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 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 20 of U.S. Patent No. 12,261,976.
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 19/088,978
US Patent Number 12,261,631
(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: compress a plurality of input data sets by: analyzing the input data sets to determine statistical properties and frequency distributions; creating one or more transformation matrices based on the determined properties, wherein the transformation matrices are configured to preserve homomorphic properties of the input data sets; transforming the input data sets using the transformation matrices to generate modified probability distributions; generating (i) main data streams comprising the transformed data and (ii) secondary data streams comprising transformation information; and compressing the main data streams while maintaining the homomorphic properties; tokenize the compressed main data streams into a plurality of sourceblocks; assign the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook; process the plurality of codewords through a machine learning core to generate a codeword response; translate the codeword response into a translated response which matches the modality of the inputs; and decompress and decrypt the translated response.
(Claim 1)
A system for deep learning using a large codeword model with homomorphically compressed dyadically encrypted 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, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: receive a plurality of inputs; preprocess the inputs to generate a plurality of input data sets; compress and encrypt the input data sets by: analyzing the input data sets to determine their properties; creating transformation matrices based on the properties of the input data; transforming the input data into modified distributions; generating main data streams of transformed data and secondary data streams of transformation information; and compressing the main data streams; tokenize the compressed main data streams into a plurality of sourceblocks; assign the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook; process the plurality of codewords through a machine learning core to generate a codeword response; translate the codeword response into a translated response which matches the modality of the inputs; decompress and decrypt the translated response; and train the machine learning core using the decompressed and decrypted response and a plurality of training data.
(Claim 2)
The system of claim 1, wherein the machine learning core is a conventional transformer-based architecture comprising: an embedding layer ;a positional encoding layer; and a series of transformer layers.
(Claim 2)
2. The system of claim 1, wherein the machine learning core is a conventional transformer-based architecture comprising: an embedding layer; a positional encoding layer; and a series of transformer layers.
(Claim 3)
. The system of claim 2, further comprising a syntactic splitting component that splits the codewords into smaller units before processing through the conventional transformer-based architecture.
(Claim 3)
The system of claim 2, further comprising a syntactic splitting component that splits the codewords into smaller units before processing through the conventional transformer-based architecture.
(Claim 4)
The system of claim 1, wherein the machine learning core is a latent transformer core comprising: a variational autoencoder with an encoder and a decoder; and a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
(Claim 4)
The system of claim 1, wherein the machine learning core is a latent transformer core comprising: a variational autoencoder with an encoder and a decoder; and a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
(Claim 5)
The system of claim 4, wherein processing the plurality of codewords through the machine learning core comprises: generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder ;learning relationships between the plurality of latent space vectors by processing them through the transformer; using the learned relationships to generate a plurality of output latent space vectors; and generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
(Claim 5)
. The system of claim 4, wherein processing the plurality of codewords through the machine learning core comprises: generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder; learning relationships between the plurality of latent space vectors by processing them through the transformer; using the learned relationships to generate a plurality of output latent space vectors; and generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
(Claim 6)
The system of claim 5, further comprising a syntactic splitting component that splits the latent space vectors into smaller units before processing through the transformer
(Claim 6)
The system of claim 5, further comprising a syntactic splitting component that splits the latent space vectors into smaller units before processing through the transformer.
(Claim 7)
The system of claim 1, wherein compressing and encrypting the input data sets further comprises: combining the compressed main data streams and the secondary data streams into output streams; and implementing security measures to protect the output streams.
(Claim 7)
. The system of claim 1, wherein compressing and encrypting the input data sets further comprises: combining the compressed main data streams and the secondary data streams into output streams; and implementing security measures to protect the output streams
(Claim 8)
The system of claim 7, wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
(Claim 8)
. The system of claim 7, wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
(Claim 9)
The system of claim 1, wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
(Claim 9)
The system of claim 1, wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
(Claim 10)
The system of claim 1, further comprising a neural upsampler that processes the codeword response to generate a reconstructed output containing more information than the translated response.
(Claim 10)
The system of claim 1, further comprising a neural upsampler that processes the codeword response to generate a reconstructed output containing more information than the translated response.
(Claim 11)
A method for deep learning using a large codeword model with homomorphically compressed dyadically encrypted data, comprising the steps of: compressing a plurality of input data sets by: analyzing the input data sets to determine statistical properties and frequency distributions; creating one or more transformation matrices based on the determined properties, wherein the transformation matrices are configured to preserve homomorphic properties of the input data sets; transforming the input data sets using the transformation matrices to generate modified probability distributions; generating (i) main data streams comprising the transformed data and (ii) secondary data streams comprising transformation information; and compressing the main data streams while maintaining the homomorphic properties; tokenizing the compressed main data streams into a plurality of sourceblocks; assigning the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook; processing the plurality of codewords through a machine learning core to generate a codeword response; translating the codeword response into a translated response which matches the modality of the inputs; and decompressing and decrypt the translated response.
(Claim 11)
11. A method for deep learning using a large codeword model with homomorphically compressed dyadically encrypted data, comprising the steps of: receiving a plurality of inputs; preprocessing the inputs to generate a plurality of input data sets; compressing and encrypting the input data sets by: analyzing the input data sets to determine their properties; creating transformation matrices based on the properties of the input data; transforming the input data into modified distributions; generating main data streams of transformed data and secondary data streams of transformation information; and compressing the main data streams; tokenizing the compressed main data streams into a plurality of sourceblocks; assigning the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook; processing the plurality of codewords through a machine learning core to generate a codeword response; translating the codeword response into a translated response which matches the modality of the inputs; decompressing and decrypting the translated response; and training the machine learning core using the decompressed and decrypted response and a plurality of training data.
(Claim 12)
The method of claim 11, wherein the machine learning core is a conventional transformer- based architecture comprising: an embedding layer; a positional encoding layer; and a series of transformer layers.
(Claim 12)
. The method of claim 11, wherein the machine learning core is a conventional transformer-based architecture comprising: an embedding layer; a positional encoding layer; and a series of transformer layers.
(Claim 13)
The method of claim 12, further comprising splitting the codewords into smaller units before processing through the conventional transformer-based architecture.
(Claim 13)
The method of claim 12, further comprising splitting the codewords into smaller units before processing through the conventional transformer-based architecture.
(Claim 14)
. The method of claim 11, wherein the machine learning core is a latent transformer core comprising: a variational autoencoder with an encoder and a decoder; and a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
(Claim 14)
The method of claim 11, wherein the machine learning core is a latent transformer core comprising: a variational autoencoder with an encoder and a decoder; and a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
(Claim 15)
. The method of claim 14, wherein processing the plurality of codewords through the machine learning core comprises: generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder; learning relationships between the plurality of latent space vectors by processing them through the transformer; using the learned relationships to generate a plurality of output latent space vectors; and generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
(Claim 15)
The method of claim 14, wherein processing the plurality of codewords through the machine learning core comprises: generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder; learning relationships between the plurality of latent space vectors by processing them through the transformer; using the learned relationships to generate a plurality of output latent space vectors; and generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
(Claim 16)
The method of claim 15, further comprising splitting the latent space vectors into smaller units before processing through the transformer.
(Claim 16)
The method of claim 15, further comprising splitting the latent space vectors into smaller units before processing through the transformer.
(Claim 17)
The method of claim 11, wherein compressing and encrypting the input data sets further comprises: combining the compressed main data streams and the secondary data streams into output streams; and implementing security measures to protect the output streams.
(Claim 17)
The method of claim 11, wherein compressing and encrypting the input data sets further comprises: combining the compressed main data streams and the secondary data streams into output streams; and implementing security measures to protect the output streams.
(Claim 18)
The method of claim 17, wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
(Claim 18)
18. The method of claim 17, wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
(Claim 19)
The method of claim 11, wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
(Claim 19)
The method of claim 11, wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
(Claim 20)
. The method of claim 11, further comprising processing the codeword response through a neural upsampler to generate a reconstructed output containing more information than the translated response.
(Claim 20)
The method of claim 11, further comprising processing the codeword response through a neural upsampler to generate a reconstructed output containing more information than the translated response.
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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