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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/18/2025 has been entered.
Response to Amendment
Claims 1, 8 and 12 have been amended, no claims have been canceled , and no claims have been added. Claims 1-22 are pending in the application.
Response to Arguments
Claim Rejections - 35 USC § 101
Regarding the newly amended claim 1, Applicant argues that “The amended claims are directed to a specific computing architecture that achieves efficient compression and accurate reconstruction of data. The claim recites a defined pipeline that applies entropy coding to generate compressed codewords, processes those codewords through a trained encoder to generate latent-space vectors, and then learns relationships among those vectors to reconstruct the data. These operations are not mental processes or disembodied mathematics; they require specialized, computer-implemented modules operating on digital codewords. For example, amended Claim 1 recites:
"apply an entropy-coding technique to the transformed data to generate compressed
codewords; generate a compressed data stream comprising the compressed codewords; process the compressed data stream through an encoder configured to receive the compressed codewords and generate latent-space vectors therefrom."
Such operations cannot practically be performed in the human mind and cannot occur without digital hardware executing programmed machine-learning models. They therefore do not recite a judicial exception under Step 2A Prong One. August 2025 USPTO AI Guidance Recent USPTO guidance confirms that the Examiner's analysis is inconsistent with current Office policy. The August 4, 2025 Memorandum to Technology Centers 2100, 2600, and 3600 instructs that: 1. The mental-process category is limited to steps that can be performed in the human mind. Generating latent-space vectors from compressed codewords and learning inter-vector relationships cannot be performed mentally. 2. A claim that merely involves mathematics does not necessarily recite a mathematical concept. Unless equations or formulas are explicitly claimed, mathematical relationships alone do not render a claim abstract. 3. Where the claim as a whole provides a technological solution to a technological problem, the analysis must recognize the improvement as a practical application. 4. If eligibility is a close call, no § 101 rejection should be made unless ineligibility is more likely than not (the "close-call standard"). Applied here, the claimed invention performs operations that cannot be carried out mentally, recites no equations, and provides a technological solution to the technical problem of high- fidelity compression and reconstruction. Under the August 2025 guidance, the claims are patent- eligible. Step 2A, Prong Two - The Claims Are Integrated into a Practical Application Even assuming, arguendo, that the claims were viewed as involving an abstract idea, they are clearly integrated into a practical application. The claim imposes meaningful, technological limitations by requiring an ordered pipeline that: " transforms input data to approximate a dyadic target distribution optimized for compression; " generates compressed codewords via entropy coding;
" processes those codewords through a specialized encoder neural network to produce
latent-space vectors; " learns inter-vector relationships to enable reconstruction; and
" generates reconstructed data as output. These are not generic computer functions. Each stage performs a distinct transformation of data within a defined computing architecture disclosed at [0091]-[0149] (dyadic encoder, transformation-matrix generator, and latent-transformer subsystem). The ordered combination achieves reduced entropy and improved reconstruction fidelity, effects that constitute improvements to computer functionality under Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). The present claims therefore recite a concrete, domain-specific architecture that meaningfully limits any alleged abstract idea. Step 2B - The Claims Recite Significantly More Than Any Alleged Abstract Idea. The amended claim also recites an inventive concept. The combination of (1) processing already- compressed codewords through a trained encoder, (2) learning latent-vector relationships derived from compressed data, and (3) generating reconstructed data based on those learned relationships defines a non-conventional configuration. The Examiner's assertion that "encoders and autoencoders are well known for compressing input into latent representations" mischaracterizes the claim. The claimed encoder does not compress raw input; it operates on entropy-coded data to produce latent vectors for reconstruction, a materially different and non-routine purpose. Nothing in the record shows that this architecture-entropy-coding followed by latent-space reconstruction-is conventional. Under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), whether elements are "well- understood, routine, or conventional" is a factual question that must be supported by evidence. The Office has provided no such evidence here. Absent factual support, a Step 2B rejection cannot stand. The amended claims are directed to a specific improvement in computer-implemented data compression and reconstruction technology. They do not recite a judicial exception, and even if they did, they are integrated into a practical application and include an inventive concept. The ordered combination of elements, applying entropy coding to generate compressed codewords, processing those codewords through a trained encoder, learning latent-vector relationships, and reconstructing data, improves the performance of the computing system itself by reducing entropy, minimizing data loss, and enabling high-fidelity reconstruction. Under the USPTO's August 2025 Al Guidance and the Enfish, McRO, DDR Holdings, and Berkheimer decisions, the claims recite eligible subject matter. Applicant therefore respectfully requests withdrawal of the § 101 rejection. “
In response, Examiner respectfully submits that the steps of “analyze a distribution of input data; transform the input data to approximate a target distribution; apply an entropy coding technique; generate a compressed data stream; generate latent space vectors; learn relationships between the latent space vectors; and generate output based on the learned” in the context of this claim encompasses a mathematical concept and are directed to an abstract idea. The claims recite mathematical manipulation of data (compressing, encoding, generating latent vectors). The claim 1 recites additional elements of a computing device, a memory, a processor and an encoder. The computing device, memory, processor and encoder are so generic that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Further, Examiner respectfully submits that arranging abstract steps in sequence does not render a claim non-abstract. The steps are described at high level of generality. The ordering reflects a desired result, not a specific technological improvement. Therefore, the claims remain directed to an abstract idea without significantly more, and are ineligible under 35 USC § 101.
Claim Rejections - 35 USC § 103
Regarding the newly amended claim 1, Applicant argues that “Amended Claim 1 now expressly recites applying an entropy coding technique to transformed data to generate compressed codewords, generating a compressed data stream comprising those codewords, processing the compressed data stream through an encoder configured to receive the compressed codewords and generate latent space vectors therefrom, and generating reconstructed data as output based on the learned relationships. This explicit sequence of entropy coding, latent vector generation from compressed codewords, and reconstruction is absent from the cited art, alone or in combination.
Bocherer discloses distribution matching for channel transmission optimization, not data compression and reconstruction. [0139] of Bocherer states that geometric Huffman codes are used "as distribution matching algorithms." Distribution matching reshapes symbol probabilities for efficient transmission but does not reduce data size for storage or enable reconstruction. Moreover, Bocherer's so-called FEC encoder adds redundancy bits for error detection and correction. This operation is the opposite of compression and produces no latent vectors or feature representations. A forward error correction encoder increases data length rather than reducing it. The present invention, as described in [0091]-[0108], performs entropy coding within a dyadic encoder coupled with a transformation matrix generator that produces a stochastic matrix B defining the mapping between input and target distributions. These components and their functions are not disclosed or suggested by Bocherer. Galloway, in contrast, is directed to biometric identification using electrocardiogram (ECG) signals. Its encoder operates on raw physiological signals to generate latent vectors for identity classification and anomaly detection. It does not receive or process compressed codewords and it has no mechanism for reconstructing original data. The output in Galloway is a probability of identity match, not reconstructed information. As described in [0143]-[0149] and [0183] of the present specification, the claimed system uses a latent transformer encoder to generate latent vectors from entropy coded data and a neural upsampler to reconstruct the original distribution. These capabilities are not present or suggested in either reference. The Examiner's reliance on Bocherer's FEC encoder as the claimed encoder is based on an unreasonably broad interpretation. If any post-processing operation following distribution matching were considered to satisfy "process the compressed data through an encoder configured to receive the compressed codewords," nearly every digital signal system would qualify. A person of ordinary skill in the art would understand that the claim requires an encoder that is structurally and functionally configured to derive latent space vectors from compressed codewords, not a conventional error correction encoder. Similarly, treating Huffman distribution matching as "compression" conflates two fundamentally distinct operations. Huffman trees can be used for many purposes unrelated to compression, and Bocherer uses them only to adjust symbol probabilities, not to reduce data volume. The Examiner's motivation to combine the references, that a person of skill "would have been motivated to apply known compression and distribution coding techniques to obtain latent space representation from compressed inputs," is not supported by articulated reasoning or evidence. The claimed system performs compression and reconstruction; Bocherer pursues transmission reliability through redundancy, while Galloway performs pattern recognition for biometric identification. There is no suggestion in either reference that combining these systems would yield or even relate to reconstruction of compressed data. To the contrary, combining them would alter the principle of operation of both. Bocherer's encoder adds redundant bits, whereas Galloway's autoencoder removes information to extract abstract features. Integrating the two would negate their respective purposes and therefore teaches away from the claimed configuration, consistent with In re Ratti, 270 F.2d 810 (CCPA 1959). Even if such a combination were attempted, a person of ordinary skill would have had no reasonable expectation of success. The data domains, symbol sequences for channel coding versus analog ECG signals for classification, are incompatible, and their objectives are unrelated. Under KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007), obviousness cannot rest on speculative combinations without a rational basis and a reasonable expectation of achieving the claimed result. The Examiner's generalized reference to "signal processing and encoding" is insufficient. Under In re Bigio, 381 F.3d 1320 (Fed. Cir. 2004), art is analogous only if it addresses the same field of endeavor or the same problem. Bocherer deals with communication channel optimization, Galloway deals with biometric classification, and the present invention deals with data compression and reconstruction. These are distinct fields with incompatible goals. Even if isolated features of each reference were accepted, the Examiner has not identified any teaching or suggestion of the specific ordered combination recited in the claim: transforming data to a target distribution optimized for compression, applying entropy coding to produce compressed codewords, processing those codewords through an encoder to generate latent space vectors, learning relationships among those vectors, and generating reconstructed data as output. Nothing in Bocherer, Galloway, or their combination points toward this pipeline or suggests using it to achieve accurate reconstruction from compressed data. Accordingly, the cited references do not disclose all of the claimed limitations alone or in combination, the Examiner's interpretation of Bocherer's teachings is unreasonably broad, and the proposed motivation to combine lacks technical and evidentiary support. The combination would change the principle of operation of both systems and concerns non-analogous art. Because the Office has not provided a reasoned rationale or factual evidence establishing a prima facie case of obviousness, Applicant respectfully requests withdrawal of the § 103 rejection. “.
In response, Examiner relies on a new combination of references.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 3, 5-7, 12, 14, 16 and 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3 and 4 of co-pending U.S. Application 19/023275. Although the claims at issue are not identical, they are not patentably distinct from each other because they are obvious variants of each other. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The chart below shows the correspondence between the claims in the current application and the claims in the patent.
Instant Application 18/907,456
Co-pending Application 19/023275
1.A system for efficient data compression and accurate reconstruction, comprising: a computing device comprising at least a memory and a processor; a plurality of programming instructions that, when operating on the processor, cause the computing device to:
analyze a distribution of input data;
transform the input data to approximate a target distribution optimized for compression;
apply an entropy coding technique to the transformed data to generate compressed codewords; generate a compressed data stream comprising the compressed codewords;
process the compressed data through an encoder configured to receive the compressed codewords and to generate latent space vectors therefrom;
learn relationships between the latent space vectors from the compressed codewords;
and generate reconstructed data as output based on the learned relationships.
3. (Original) The system of claim 1, further comprising a dyadic distribution compression and encryption subsystem comprising a third plurality of programming instructions that, when operating on the processor, cause the computing device to:
3.analyze input data to determine its properties;
transform the input data into a dyadic distribution;
3.generate a main data stream of transformed data and a secondary data stream of transformation information; and compress the main data stream.
4. receive the compressed data as input vectors; generate latent space vectors by processing the input vectors through a variational autoencoder's encoder;
4. process the latent space vectors through a transformer to learn relationships between the vectors,
4. and decode output latent space vectors through a variational autoencoder's decoder to produce final output data.
3. The system of claim 1, wherein the target distribution is a dyadic distribution.
3. (Original) The system of claim 1, further comprising a dyadic distribution compression… transform the input data into a dyadic distribution;
5. The system of claim 1, wherein a latent transformer architecture is utilized to generate the latent space vectors.
4. (Original) The system of claim 3, further comprising a large codeword model with a latent transformer core comprising a fourth plurality of programming instructions that, when operating on the processor, cause the computing device to: receive the compressed data as input vectors; generate latent space vectors by processing the input vectors through a variational autoencoder's encoder;
6. (Original) The system of claim 1, wherein the encoder is a variational autoencoder.
4. generate latent space vectors by processing the input vectors through a variational autoencoder's encoder;
7. (Original) The system of claim 1, further comprising a Large Codeword Model configured to process the compressed data stream into a plurality of latent space vectors.
4. (Original) The system of claim 3, further comprising a large codeword model with a latent transformer core comprising… receive the compressed data as input vectors; generate latent space vectors by processing the input vectors through a variational autoencoder's encoder;
Claim 12 corresponds to claim 1, and is rejected accordingly.
Claim 14 corresponds to claim 3, and is rejected accordingly.
Claim 16 corresponds to claim 5, and is rejected accordingly.
Claim 18 corresponds to claim 7, and is rejected accordingly.
Each patent claim in the above chart contains all the limitations recited in the corresponding claim of the current application. In other words, each patent claim is either 1) narrower than or 2) substantially equivalent to the corresponding claim of the instant application. It would have been obvious to a person of ordinary skill in the data processing art at the time the invention was made to omit elements when the remaining elements perform as before. A person of ordinary skill could have arrived at the present claims by omitting the details of the patent claims. See In re Karlson (CCPA) 136 USPQ 184, decided January 16, 1963 (“Omission of element and its function in combination is obvious expedient if remaining elements perform same functions as before.”).
Regarding claim 1, ‘US19/023275 ‘ discloses the features of claim 1 of the instant application as shown above,
However ‘US19/023275 ‘ does not recite “
apply an entropy coding technique to the transformed data to generate compressed codewords; generate a compressed data stream comprising the compressed codewords;
However Zandi discloses:
apply an entropy coding technique to the transformed data to generate compressed codewords; (Zandi, column 7, line 19- In response to the output from wavelet transform block 102, the ordering/modeling block 103 produces at least one bit stream that is received by an entropy coder 104. In response to the input from ordering/modeling block 103, entropy coder 104 produces code stream 107; abstract, An entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream; column 2, line 57- The entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream.)
generate a compressed data stream comprising the compressed codewords; (Zandi, column 7, line 19- In response to the output from wavelet transform block 102, the ordering/modeling block 103 produces at least one bit stream that is received by an entropy coder 104. In response to the input from ordering/modeling block 103, entropy coder 104 produces code stream 107; abstract, An entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream; column 2, line 57- The entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of ‘US19/023275 ‘ with the teaching of Zandi to produce the compressed data stream. (Zandi, abstract) and also in order to reduce the number of bits required for storage or transmission to achieve efficient compression.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the guidance set forth in MPEP 2106 which has incorporated the 2019 PEG. Independent claim 1 recites an apparatus . Therefore, Step 1 is satisfied for claims 1-22.
The independent claim 1 recites: A system for efficient data compression and accurate reconstruction, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions that, when operating on the processor, cause the computing device to:
analyze a distribution of input data;
transform the input data to approximate a target distribution optimized for compression;
apply an entropy coding technique to the transformed data to generate compressed codewords;
generate a compressed data stream comprising the compressed codewords;
process the compressed data through an encoder configured to receive the compressed codewords and to generate latent space vectors therefrom;
learn relationships between the latent space vectors from the compressed codewords; and
generate reconstructed data as output based on the learned relationships.
Step 1 Analysis: Claim 1 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mathematical concepts that are mathematical relationships.
The above-noted limitations of analyze, transform, apply, generate, process, learn and generate as drafted, are concepts that, under their broadest reasonable interpretation, covers mathematical relationships. That is nothing in the claim precludes these steps from mathematical relationships. For example, analyze a distribution of input data; transform the input data to approximate a target distribution; apply an entropy coding technique; generate a compressed data stream; generate latent space vectors; learn relationships between the latent space vectors; and generate reconstructed data as output based on the learned relationships, in the context of this claim encompasses a mathematical concept.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because besides the abstract idea. The claim 1 recites additional elements of a computing device, a memory, a processor and an encoder.
The computing device, memory, processor and encoder are so generic that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim 1 recites additional elements of a computing device, memory, processor and an encoder.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing device, memory, processor and encoder” are simply performing a generic computer function amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Accordingly, the additional elements, taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 2,
Step 1 Analysis: Claim 2 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 2 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 2 recites “wherein the computing device is further caused to: apply entropy decoding to the output to produce a decompressed data stream; process the decompressed data stream; reconstruct the original data distribution, recovering information altered during the initial transformation; and process the reconstructed data to extract relevant information.” The above-noted limitation of claim 2, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – computing device
The computing device is recited at a high-level of generality so that it represents no more than mere instructions to apply the judicial exception on a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim only recites one additional element-computing device
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “computing device” is simply applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3,
Step 1 Analysis: Claim 3 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 3 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 3 recites “wherein the target distribution is a dyadic distribution.” The above-noted limitation of claim 3, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
There is no additional element integrate the abstract idea into a practical application. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, there is no additional element integrate the abstract idea into a practical application. The claim is not patent eligible.
Regarding claim 4,
Step 1 Analysis: Claim 4 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 4 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 4 recites “wherein the entropy coding technique is Huffman coding.” The above-noted limitation of claim 4, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
There is no additional element integrate the abstract idea into a practical application. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, there is no additional element integrate the abstract idea into a practical application. The claim is not patent eligible.
Regarding claim 5,
Step 1 Analysis: Claim 5 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 5 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 5 recites “wherein a latent transformer architecture is utilized to generate the latent space vectors.” The above-noted limitation of claim 5, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – a latent transformer architecture
The latent transformer architecture is recited at a high-level of generality so that it represents no more than mere instructions to apply the judicial exception on a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim only recites one additional element- a latent transformer architecture
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “a latent transformer architecture” is simply applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6,
Step 1 Analysis: Claim 6 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 6 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 6 recites “wherein the encoder is a variational autoencoder.” The above-noted limitation of claim 6, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – wherein the encoder is a variational autoencoder.
While the additional element of “wherein the encoder is a variational autoencoder” is more specific than a generic encoder, the recited encoder does not add a meaningful limitation beyond implanting the abstract idea of mathematical concepts. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim recites one additional element- wherein the encoder is a variational autoencoder.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “wherein the encoder is a variational autoencoder.” is simply applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 7,
Step 1 Analysis: Claim 7 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 7 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 7 recites “further comprising a Large Codeword Model configured to process the compressed data stream into a plurality of latent space vectors.” The above-noted limitation of claim 7, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – a Large Codeword Model
While the additional element of “a Large Codeword Model” adds some specificity to the process, the recited element still does not add a meaningful limitation beyond implanting the abstract idea of mathematical concepts. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim recites one additional element- a Large Codeword Model The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “a Large Codeword Model” is applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8,
Step 1 Analysis: Claim 8 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 8 is dependent on claim 2, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 8 recites “wherein the computing device is further caused to: compare the reconstructed data to the original input data; quantify any discrepancies or information loss; and provide feedback to optimize a transformation and neural reconstruction processes.” The above-noted limitation of claim 8, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements – computing device and neural reconstruction processes
The computing device is recited at a high-level of generality so that it represents no more than mere instructions to apply the judicial exception on a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
While the additional element of “neural reconstruction processes” adds some specificity to the process, the recited element still does not add a meaningful limitation beyond implanting the abstract idea of mathematical concepts. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim recites additional elements- computing device and neural reconstruction processes
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements computing device and neural reconstruction processes” are applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9,
Step 1 Analysis: Claim 9 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 9 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 9 recites “wherein the computing device is further caused to identify unusual patterns or deviations in the latent space vectors.” The above-noted limitation of claim 9, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
There is no additional element integrate the abstract idea into a practical application. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, there is no additional element integrate the abstract idea into a practical application. The claim is not patent eligible.
Regarding claim 10,
Step 1 Analysis: Claim 10 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 10 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 10 recites “wherein the system is configured to operate on streaming data in real-time.” The above-noted limitation of claim 10, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – operate on streaming data in real-time.
The “operate on streaming data in real-time“ is recited at a high-level of generality so that it represents no more than mere instructions to apply the judicial exception on a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim only recites one additional element- operate on streaming data in real-time.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “operate on streaming data in real-time.” is simply applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11,
Step 1 Analysis: Claim 11 is directed to a system, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 11 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 11 recites “wherein the computing device is further caused to generate human-readable reports based on the extracted relevant information.” The above-noted limitation of claim 11, as drafted, is a process that, under its broadest reasonable interpretation, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
In particular, the claim recites one additional element – computing device
The computing device is recited at a high-level of generality so that it represents no more than mere instructions to apply the judicial exception on a computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim only recites one additional element-computing device
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “computing device” is simply applying the abstract idea.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
The independent claim 12 recites:
A method for efficient data compression and accurate reconstruction, comprising the steps of:
analyzing a distribution of input data;
transforming the data to approximate a target distribution optimized for compression;
applying an entropy coding technique to the transformed data;
generating compressed data stream;
processing the compressed data through an encoder to generate latent space vectors;
learning relationships between the latent space vectors; and
generating output based on the learned relationships.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mathematical concepts that are mathematical relationships.
The above-noted limitations of analyzing, transforming, applying, generating, processing, learning and generating as drafted, are concepts that, under their broadest reasonable interpretation, covers mathematical relationships. That is nothing in the claim precludes these steps from mathematical relationships. For example, analyzing a distribution of input data; transforming the input data to approximate a target distribution; applying an entropy coding technique; generating compressed data stream ;generating latent space vectors; learning relationships between the latent space vectors; and generating output based on the learned relationships, in the context of this claim encompasses a mathematical concept.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships but for the recitation of generic computer components, then they fall within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because besides the abstract idea. The claim 12 recites an additional element of an encoder.
The encoder is so generic that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim 12 recites an additional elements of an encoder.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “encoder” is simply performing a generic computer function amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Accordingly, the additional elements, taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Claim 13 corresponds to claim 2, and is rejected accordingly.
Claim 14 corresponds to claim 3, and is rejected accordingly.
Claim 15 corresponds to claim 4, and is rejected accordingly.
Claim 16 corresponds to claim 5, and is rejected accordingly.
Claim 17 corresponds to claim 6, and is rejected accordingly.
Claim 18 corresponds to claim 7, and is rejected accordingly.
Claim 19 corresponds to claim 8, and is rejected accordingly.
Claim 20 corresponds to claim 9, and is rejected accordingly.
Claim 21 corresponds to claim 10, and is rejected accordingly.
Claim 22 corresponds to claim 11, and is rejected accordingly.
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 1-2, 4-5, 7, 10, 12-13, 15-16, 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zandi (US 7,167,592 B2)in view of SARKAR (US20190050640A1)
Regarding claim 1, Zandi discloses: A system for efficient data compression and accurate reconstruction, comprising: a computing device comprising at least a memory and a processor; a plurality of programming instructions that, when operating on the processor, cause the computing device to: analyze a distribution of input data; (Zandi, column 5, line 25- context model: Available information relative to the current bit to be coded that give historically learned information about the current bit. This enables conditional probability estimation for entropy coding; column 21, line 21- This model uses bits within a coding unit based on the spatial and spectral dependencies of the coefficients. The available binary values of the neighboring coefficients, and parent coefficients can be used to create contexts; Fig. 1, item 103 “Coefficient Data Ordering and Modeling”; column 7, line 19- In response to the output from wavelet transform block 102, the ordering/modeling block 103 produces at least one bit stream that is received by an entropy coder 104; column 7, lines 25-35)
transform the input data to approximate a target distribution optimized for compression; (Zandi, Fig. 1; column 7, line 16- input image data 101 is received by wavelet transform block 102. The output of wavelet transform block 102 is coupled to coefficient data ordering and modeling block 103; column 7, line 45- the wavelets of the present invention are excellent for energy compaction and compression performance; column 7, lines 25-35; column 5, line 25- context model: Available information relative to the current bit to be coded that give historically learned information about the current bit. This enables conditional probability estimation for entropy coding;)
apply an entropy coding technique to the transformed data to generate compressed codewords; (Zandi, column 7, line 19- In response to the output from wavelet transform block 102, the ordering/modeling block 103 produces at least one bit stream that is received by an entropy coder 104. In response to the input from ordering/modeling block 103, entropy coder 104 produces code stream 107; abstract, An entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream; column 2, line 57- The entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream.)
generate a compressed data stream comprising the compressed codewords;
(Zandi, column 7, line 19- In response to the output from wavelet transform block 102, the ordering/modeling block 103 produces at least one bit stream that is received by an entropy coder 104. In response to the input from ordering/modeling block 103, entropy coder 104 produces code stream 107; abstract, An entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream; column 2, line 57- The entropy coder performs entropy coding on the embedded codestream to produce the compressed data stream.)
However Zandi does not clearly disclose:
process the compressed data through an encoder configured to receive the compressed codewords and to generate latent space vectors therefrom; learn relationships between the latent space vectors from the compressed codewords; and generate reconstructed data as output based on the learned relationships.
However SARKAR discloses:
process the compressed data through an encoder configured to receive the compressed codewords and to generate latent space vectors therefrom; (SARKAR ,[0060] scaled image 212 is a lower dimensional representation of high resolution document image 202. Scaled image 212 is then processed by autoencoder 210… autoencoder 210 in a first phase processes scaled image using encoder 208(a) to generate feature map 226(d), which may be a lower dimensional representation of scaled image 212 in what is commonly referred to as the latent space.)
learn relationships between the latent space vectors from the compressed codewords; and generate reconstructed data as output based on the learned relationships. (SARKAR, [0060] Autoencoder in a second phase utilizes decoder 208(b) to map the latent space representation (i.e., feature map 226(d)) back to the higher dimensional space associated with scaled image 212 to generate reconstructed scaled image 222. [0063] Reconstruction loss block 214 is utilized during a training phase… reconstruction loss block 214 may generate a scalar output characterizing the reconstruction loss)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi with the teaching of SARKAR of reducing computational cost associated with processing high-resolution data by utilizing lower-dimensional representation, including scaled images and latent feature representations generated by an encoder, ([0007], [0026]-[0027], [0060]) to further reduce computational and storage requirements.
Claim 12 corresponds to claim 1, and is rejected accordingly.
Regarding claim 2, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Claim 2 further recites:
wherein the computing device is further caused to: apply entropy decoding to the output to produce a decompressed data stream; (Zandi, Fig. 14, item 1405; column 23, line 20- the processing logic models each bit of each coefficient with a context model and entropy decoder (processing block 1405); column 6, line 61- The encoding portion is responsible for encoding input data to create compressed data, while the decoding portion is responsible for decoding previously encoded data to produce a reconstructed version of the original input data.)
process the decompressed data stream; (Zandi, Fig. 14, item 1405 “Entropy Decode”, item 1406 “Convert Coefficients”, item 1407 “Apply Inverse Reversible Filter From Coefficients From Coarsest Level Of Decomposition”; )
reconstruct the original data distribution, (Zandi, column 6, line 64- produce a reconstructed version of the original input data; column 8, line 10- a signal with integer coefficients can be losslessly recovered; Fig. 3B Reversible Filters (Inverse)[Wingdings font/0xE0] Reconstructed Data)
recovering information altered during the initial transformation;
(Zandi, column 8, line 8- a reversible wavelet transform comprises an implementation of an exact-reconstruction system in integer arithmetic, such that a signal with integer coefficients can be losslessly recovered; column 6, line 64- produce a reconstructed version of the original input data; Fig. 14, item 1405 “Entropy Decode”, item 1406 “Convert Coefficients”, item 1407 “Apply Inverse Reversible Filter From Coefficients From Coarsest Level Of Decomposition”)
and process the reconstructed data to extract relevant information. (Zandi ,Fig. 14, item 1410 “Store/Transmit Reconstructed Data”; Figures 10-11; column 18, line 41, The analysis unit receives the coefficients generated by the filters and classifies them into decisions, e.g., rather than encoding the coefficients completely, only relevant information is extracted.)
Claim 13 corresponds to claim 2, and is rejected accordingly.
Regarding claim 4, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Claim 4 further recites:
wherein the entropy coding technique is Huffman coding. (Zandi, column 36, line 4- some or all data is coded with a M-ary entropy coder instead of a binary entropy coder. M-ary coders include Tunstall, fixed Huffman, Adaptive Huffman, etc. For example, one Huffman code could be used for head bits.)
Claim 15 corresponds to claim 4, and is rejected accordingly.
Regarding claim 5, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Zandi does not disclose:
wherein a latent transformer architecture is utilized to generate the latent space vectors.
However SARKAR discloses:
wherein a latent transformer architecture is utilized to generate the latent space vectors. (SARKAR [0060] scaled image 212 is a lower dimensional representation of high resolution document image 202. Scaled image 212 is then processed by autoencoder 210… autoencoder 210 in a first phase processes scaled image using encoder 208(a) to generate feature map 226(d), which may be a lower dimensional representation of scaled image 212 in what is commonly referred to as the latent space.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi with the teaching of SARKAR of reducing computational cost associated with processing high-resolution data by utilizing lower-dimensional representation, including scaled images and latent feature representations generated by an encoder, ([0007], [0026]-[0027], [0060]) to further reduce computational and storage requirements.
Claim 16 corresponds to claim 5, and is rejected accordingly.
Regarding claim 7, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Zandi does not clearly disclose: a Large Codeword Model configured to process the compressed data stream into a plurality of latent space vectors.
However, SARKAR discloses:
a Large Codeword Model configured to process the compressed data stream into a plurality of latent space vectors. (SARKAR [0106] wherein said autoencoder processes said lower-resolution document image to generate at latent space representation of said lower-resolution document image; [0060] scaled image 212 is a lower dimensional representation of high resolution document image 202. Scaled image 212 is then processed by autoencoder 210… autoencoder 210 in a first phase processes scaled image using encoder 208(a) to generate feature map 226(d), which may be a lower dimensional representation of scaled image 212 in what is commonly referred to as the latent space.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi with the teaching of SARKAR of reducing computational cost associated with processing high-resolution data by utilizing lower-dimensional representation, including scaled images and latent feature representations generated by an encoder, ([0007], [0026]-[0027], [0060]) to further reduce computational and storage requirements.
Claim 18 corresponds to claim 7, and is rejected accordingly.
Regarding claim 10, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Claim 10 further recites: wherein the system is configured to operate on streaming data in real-time. (Zandi, column 22, line 27, incoming data stream ;column 40, line 41- There are many possible application targets for a particular compressed codestream. It might be desirable to have a codestream that can be sent to… a fixed-rate real-time device; column 41, line 49- If the target is a fixed-rate embedded target, this application assumes that a real-time constant pixel output must be maintained while using a constrained channel. In this case, there is a certain maximum codestream data rate locally in time (minimum compression ratio).
Claim 21 corresponds to claim 10, and is rejected accordingly.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zandi (US 7,167,592 B2)in view of SARKAR (US20190050640A1) in view of Lynch (US 6,847,317 B2)
Regarding claim 3, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Zandi in view of SARKAR does not disclose:
wherein the target distribution is a dyadic distribution.
However Lynch discloses:
wherein the target distribution is a dyadic distribution. (Lynch, column 5, line 15, the DM encoder; column 3, lin3e 40- dyadic-monotonic (DM) codec framework column 6, line1 , the probability functions can be approximated; column 4, Table 1, The function delta maps each of the 2k context values to 2-m (corresponding to “the target distribution is a dyadic distribution “)where 0 < m ≤ n. The intention of the function delta is that it quantitatively captures the information about the probability of the value of the next symbol; column 6, e.g. line 13- If the codec has useful compression properties; column 4, line 20- These constraints, together with probability information, may be used in choosing mps and delta and enable a useful combination of algorithm simplicity and entropy coding efficiency in important applications; column 3, line 28- the modulus may reflect a steepness of a probability distribution curve associated with a compression algorithm utilized by the codec framework 100.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi in view of SARKAR with the teaching of Lynch to enable a useful combination of algorithm simplicity and entropy coding efficiency in important applications, (column 4, line 22) and also to utilize a minimal computational complexity given a predetermined, desired performance level, (column 2, line 59).
Claim 14 corresponds to claim 3, and is rejected accordingly.
Claims 6, 9, 11,17, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zandi (US 7,167,592 B2)in view of SARKAR (US20190050640A1) in view of Galloway (US 2018/0260706 Al)
Regarding claim 6, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Zandi in view of SARKAR does not clearly disclose:
wherein the encoder is a variational autoencoder.
However, Galloway discloses:
wherein the encoder is a variational autoencoder. (Galloway, [0054] the latent space parameterization output that is generated by an encoder comprises an output generated by a variational auto-encoder.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi in view of SARKAR with the teaching of Galloway to provide improved detection in changes of one or more conditions of a subject (Galloway, [0022]), and to potentially catch a change in condition or status of the subject before a human would, (Galloway, [0025]) and also to reduce the dimensionality of an electrocardiogram and clustering within the latent space in fewer dimensions, (Galloway, [0054]).
Claim 17 corresponds to claim 6, and is rejected accordingly.
Regarding claim 9, Zandi in view of SARKAR discloses all of the features with respect to claim 1 as outlined above. Zandi in view of SARKAR does not clearly disclose: wherein the computing device is further caused to identify unusual patterns or deviations in the latent space vectors.
However, Galloway discloses:
wherein the computing device is further caused to identify unusual patterns or deviations in the latent space vectors. (Galloway, [0025] the machine learning model may generate an output that the identity of the new electrocardiogram does not match the identity of the subject. However, by alerting the subject, medical practitioners, or others to the identity mismatch, an identity analysis system can potentially catch a change in condition or status of the subject before a human would; [0027]; [0028], e.g. The identity analysis system can then use the latent space vector representation of an electrocardiogram input to the machine learning model to determine certain characteristics of the electrocardiogram. …additional electrocardiograms received from the subject can be compared to that region to determine if there is a change in condition or status of the subject. For example, if the machine learning model generates a vector for a new electrocardiogram that is outside of the region expected for a subject, it may determine that the new electrocardiogram doesn't match the subject.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi in view of SARKAR with the teaching of Galloway to provide improved detection in changes of one or more conditions of a subject (Galloway, [0022]), and to potentially catch a change in condition or status of the subject before a human would, (Galloway, [0025]) and also to reduce the dimensionality of an electrocardiogram and clustering within the latent space in fewer dimensions, (Galloway, [0054]).
Claim 20 corresponds to claim 9, and is rejected accordingly.
Regarding claim 11, Zandi in view of SARKAR discloses all of the features with respect to claim 2 as outlined above. Zandi in view of SARKAR does not clearly disclose: wherein the computing device is further caused to generate human-readable reports based on the extracted relevant information.
However Galloway discloses:
wherein the computing device is further caused to generate human-readable reports based on the extracted relevant information. (Galloway, [0047], For example, the user interface generator 135 can provide an interface confirming the identity of the subject, providing an indication of a possible change or condition (corresponding to “the extracted relevant information”) , or otherwise indicating to the user of the electrocardiogram sensor 115 that there may be an emerging change in the user's electrocardiogram. The alert service 140 can generate an alert to a medical practitioner, family member, or other individual indicating that there may be a change in the status of the user based on the lack of matched identity (corresponding to “generate human-readable reports”) . For example, a record of the electrocardiogram can be sent to a medical practitioner or to other machine learning models to determine one or more conditions associated with any change in the electrocardiogram compared to other records of the subject. )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi in view of SARKAR with the teaching of Galloway to provide improved detection in changes of one or more conditions of a subject (Galloway, [0022]), and to potentially catch a change in condition or status of the subject before a human would, (Galloway, [0025]) and also to reduce the dimensionality of an electrocardiogram and clustering within the latent space in fewer dimensions, (Galloway, [0054]).
Claim 22 corresponds to claim 11, and is rejected accordingly.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zandi (US 7,167,592 B2)in view of SARKAR (US20190050640A1) in view of Sydorenko (6,091,773)
Regarding claim 8, Zandi in view of SARKAR discloses all of the features with respect to claim 2 as outlined above. Zandi in view of SARKAR does not clearly disclose:
wherein the computing device is further caused to: compare the reconstructed data to the original input data; quantify any discrepancies or information loss; and provide feedback to optimize transformation and neural reconstruction processes.
However Sydorenko discloses:
wherein the computing device is further caused to: compare the reconstructed data to the original input data; quantify any discrepancies or information loss; and provide feedback to optimize transformation and neural reconstruction processes. (Sydorenko, column 2, line 9- measuring the "perceptual distance" between an approximate, reconstructed representation of a sensory signal (such as an audio or video signal) and the original sensory signal. The perceptual distance in this context is a direct quantitative measure of the likelihood that a human observer can distinguish the original audio or video signal from the reconstructed approximation to the original audio or video signal…line 39- provides a mathematical framework for statistically quantifying the detectability of differences in the neural representation arising from differences in sensory input; column 3, line 59-Specifically, it is desired to couple lossy encoding 14 with a bit allocation method and apparatus 18 which intelligently minimizes perceptible distortions in a reconstructed signal when compared to an original signal; fig. 2, “Feedback For Constant Bit-Rate Mode”; column 10, line 59- When operating in the variable bit rate mode, the system operates with a constant user-specified perceptual threshold(s). When operating in the constant bit-rate mode, the computational loop additionally monitors the bit-rate at the output of the system ( after the Lossless Coder/data formatter). To achieve a constant bit-rate at the output, independent of the nature of the source audio, the computational loop jointly varies (scales) the perceptual user-specified output data rate while not exceeding the re-scaled perceptual threshold in any band. distance threshold(s) up or down to achieve a constant)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Zandi in view of SARKAR with the teaching of Sydorenko to provide computationally efficient summaries of the neurophysiologically-based transformations performed by the human brain on input sensory signals , (Sydorenko, column 4, lines 12-15) and also to minimizing the perceptual distance between the source and lossy coded image, (Sydorenko, column 12, lines 38-40)
Claim 19 corresponds to claim 8, and is rejected accordingly.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Faezeh Forouharnejad whose telephone number is (571)270-7416. The examiner can normally be reached on generally Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shah Sanjiv can be reached on (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.F. /
Examiner, Art Unit 2166
/SANJIV SHAH/ Supervisory Patent Examiner, Art Unit 2166
Prosecution Insights