Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) [ 1-20 ] are rejected under 35 U.S.C. 103 as being unpatentable over [ Medard et al. (Pub No. US 20190199473), hereinafter "Medard", in view of Kerr et al. (Pub No. US 20040225940) ].
As per claim 1, Medard significantly teaches a method of decoding a plurality of received symbols (one method 20 of decoding data, received from a data sender using a noisy data channel [Medard PP 0064], receiver receives a noisy channel output block. [Medard PP 0065])
forming noise effect sequences from the noise effect symbols (the receiver first creates an ordered list of noise sequences [Medard PP 0052], rank-orders noise sequences from most likely to least likely [Medard PP 0051]);
determining a noise effect sequence guessing order according to the respective weights (creates an ordered list of noise sequences, G: An →{1, . . . , |A|n }, from most likely to least likely, breaking ties arbitrarily [Medard PP 0052], selecting, by a noise guesser according to a noise guessing order, a sequence of noise symbols [Medard PP 0010]);
forming one or more words by inverting a set of sequences of noise effect symbols, on the plurality of received symbols, according to the noise effect sequence guessing order (forms a putative channel input codeword as the received channel output block with the effect of the guessed noise block reversed. [Medard PP 0070], Y n =X n ⊕N n [Medard PP 0047]);
determining whether each of the formed one or more words is a codeword (determining whether the output of a binary function on the putative channel input codeword is true or false. [Medard PP 0071], consulting a codebook memory to determine whether the putative channel input codeword is stored inside. [Medard PP 0071]); and
terminating according to a termination condition (abandons guessing after some number of noise removal queries [Medard PP 0060], obtaining the termination condition [Medard PP 0010]).
Medard does not explicitly teach “the method comprising: further receiving, for one or more of the received symbols, up to log2(Q) bits of associated soft information, where log2(Q) is an integer; assigning, to at least one of the one or more of the received symbols, one or more noise effect symbols having a respective weight that is determined by the up to log2(Q) bits of associated soft information”
However, Kerr, in an analogous art, teaches the method comprising: further receiving, for one or more of the received symbols, up to log2(Q) bits of associated soft information, where log2(Q) is an integer (The information vector y is also referred to hereafter as a soft information vector, and its elements are referred to as soft values related to code word symbols, or received samples. [Kerr PP 0009], With reference to FIG. 8, input information includes a set of at least (m-1) log likelihood ratios for each symbol position in a received sequence of samples [Kerr PP 0177]);
assigning, to at least one of the one or more of the received symbols, one or more noise effect symbols having a respective weight that is determined by the up to log2(Q) bits of associated soft information (In a first step 1, the reliability vector is formed by the element-by-element multiplication of the decision vector, d, and the input soft values, y [Kerr PP 0077], elements rj of the reliability vector r are calculated from the log-likelihood ratios as a difference between a maximum log-likelihood ratio and a second largest log-likelihood ratio for each symbol position j. [Kerr PP 0180])
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 2, Medard significantly teaches wherein determining the noise effect sequence guessing order comprises determining a total weight of each noise effect sequence (rank-orders noise sequences from most likely to least likely [Medard PP 0051], orders the noise blocks in likelihood order, with the maximum likelihood (ML) noise block first [Medard PP 0066])
Medard does not explicitly teach “and allocating the noise effect sequences into bins according to their respective total weights.”
However, Kerr, in an analogous art, teaches and allocating the noise effect sequences into bins according to their respective total weights (forming a reliability vector from the input information … identifying (n-k) linearly independent least reliable symbols and k most reliable symbols [Kerr PP 0047], elements rj of the reliability vector r are calculated from the log-likelihood ratios as a difference between a maximum log-likelihood ratio and a second largest log-likelihood ratio for each symbol position j [Kerr PP 0180]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 3, Medard does not explicitly teach “wherein allocating the noise effect sequences into bins comprises allocating noise effect sequences having larger reliability values to bins having smaller total weights.”
However, Kerr, in an analogous art, teaches wherein allocating the noise effect sequences into bins comprises allocating noise effect sequences having larger reliability values to bins having smaller total weights (identifying (n-k) linearly independent least reliable symbols and k most reliable symbols [Kerr PP 0047], elements rj of the reliability vector r are calculated from the log-likelihood ratios as a difference between a maximum log-likelihood ratio and a second largest log-likelihood ratio for each symbol position j [Kerr PP 0180], the reliability vector is formed by the element-by-element multiplication of the decision vector, d, and the input soft values, y [Kerr PP 0077]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 4, Medard does not explicitly teach “wherein allocating a noise effect sequence into a given bin comprises solving an integer partition problem associated with the total weight of the given bin.”
However, Kerr, in an analogous art, teaches wherein allocating a noise effect sequence into a given bin comprises solving an integer partition problem associated with the total weight of the given bin (identifying (n-k) linearly independent least reliable symbols and k most reliable symbols [Kerr PP 0047], a set of (n-k) linearly-independent least reliable symbols and a set of k most reliable symbols are identified [Kerr PP 0142], select a parity equation, and determine a position of a "least-reliable" bit among bit positions involved in the parity equation [Kerr PP 0156], The above process is repeated until the (n-k) lrs locations are found [Kerr PP 0159]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 5, Medard does not explicitly teach “further comprising determining, for one or more of the noise effect symbols, one of up to Q reliability levels by discretizing a reliability value for the one or more of the noise effect symbols.”
However, Kerr, in an analogous art, teaches further comprising determining, for one or more of the noise effect symbols, one of up to Q reliability levels by discretizing a reliability value for the one or more of the noise effect symbols (The information vector y is also referred to hereafter as a soft information vector, and its elements are referred to as soft values [Kerr PP 0067], log-likelihood ratios can be calculated for each bit [Kerr PP 0029], elements rj of the reliability vector r are calculated from the log-likelihood ratios [Kerr PP 0180]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 6, Medard significantly teaches wherein the received symbols are binary symbols and each of the one or more of the received symbols has one noise effect symbol (A “binary symmetric channel” (or “BSC”) is a memoryless channel that is binary (useful signals are encoded using bits) [Medard PP 0044], The next most likely error, for a binary symmetric channel, is likely to be a single-bit error [Medard PP 0069]).
As per claim 7, Medard significantly teaches wherein the selection of noise effect symbols in a particular noise effect sequence is determined by a measure of proximity of the noise effect sequence to the received signal (the decoder rank-orders noise sequences from most likely to least likely [Medard PP 0051], G (z n,i )≤G (z n,j ) if P (N n =z n,i )≥P (N n =z n,j ) [Medard PP 0052], the data receiver consults a noise model for which a likelihood estimate for each noise block may be (or has been) computed [Medard PP 0066]).
As per claim 8, Medard significantly teaches wherein the measure of proximity comprises a Hamming weight (Decoding a block code as known in the art requires ‘guessing’ a codeword by determining the codeword in a code book having the smallest Hamming distance (i.e. number of changed bits) to the received data block. [Medard PP 0004], The next most likely error, for a binary symmetric channel, is likely to be a single-bit error. [Medard PP 0069]).
As per claim 9, Medard significantly teaches a noise guesser for iteratively guessing noise effect sequences according to a noise effect sequence guessing order determined according to the respective weights (a noise guesser 32 for iteratively guessing noise blocks [Medard PP 0082], the decoder rank-orders noise sequences from most likely to least likely [Medard PP 0051]);
a putative codeword buffer for transiently storing putative codewords formed by inverting a set of sequences of noise effect symbols, on the plurality of received symbols, according to the noise effect sequence guessing order (a putative codeword buffer 33 for transiently storing putative channel inputs [Medard PP 0083], forms a putative channel input codeword as the received channel output block with the effect of the guessed noise block reversed [Medard PP 0070]);
and a codeword validator for determining whether each of the formed one or more words is a codeword (a codeword validator 34 for validating putative channel inputs [Medard PP 0084], determine whether the block stored in the putative codeword buffer 33 is a valid codeword [Medard PP 0086]).
Medard does not explicitly teach “A system for decoding a plurality of received symbols, the system comprising: a receiver for receiving from a data channel the plurality of received symbols and further receiving, for one or more of the received symbols, up to log2(Q) bits of associated soft information, where log2(Q) is an integer; a discretization system for assigning, to at least one of the one or more of the received symbols, one or more noise effect symbols having a respective weight that is determined by the up to log2(Q) bits of associated soft information, and for forming noise effect sequences from the noise effect symbols”
However, Kerr, in an analogous art, teaches A system for decoding a plurality of received symbols, the system comprising: a receiver for receiving from a data channel the plurality of received symbols and further receiving, for one or more of the received symbols, up to log2(Q) bits of associated soft information, where log2(Q) is an integer (The information vector y is also referred to hereafter as a soft information vector, and its elements are referred to as soft values related to code word symbols, or received samples. [Kerr PP 0009], With reference to FIG. 8, input information includes a set of at least (m-1) log likelihood ratios for each symbol position in a received sequence of samples [Kerr PP 0177]);
a discretization system for assigning, to at least one of the one or more of the received symbols, one or more noise effect symbols having a respective weight that is determined by the up to log2(Q) bits of associated soft information, and for forming noise effect sequences from the noise effect symbols (In a first step 1, the reliability vector is formed by the element-by-element multiplication of the decision vector, d, and the input soft values, y [Kerr PP 0077], elements rj of the reliability vector r are calculated from the log-likelihood ratios as a difference between a maximum log-likelihood ratio and a second largest log-likelihood ratio for each symbol position j. [Kerr PP 0180]);
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 10, Medard significantly teaches wherein the receiver comprises a network interface card (The block receiver 31 may be, for example, a network interface card (NIC) [Medard PP 0081]).
As per claim 11, Medard significantly teaches wherein the putative codeword buffer comprises a primary storage or a volatile memory (The putative codeword buffer 33 may be, for example, primary storage, a volatile memory, or similar means. [Medard PP 0083]).
As per claim 12, Medard significantly teaches further comprising a codebook for use by the codeword validator to determine whether the word stored in the putative codeword buffer is a valid codeword (The device 30 may include an optional codebook 35 for use by the codeword validator 34 [Medard PP 0086], the codeword validator 34 uses the codebook 35 to determine whether the block stored in the putative codeword buffer 33 is a valid codeword. [Medard PP 0086]).
As per claim 13, Medard significantly teaches further comprising a noise outputter for outputting channel noise effect sequences, as determined by the codeword validator (The device 30 includes a noise outputter 37 for outputting channel noise blocks, as determined by the codeword validator 34 [Medard PP 0089]).
As per claim 14, Medard does not explicitly teach “wherein the noise guesser is configured to determine the noise effect sequence guessing order by determining a total weight of each noise effect sequence assigned by the discretization system and allocating the noise effect sequences into bins according to their respective total weights.”
However, Kerr, in an analogous art, teaches wherein the noise guesser is configured to determine the noise effect sequence guessing order by determining a total weight of each noise effect sequence assigned by the discretization system and allocating the noise effect sequences into bins according to their respective total weights (elements rj of the reliability vector r are calculated [Kerr PP 0180], (n-k) linearly independent least reliable symbols and k most reliable symbols are identified [Kerr PP 0079], The above process is repeated until the (n-k) lrs locations are found [Kerr PP 0159]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 15, Medard does not explicitly teach “wherein allocating the noise effect sequences into bins comprises allocating noise effect sequences having larger reliability values to bins having smaller total weights.”
However, Kerr, in an analogous art, teaches wherein allocating the noise effect sequences into bins comprises allocating noise effect sequences having larger reliability values to bins having smaller total weights (identifying (n-k) linearly independent least reliable symbols and k most reliable symbols [Kerr PP 0047], the bit position with the minimum value in the reliability vector is found and denoted as a least reliable symbol position [Kerr PP 0081], The symbol positions are thus separated into the "least reliable symbols" (lrs) that are in the "systematic" portion of the matrix, and "most reliable symbols" (mrs) in the "non-systematic" portion of the matrix. [Kerr PP 0081]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 16, Medard does not explicitly teach “wherein allocating a noise effect sequence into a given bin comprises solving an integer partition problem associated with the total weight of the given bin.”
However, Kerr, in an analogous art, teaches wherein allocating a noise effect sequence into a given bin comprises solving an integer partition problem associated with the total weight of the given bin (select a parity equation, and determine a position of a "least-reliable" bit among bit positions involved in the parity equation [Kerr PP 0156], eliminate from a set of valid parity equations all parity equations that contain said bit location [Kerr PP 0160], The above process is repeated until the (n-k) lrs locations are found [Kerr PP 0159]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 17, Medard does not explicitly teach “wherein the discretization system is configured to determine, for one or more of the noise effect symbols, one of up to Q reliability levels by discretizing a reliability value for the one or more of the noise effect symbols.”
However, Kerr, in an analogous art, teaches wherein the discretization system is configured to determine, for one or more of the noise effect symbols, one of up to Q reliability levels by discretizing a reliability value for the one or more of the noise effect symbols (The information vector y is also referred to hereafter as a soft information vector, and its elements are referred to as soft values [Kerr PP 0067], log-likelihood ratios can be calculated for each bit [Kerr PP 0029], elements rj of the reliability vector r are calculated from the log-likelihood ratios [Kerr PP 0180]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing data of the claimed invention to have modified the decoder disclosed by Medard to incorporate Kerr’s teaching of soft decision decoding, in order to reduce computational complexity and improve speed of processing (can provide a significant reduction in computational complexity and thereby improve speed of processing [Kerr PP 0032]). Applying these teachings would have been a predictable variation for someone of ordinary skill in the art to Medard’s invention.
As per claim 18, Medard significantly teaches wherein the receiver is configured to receive the received symbols as binary symbols (A “binary symmetric channel” (or “BSC”) is a memoryless channel that is binary (useful signals are encoded using bits) [Medard PP 0044], The next most likely error, for a binary symmetric channel, is likely to be a single-bit error [Medard PP 0069]).
As per claim 19, Medard significantly teaches wherein the discretization system forms noise effect symbols into a particular noise effect sequence using a measure of proximity of the noise effect sequence to the received signal (the decoder rank-orders noise sequences from most likely to least likely [Medard PP 0051], G (z n,i )≤G (z n,j ) if P (N n =z n,i )≥P (N n =z n,j ) [Medard PP 0052], the data receiver consults a noise model for which a likelihood estimate for each noise block may be (or has been) computed [Medard PP 0066]).
As per claim 20, Medard significantly teaches wherein the measure of proximity comprises a Hamming weight (Decoding a block code as known in the art requires ‘guessing’ a codeword by determining the codeword in a code book having the smallest Hamming distance (i.e. number of changed bits) to the received data block. [Medard PP 0004], The next most likely error, for a binary symmetric channel, is likely to be a single-bit error. [Medard PP 0069]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREEM FUAD ALHWAMDEH whose telephone number is (571)272-5501. The examiner can normally be reached Mon-Fri 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Albert Decady can be reached at (571) 272-3819. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAREEM FUAD ALHWAMDEH/Examiner, Art Unit 2112
/ALBERT DECADY/Supervisory Patent Examiner, Art Unit 2112