices, one of the fact subsets; generating, by each of the plurality of networked computing devices predicting a respective one of the fact subsets using the neural network model, a respective intermediate prediction; and receiving, at a single networked computing device, the respective intermediate prediction corresponding to each of the fact subsets.
(Claim 7)
The computer-implemented method of claim 1, further comprising: transmitting, to a computing device, the neural network model, as trained; and providing an unlabeled new data set based upon data collected by the computing device, wherein the unlabeled new data set comprises one or more new fact sets.
(Claim 7)
The computer-implemented method of claim 1, further comprising: receiving, in a computing device, the neural network model, as trained; generating an unlabeled new data set based upon data collected by the computing device, wherein the unlabeled new data set includes a plurality of new fact sets; and generating a plurality of device predictions, at least by predicting the unlabeled new data set using the neural network model, as trained.
(Claim 8)
The computer-implemented method of claim 7, further comprising: generating one or more device predictions, by at least predicting the unlabeled new data set using the neural network model, as trained.
(Claim 8)
The computer-implemented method of claim 1, further comprising: sending, to a remote computing device, the neural network model, as trained, to enable the remote computing device to analyze one or more unlabeled new data sets using the neural network model, as trained.
(Claim 9)
. The computer-implemented method of claim 1, further comprising: sending, to a computing device, the neural network model, as trained, to enable the computing device to analyze one or more unlabeled new data sets using the neural network model, as trained.
(Claim 10)
The computer-implemented method of claim 9, wherein the computing device is a mobile electronic device of a user.
(Claim 9)
The computer-implemented method of claim 8, wherein sending, to the remote computing device, the neural network model, as trained, comprises sending the neural network model, as trained, to a mobile computing device of a user.
(Claim 11)
. A system comprising: one or more processors; and one or more non-transitory computer-readable storage media storing computing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: providing a labeled data set, wherein the labeled data set comprises: one or more first fact sets; and one or more labels; and training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises: (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model; (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions; and (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
(Claim 10)
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to: generate an unlabeled simulated data set by expanding an initial data set, wherein the initial data set includes a first plurality of fact sets and wherein the unlabeled simulated data set includes a second plurality of fact sets, generate a labeled data set, at least by predicting the unlabeled simulated data set using a complex model to output a plurality of labels, wherein the labeled data set includes the second plurality of fact sets and the plurality of labels, and wherein each fact set of the second plurality of fact sets corresponds to a respective one of the plurality of labels, and train, using the labeled data set, a neural network model associated with a plurality of training parameters, wherein training the neural network model includes: (i) generating a plurality of intermediate predictions, at least by predicting the second plurality of fact sets using the neural network model, (ii) comparing the plurality of labels to the plurality of intermediate predictions to produce a measure of accuracy, and (iii) modifying, based on the measure of accuracy, at least one of the plurality of training parameters of the neural network model.
(Claim 12)
The system of claim 11, wherein (i), (ii), or (iii) are iteratively repeated until the measure of accuracy is within a predetermined threshold.
(Claim 11)
The computing system of claim 10 wherein the instructions further cause (i), (ii), and (iii) to iteratively repeat until the measure of accuracy is within a predetermined threshold.
(Claim 13)
The system of claim 11, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: providing an unlabeled simulated data set by expanding an initial data set, wherein: the unlabeled simulated data set comprises the one or more first fact sets; and the initial data set comprises one or more second fact sets.
(Claim 12)
The computing system of claim 10, wherein the first plurality of fact sets and the second plurality of fact sets both include a plurality of fact types, and wherein the instructions cause the computing system to generate the unlabeled simulated data set such that a distribution of the plurality of fact types within the second plurality of fact sets is skewed as compared to a distribution of the plurality of fact types within the first plurality of fact sets.
(Claim 14)
The system of claim 13, wherein: the one or more first fact sets and the one or more second fact sets comprise one or more fact types; and providing the unlabeled simulated data set comprises providing the unlabeled simulated data set such that a distribution of the one or more fact types of the one or more first fact sets is skewed as compared to a distribution of the one or more fact types of the one or more second fact sets.
(Claim 13)
The computing system claim 10, wherein the instructions further cause the computing system to generate a graphical depiction of the neural network model, as trained.
(Claim 15)
The system of claim 11, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: receiving, from an electronic database, a definition of the one or more training parameters of the neural network model.
.(Claim 14)
The computing system of claim 10, further comprising: a remote electronic database, wherein the instructions further cause the computing system to access the remote electronic database, and obtain, from the remote electronic database, a definition of the plurality of training parameters of the neural network model
(Claim 16)
The system of claim 11, wherein generating the one or more intermediate predictions comprises: dividing the one or more first fact sets into one or more fact subsets; receiving, at one or more networked computing devices, the one or more fact subsets; generating, by the one or more networked computing devices, a respective intermediate prediction; and receiving, at the one or more networked computing devices, the respective intermediate prediction.
(Claim 17)
The system of claim 11, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: transmitting, to a computing device, the neural network model, as trained; and providing an unlabeled new data set based upon data collected by the computing device, wherein the unlabeled new data set comprises one or more new fact sets.
(Claim 15)
The computing system of claim 10, wherein the instructions further cause the computing system to: send, to a remote computing device, the neural network model, as trained, to enable the remote computing device to analyze one or more unlabeled new data sets using the neural network model, as trained.
(Claim 18)
The system of claim 17, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: generating one or more device predictions, by at least predicting the unlabeled new data set using the neural network model, as trained.
(Claim 16)
The computing system of claim 15, wherein the remote computing device is a mobile computing device of a user.
(Claim 19)
The system of claim 11, wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform operations comprising: sending, to a mobile electronic device, the neural network model, as trained, to enable the mobile electronic device of a user to analyze one or more unlabeled new data sets using the neural network model, as trained.
(Claim 20)
A non-transitory computer readable storage medium storing one or more computing instructions that, when run on one or more processors, cause the one or more processors to perform operations comprising: providing a labeled data set, wherein the labeled data set comprises: one or more first fact sets; and one or more labels; and training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model, wherein training the neural network model comprises: (i) generating one or more intermediate predictions, by at least predicting the one or more first fact sets using the neural network model; (ii) comparing the one or more labels to the one or more intermediate predictions to produce a measure of accuracy for the one or more intermediate predictions; and (iii) modifying, based on the measure of accuracy, at least one of the one or more training parameters of the neural network model.
(Claim 17)
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: generate an unlabeled simulated data set by expanding an initial data set, wherein the initial data set includes a first plurality of fact sets and wherein the unlabeled simulated data set includes a second plurality of fact sets; generate a labeled data set, at least by predicting the unlabeled simulated data set using a complex model to output a plurality of labels, wherein the labeled data set includes the second plurality of fact sets and the plurality of labels, and wherein each fact set of the second plurality of fact sets corresponds to a respective one of the plurality of labels; and train, using the labeled data set, a neural network model associated with a plurality of training parameters, at least in part by: (i) generating a plurality of intermediate predictions, at least by predicting the second plurality of fact sets using the neural network model; (ii) comparing the plurality of labels to the plurality of intermediate predictions to produce a measure of accuracy; and (iii) modifying, based on the measure of accuracy, at least one of the plurality of training parameters of the neural network model.
(Claim 18)
The non-transitory computer readable medium of claim 17 containing further program instructions that when executed, cause the computer to: iteratively repeat (i), (ii), and (iii) until the measure of accuracy is within a predetermined threshold.
(Claim 19)
The non-transitory computer readable medium of claim 17, wherein the first plurality of fact sets and the second plurality of fact sets both include a plurality of fact types, and wherein the program instructions, when executed, cause the computer to generate the unlabeled simulated data set such that a distribution of the plurality of fact types within the second plurality of fact sets is skewed as compared to a distribution of the plurality of fact types within the first plurality of fact sets.
(Claim 20)
The non-transitory computer readable medium of claim 17, further comprising program instructions that, when executed, cause the computer to: generate a graphical depiction of the neural network model, as trained.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN BRUNER JEANGLAUDE whose telephone number is (571)272-1804. The examiner can normally be reached Monday-Thursday 7:00 AM-5:00 PM.
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/JEAN B JEANGLAUDE/Primary Examiner, Art Unit 2845
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