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 – 27 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 27 of U.S. Patent No. 12,289,121.
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 19/070,434
US Patent Number 12,289,121
(Claim 1)
A system for decoding and enhancing compressed data streams, comprising: a computing device comprising at least a memory and a processor; a plurality of programming instructions, when operating on the processor, cause the computing device to: receive a compressed data stream; decompress the compressed data stream to produce a decompressed data stream; analyze characteristics of the decompressed data stream; enhance the decompressed data stream using at least one of a neural network model, a data transformer, or a stream conditioner, to produce an enhanced data stream; and output the enhanced data stream.
(Claim 1)
A system for decoding and enhancing lossy compressed data streams, comprising: a computing device comprising at least a memory and a processor; a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: receive a compressed data stream; decompress the compressed data stream to produce a decompressed data stream; enhance the decompressed data stream using at least one neural network model to produce an enhanced data stream; estimate the quality of the enhanced data stream; and output the enhanced data stream; wherein the at least one neural network model comprises an adaptive neural upsampler configured to recover information lost in compression by leveraging correlations between subsets of the compressed data stream.
(Claim 2)
The system of claim 1, wherein decompressing the data stream comprises using at least of one of a Huffman decoder, a dyadic distribution decoder, or a prefix-based decoder.
(Claim 2)
The system of claim 1, wherein decompressing the compressed data stream comprises using a Huffman decoder.
(Claim 3) The system of claim 1, wherein analyzing characteristics of the decompressed data stream comprises determining data type, complexity, noise level, or compression ratio.
(Claim 3)
The system of claim 1, wherein the computing device is further caused to analyze characteristics of the decompressed data stream.
(Claim 4)
The system of claim 1, wherein the instructions further cause the system to select an enhancement technique from a plurality of available enhancement techniques based on the analyzed characteristics.
(Claim 4)
The system of claim 3, wherein the computing device is further caused to select an appropriate neural network model from a plurality of neural network models based on the analyzed characteristics.
(Claim 5)
The system of claim 1, wherein enhancing the decompressed data stream comprises at least one of recovering lost information, improving resolution, reducing noise, or correcting artifacts introduced during compression.
(Claim 5)
The system of claim 1, wherein enhancing the decompressed data stream comprises recovering information lost during lossy compression.
(Claim 6)
The system of claim 1, wherein the instructions further cause the system to dynamically adjust enhancement parameters based on the determined quality metric.
(Claim 6)
The system of claim 1, wherein the computing device is further caused to adjust the neural network model based on the estimated quality of the enhanced data stream.
(Claim 7)
. The system of claim 1, wherein the instructions further cause the system to implement at least one security measure selected from hash verification, digital signatures, encrypted data processing, secure enclaves, input validation, secure communication channels, and audit logging.
(Claim 7)
The system of claim 1, wherein the computing device is further caused to implement security measures to ensure data integrity throughout the enhancement process.
(Claim 8)
The system of claim 1, wherein the compressed data stream comprises at least one of financial time-series data, image data, audio data, video data, sensor data, or genetic information.
(Claim 8)
The system of claim 1, wherein the compressed data stream comprises financial time-series data.
(Claim 9)
. The system of claim 1, wherein any enhancement techniques used are trained using paired training data comprising original uncompressed data and corresponding compressed versions of the original uncompressed data.
(Claim 9)
The system of claim 1, wherein the neural network model is trained using pairs of original uncompressed data and corresponding lossy compressed data.
(Claim 10)
The system of claim 1, wherein the instructions further cause the system to implement adaptive learning to modify enhancement parameters based on processed data streams.
(Claim 10)
The system of claim 1, wherein the computing device is further caused to perform online learning to adapt the neural network model based on recent data streams.
(Claim 11)
The system of claim 1, wherein determining the quality metric comprises at least one of comparing against predicted original data, measuring statistical properties, calculating distortion metrics, or evaluating perceptual quality
(Claim 11)
The system of claim 1, wherein estimating the quality of the enhanced data stream comprises comparing the enhanced data stream to a predicted original data stream.
(Claim 12)
The system of claim 1, wherein the instructions further cause the system to monitor the determined quality metric against a threshold, and when the quality metric fails to meet the threshold, reprocess the decompressed data stream using modified enhancement parameters
(Claim 12)
The system of claim 1, wherein the computing device is further caused to iteratively reprocess the decompressed data stream with different neural network models if the estimated quality falls below a predetermined threshold.
(Claim 13)
The system of claim 1, wherein enhancing the decompressed data stream comprises applying at least one of upsampling, interpolation, pattern recognition, or feature extraction.
(Claim 13)
The system of claim 1, wherein the neural network model is a neural upsampler configured to increase the resolution or quality of the decompressed data stream.
(Claim 14)
The system of claim 1, wherein the instructions further cause the system to detect characteristics of the compression applied to the received data stream and adjust enhancement parameters based on the detected compression characteristics.
(Claim 14)
The system of claim 1, wherein the computing device is further caused to detect the level of compression in the received compressed data stream and adjust the enhancement process accordingly.
(Claim 15)
The system of claim 1, wherein the system processes streaming data in real-time while maintaining latency below a predetermined threshold.
(Claim 16) .
A method for enhancing compressed data, comprising the steps of: receiving a compressed data stream; decompressing the compressed data stream to produce a decompressed data stream; analyzing characteristics of the decompressed data stream; enhancing the decompressed data stream using the selected enhancement technique to produce an enhanced data stream, wherein the enhancement technique comprises at least one of neural network processing, data transformation, or stream conditioning; evaluating quality of the enhanced data stream; and outputting the enhanced data stream.
(Claim 16)
A method for decoding and enhancing lossy compressed data streams, comprising the steps of: receiving a compressed data stream; decompressing the compressed data stream to produce a decompressed data stream; enhancing the decompressed data stream using at least one neural network model to produce an enhanced data stream; estimating the quality of the enhanced data stream; and output the enhanced data stream; wherein the at least one neural network model comprises an adaptive neural upsampler configured to recover information lost in compression by leveraging correlations between subsets of the compressed data stream.
(Claim 17)
The method of claim 16, wherein decompressing the compressed data stream comprises at least one of Huffman decoding, dyadic distribution decoding, or prefix-based decoding.
(Claim 17)
The method of claim 16, wherein decompressing the compressed data stream comprises using a Huffman decoder.
(Claim 18)
. The method of claim 16, wherein analyzing characteristics comprises determining at least one of data type, data complexity, noise levels, frequency content, edge density, or compression ratio.
(Claim 18)
The method of claim 16, further comprising the step of analyzing characteristics of the decompressed data stream.
(Claim 19)
The method of claim 16, further comprising analyzing the characteristics of the compressed or decompressed data stream and selecting an enhancement technique from a plurality of available enhancement techniques based on the analyzed characteristics.
(Claim 19)
The method of claim 18, further comprising the step of selecting an appropriate neural network model from a plurality of neural network models based on the analyzed characteristics.
(Claim 20)
. The method of claim 16, wherein enhancing the decompressed data stream comprises at least one of recovering information, improving resolution, reducing noise, interpolating missing values, synthesizing details, or correcting compression artifacts.
(Claim 20)
The method of claim 16, wherein enhancing the decompressed data stream comprises recovering information lost during lossy compression.
(Claim 21)
The method of claim 16, further comprises monitoring the quality of the enhanced data stream and dynamically adjusting enhancement parameters based on the monitored quality.
(Claim 21)
The method of claim 16, further comprising the step of adjusting the neural network model based on the estimated quality of the enhanced data stream.
(Claim 22)
. The method of claim 16, further comprises the step of implementing at least one of hash verification, digital signature verification, encrypted data processing, secure enclaves, input validation, secure communication channels or audit logging.
(Claim 22)
The method of claim 16, further comprising the step of implementing security measures to ensure data integrity throughout the enhancement process.
(Claim 23)
The method of claim 16, wherein the compressed data stream comprises at least one of financial time-series data, image data, audio data, video data, sensor data, genetic information or machine-readable instructions.
(Claim 23)
The method of claim 16, wherein the compressed data stream comprises financial time-series data.
(Claim 24)
The method of claim 16, further comprises training the enhancement technique using paired training data comprising original uncompressed data and corresponding compressed versions of the original uncompressed data.
(Claim 24)
. The method of claim 16, wherein the neural network model is trained using pairs of original uncompressed data and corresponding lossy compressed data.
(Claim 25)
The method of claim 16, further comprising monitoring performance of the enhancement process, collecting feedback data on enhancement quality, and adjusting enhancement parameters based on the collected feedback data
(Claim 25)
The method of claim 16, further comprising the step of performing online learning to adapt the neural network model based on recent data streams.
(Claim 26)
The method of claim 16, wherein evaluating quality comprises at least one of comparing against predicted original data, measuring statistical properties, calculating distortion metrics, evaluating perceptual quality, measuring signal-to-noise ratio, or calculating structural similarity metrics.
(Claim 26)
The method of claim 16, wherein estimating the quality of the enhanced data stream comprises comparing the enhanced data stream to a predicted original data stream.
(Claim 27) .
The method of claim 16, wherein enhancing the decompressed data stream comprises at least one of upsampling, interpolation, pattern recognition, feature extraction, detail synthesis, or artifact removal.
(Claim 27)
The method of claim 16, wherein the neural network model is a neural upsampler configured to increase the resolution or quality of the decompressed data stream.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dameon Levi can be reached at 571-272-2105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN B JEANGLAUDE/Primary Examiner, Art Unit 2845
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