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 – 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 17 of U.S. Patent No. 12,218,696.
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).
Also, 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 18/926,584
US Patent Number 12,218,287
(Claim 1)
A system for data compression with protocol adaptation using deep learning, comprising: a plurality of computing devices each comprising at least a processor, a memory, and a network interface; wherein a plurality of programming instructions stored in one or more of the memories and operating on one or more of the processors of the plurality of computing devices causes the plurality of computing devices to: receive a plurality of training data; receive a plurality of protocol policy data; preprocess the training data and protocol policy data using a data preprocessor configured to perform data cleansing, data transformation, data reduction, data normalization, and data splitting; train a deep learning algorithm using the preprocessed training data and protocol policy data, wherein the deep learning algorithm comprises a neural network with multiple hidden layers; implement a machine learning training loop comprising: a trainer configured to manage the training of the deep learning algorithm; a validator configured to evaluate the trained algorithm on a validation dataset; a parametric optimizer configured to tune hyperparameters of the deep learning algorithm based on validation results; generate a protocol appendix using the trained deep learning algorithm, wherein the protocol appendix comprises data manipulation rules for transforming decoded data into protocol formatted data; append the protocol appendix to a codebook; receive encoded data; and decode the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data by applying the data manipulation rules from the protocol appendix.
(Claim 1)
. A system for data compression with protocol adaptation, comprising: a plurality of computing devices each comprising at least a processor, a memory, and a network interface; wherein a plurality of programming instructions stored in one or more of the memories and operating on one or more of the processors of the plurality of computing devices causes the plurality of computing devices to: receive a plurality of training data; receive a plurality of protocol policy data; use a subset of the training data and a subset of the protocol policy data as inputs to train a machine learning algorithm, wherein the machine learning algorithm is configured to produce a protocol appendix; append the protocol appendix to a codebook; receive encoded data; and decode the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data.
(Claim 2)
The system of claim 1, wherein the deep learning algorithm is further used to generate the codebook based on analysis of a second training data.
(Claim 2)
The system of claim 1, wherein the system is further configured to generate the codebook based on analysis of a second training data.
(Claim 3)
The system of claim 1, wherein the neural network comprises convolutional layers.
(Claim 3)
The system of claim 1, wherein the machine learning algorithm is selected from the group consisting of decision trees, random forest, k-Nearest Neighbors and support vector machines.
(Claim 4)
The system of claim 1, wherein the deep learning algorithm is configured to perform feature extraction on the protocol policy data to identify relevant characteristics for protocol formatting.
(Claim 4)
The system of claim 1, wherein the machine learning algorithm is a deep learning algorithm.
(Claim 5)l
The system of claim 4, wherein the deep learning algorithm is trained to identify unique defining features and characteristics in the training data for use in creating mappings between the data and the identified features and characteristics.
(Claim 5)l
The system of claim 4, wherein the deep learning algorithm is a neural network.
(Claim 6)
The system of claim 1, wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.
(Claim 6)
The system of claim 1, wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.
(Claim 7)
. The system of claim 1, wherein the protocol appendix is appended to the codebook in the form of bit extensions, with the structure of the bit extensions determined by the deep learning algorithm.
(Claim 7)
The system of claim 1, wherein the protocol appendix is appended to the codebook in the form of bit extensions.
(Claim 8)
The system of claim 1, wherein the protocol appendix is appended to the codebook in the form of a multi-dimensional array, with the dimensions and structure of the array optimized by the deep learning algorithm.
(Claim 8)
The system of claim 1, wherein the protocol appendix is appended to the codebook in the form of a dimensional array.
(Claim 9)
. A method for data compression with protocol adaptation using deep learning, comprising the steps of: receiving a plurality of training data; receiving a plurality of protocol policy data; preprocessing the training data and protocol policy data using a data preprocessor configured to perform data cleansing, data transformation, data reduction, data normalization, and data splitting; training a deep learning algorithm using the preprocessed training data and protocol policy data, wherein the deep learning algorithm comprises a neural network with multiple hidden layers; implementing a machine learning training loop comprising: a trainer configured to manage the training of the deep learning algorithm; a validator configured to evaluate the trained algorithm on a validation dataset; a parametric optimizer configured to tune hyperparameters of the deep learning algorithm based on validation results; generating a protocol appendix using the trained deep learning algorithm, wherein the protocol appendix comprises data manipulation rules for transforming decoded data into protocol formatted data; appending the protocol appendix to a codebook; receiving encoded data; and decoding the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data by applying the data manipulation rules from the protocol appendix.
(Claim 9)
9. A method for data compression with protocol adaptation, comprising the steps of: receiving a plurality of training data; receiving a plurality of protocol policy data; using a subset of the training data and a subset of the protocol policy data as inputs to train a machine learning algorithm, wherein the machine learning algorithm is configured to produce a protocol appendix; appending the protocol appendix to a codebook; receiving encoded data; and decoding the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data.
(Claim 10)
The method of claim 9, wherein the deep learning algorithm is further used to generate the codebook based on analysis of a second training data.
(Claim 10)
The method of claim 9, further comprising the step of generating the codebook based on analysis of a second training data.
(Claim 11)
The method of claim 9, wherein the neural network comprises convolutional layers.
(Claim 11)
The method of claim 9, wherein the machine learning algorithm is selected from the group consisting of decision trees, random forest, k-Nearest Neighbors and support vector machines.
(Claim 12)
The method of claim 9, wherein the deep learning algorithm is configured to perform feature extraction on the protocol policy data to identify relevant characteristics for protocol formatting.
(Claim 12)
The method of claim 9, wherein the machine learning algorithm is a deep learning algorithm.
(Claim 13)
The method of claim 12, wherein the deep learning algorithm is trained to identify unique defining features and characteristics in the training data for use in creating mappings between the data and the identified features and characteristics.
(Claim 13)
The method of claim 12, wherein the deep learning algorithm is a neural network.
(Claim 14)
The method of claim 9, wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.
(Claim 14)
14. The method of claim 9, wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.
(Claim 15)
The method of claim 9, wherein the protocol appendix is appended to the codebook in the form of bit extensions, with the structure of the bit extensions determined by the deep learning algorithm.
(Claim 15)
The method of claim 9, wherein the protocol appendix is appended to the codebook in the form of bit extensions.
.
(Claim 16)
The method of claim 9, wherein the protocol appendix is appended to the codebook in the form of a multi-dimensional array, with the dimensions and structure of the array optimized by the deep learning algorithm.
(Claim 16)
The method of claim 9, wherein the protocol appendix is appended to the codebook in the form of a dimensional array.
(Claim 17)
A computer-readable, non-transitory medium comprising a plurality of programming instructions that, when operating on a plurality of computing devices each comprising at least a processor, a memory, and a network interface, cause the plurality of computing devices to carry out the method of claim 9.
(Claim 17)
17. A computer-readable, non-transitory medium comprising a plurality of programming instructions that, when operating on a plurality of computing devices each comprising at least a processor, a memory, and a network interface, cause the plurality of computing devices to carry out the method of claim .9
Conclusion
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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