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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Number 12,206,851) in view of Esenlik et al. (USPGPUB 2023/0336784).
Regarding claims 1 and 6, Zhang et al. disclose a computer system and method (figs. 1 – 7) comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that: preprocess data to generate input data (col. 5, lines 20 – 37); perform a discrete cosine transform (DCT) operation on the input data to create a plurality of subbands (col. 14, lines 7 – 25); compress the plurality of subbands using an encoder within a multi-layer autoencoder to generate compressed data (col. 14, lines 7 to 53); decompress the compressed bitstream using a decoder within the multi-layer autoencoder to obtain reduced output data (col. 19, lines 19 – 41); and process the reduced output data through a context recovery network to recover information lost in compression, thereby generating restored output data (col. 20, line 36 to col. 21, line 11) but do not disclose a computer system and method comprising a preprocess raw data. However, Esenlik et al., in the same field of endeavor, disclose a computer system and method (figs. 1 – 11) that comprise a preprocess raw data (paragraphs 0120 – 0121). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the invention to modify Zhang et al.’s computer system and method with that of Esenlik et al. in order to improve the performance of the system and method.
Regarding claim 5, Zhang et al. disclose all the limitations discussed above except the computer system, wherein the raw data comprises IoT sensor data or hyperspectral data. However, Esenlik et al., in a related, field, disclose the computer system (figs. 1 – 4), wherein the raw data comprises IoT sensor data or hyperspectral data (paragraph 0120). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the invention to modify Zhang et al.’s computer system and method with that of Esenlik et al. in order to improve the performance of the system.
Regarding claim 7, Zhang et al. and Esenlik et al combined disclose a computer method including the method taught by Zhang wherein the multi-layer autoencoder comprises a first kernel configured with five channels and a stride value of 1 that provides output to a first plurality of residual blocks, which in turn provides output to a first attention network, followed by a second plurality of residual blocks providing output to a second kernel configured with five channels and a stride value of 2, which ultimately provides output to a second attention network (col 6, line 65 to col. 7, line 67)
Regarding claim 8, Zhang et al. and Esenlik et al combined disclose a computer method including the method taught by Zhang, wherein processing the reduced output data through the context recovery network comprises implementing three loss functions respectively associated with first low frequency, second low frequency, and high frequency groups, wherein at least one of the loss functions employs a weighting scheme optimized for data restoration (col. 20, line 36 to col. 21, line 21).
Regarding claims 4, 9, Zhang et al. and Esenlik et al combined disclose a computer system method including the system and method taught by Zhang, wherein preprocessing the raw data includes sequential application of a data normalization process followed by a noise reduction process and an outlier reduction process, with subsequent organization of input data by data type prior to compression (col. 43, lines 6 – 58).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 3 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Number 12,206,851) in view of Esenlik et al. (USPGPUB 2023/0336784) as applied to claim 1 above, and further in view of Liang et al. (US Patent Number 12,347,445).
Regarding claims 2, 3, both Zhang et al. and Esenlik et al. combined disclose all the limitations of the claims except the computer system, wherein the context recovery network comprises a first recovery stage associated with a first low frequency group, a second recovery stage associated with a second low frequency group, and a third recovery stage associated with a high frequency group (claim 2); the computer system, wherein each recovery stage implements a respective loss function optimized for data restoration (claim 3). However, Liang et al., in the same field of endeavor, disclose a computer system, wherein the context recovery network comprises a first recovery stage associated with a first low frequency group, a second recovery stage associated with a second low frequency group, and a third recovery stage associated with a high frequency group (col. 22, lines 9 – 29). Liang also discloses a the computer system, wherein each recovery stage implements a respective loss function optimized for data restoration (col. 8, line 49 - Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing of the invention to incorporate Liang et al.’s system in Zhang et al. and Esenlik et al. combined to improve the performance of the computer system.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 10 of U.S. Patent No. 12,166,507 in view of Parker et al. (USPGPUB 2003/0122942).
US Application Number 19/048,898
US Patent Number 12,166,507
(Claim 1)
A computer system comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that: preprocess raw data to generate input data; perform a discrete cosine transform (DCT) operation on the input data to create a plurality of subbands; compress the plurality of subbands using an encoder within a multi-layer autoencoder to generate compressed data; decompress the compressed bitstream using a decoder within the multi-layer autoencoder to obtain reduced output data; and process the reduced output data through a context recovery network to recover information lost in compression, thereby generating restored output data.
(Claim 1)
A system for compressing and restoring data, 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 that, when operating on the processor, cause the computing device to: preprocess raw data to generate a plurality of input data sets; compress the plurality of input data sets into a plurality of compressed data sets using an encoder within a multi-layer autoencoder; decompress the plurality of compressed data sets using a decoder located within a multi-layer autoencoder to obtain a plurality of reduced output data sets; and process the plurality of reduced output data sets through a correlation network to recover information lost in compression by leveraging correlations between the plurality of input data sets, thereby generating a plurality of restored output data sets.
(Claim 2)
The computer system of claim 1, wherein the context recovery network comprises a first recovery stage associated with a first low frequency group, a second recovery stage associated with a second low frequency group, and a third recovery stage associated with a high frequency group.
(Claim 2)
The system of claim 1, wherein the multi-level autoencoder comprises an encoder and a decoder, and the encoder comprises convolutional layers, pooling layers, and activation functions.
(Claim 3)
The computer system of claim 2, wherein each recovery stage implements a respective loss function optimized for data restoration.
(Claim 3)
The system of claim 1, wherein the correlation network comprises convolutional layers and activation functions.
(Claim 4)
The computer system of claim 1, wherein preprocessing the raw data includes sequential application of a data normalization process followed by a noise reduction process and an outlier reduction process, with subsequent organization of input data by data type prior to compression.
(Claim 4)
The system of claim 1, wherein the plurality of data sets comprises a plurality of IoT sensor data where the incoming IoT sensor data is organized by sensor type prior to preprocessing.
(Claim 5)
The computer system of claim 1, wherein the raw data comprises IoT sensor data or hyperspectral data.
(Claim 5)
The system of claim 1, wherein the plurality of data sets comprises hyperspectral data.
(Claim 6)
A method for compressing and restoring data, comprising: preprocessing raw data to generate input data; performing a discrete cosine transform (DCT) operation on the input data to create a plurality of subbands; compressing the plurality of subbands using an encoder within a multi-layer autoencoder to generate a latent space representation; decompressing the compressed bitstream using a decoder within the multi-layer autoencoder to obtain reduced output data; and processing the reduced output data through a context recovery network to recover information lost in compression, thereby generating restored output data.
(Claim 6) A method for compressing and restoring data, comprising the steps of: preprocessing raw data to generate a plurality of input data sets; compressing the plurality of input data sets into a plurality of compressed data sets using an encoder within a multi-layer autoencoder; decompressing the plurality of compressed data sets using a decoder located within a multi-layer autoencoder to obtain a plurality of reduced output data sets; and processing the plurality of reduced output data sets through a correlation network to recover information lost in compression by leveraging correlations between the plurality of input data sets, thereby generating a plurality of restored output data sets.
(Claim 7)
The method of claim 6, wherein the multi-layer autoencoder comprises a first kernel configured with five channels and a stride value of 1 that provides output to a first plurality of residual blocks, which in turn provides output to a first attention network, followed by a second plurality of residual blocks providing output to a second kernel configured with five channels and a stride value of 2, which ultimately provides output to a second attention network.
(Claim 7)
The method of claim 6, wherein the multi-level autoencoder comprises an encoder-decoder architecture with convolutional layers, pooling layers, and activation functions.
(Claim 8)
The method of claim 6, wherein processing the reduced output data through the context recovery network comprises implementing three loss functions respectively associated with first low frequency, second low frequency, and high frequency groups, wherein at least one of the loss functions employs a weighting scheme optimized for data restoration.
(Claim 8)
The method of claim 6, wherein the correlation network comprises an architecture with convolutional layers and activation functions.
(Claim 9)
The method of claim 6, wherein preprocessing the raw data includes sequential application of data normalization, noise reduction, and outlier reduction processes, followed by organization of the input data by data type.
(Claim 9)
The method of claim 6, wherein the plurality of data sets comprises a plurality of IoT sensor data where the incoming IoT sensor data is organized by sensor type prior to preprocessing.
(Claim 10)
The method of claim 6, wherein the plurality of data sets comprises hyperspectral data.
US Patent Number 12,166,507 discloses all the limitations of claims 1 and 6 except the computer system and method that perform a discrete cosine transform operation on the input data to create a plurality of subbands. However, Parker et al., in a related field, disclose a computer system and method where discrete cosine transform operation is used to create a plurality of subbands (figs. 6, 9). Therefore, it would have been obvious to one of ordinary skill in the art at the time of the filing of the invention to modify US Patent Number 12,166,507 with that of Parker et al. for the purpose of improving the system.
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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