DETAILED ACTION
Applicant’s response, filed 4/8/2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 Status
Claims 1-20 are currently pending and examined on the merits.
Claims 1-20 are rejected.
Priority
The instant application claims priority to U.S. Provisional Application 63/193,788 filed on 27 May 2021. At this point in examination, the effective filing date of claims 1-20 is 27 May 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3 April 2023 is in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Claim Rejections - 35 USC § 112
The previous rejections to claims 1, 14, and 20 under 35 U.S.C. 112(b) are withdrawn in view of the claim amendments.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-13 are directed to a method (process) for identifying one or more segments of a target dataset that match segments of other datasets in a database. Claims 14-19 are directed to a system (machine). Claim 20 is directed to a non-transitory computer-readable storage medium (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-4, 6, 8-9, and 14-20 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claims 1, 14, and 20 further recite:
encoding the target dataset to generate a pair of encoded target bitmap sequences based on an encoding scheme, wherein the target dataset comprises a pair of data value sequences, the encoding scheme defines encoding values based on homogeneity between the pair of data value sequences, and the pair of encoded target bitmap sequences comprises a first encoded target bitmap sequence that encodes a first type of homogeneous locations and a second encoded target bitmap sequence that encodes a second type of homogeneous locations (i.e., mental processes);
comparing the pair of sparse encoded target bitmap sequences with sparse encoded bitmap sequences of other datasets as a pre-scan to eliminate, from further comparison, candidate datasets that include at least one homogeneous mismatched location indicated by the pre-scan (i.e., mental processes);
for each candidate dataset not eliminated by the pre-scan, comparing the pair of encoded target bitmap sequences with a corresponding pair of encoded bitmap sequences of the candidate dataset to identify homogeneous mismatched locations, wherein a homogeneous mismatched location is a location where the target dataset and the candidate dataset are both homogeneous but have different types of homogeneity at the location (i.e., mental processes);
wherein identifying the homogeneous mismatched locations comprises, for each of a plurality of windows, performing a bitwise AND operation between the first encoded target bitmap sequence and a second encoded bitmap sequence of the candidate dataset (i.e., mental processes, mathematical concepts);
determining that the window includes a homogeneous mismatched location when the bitwise AND operation produces a nonzero result for the window (i.e., mental processes);
identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified, wherein the matched segment is contained between two homogeneous mismatched locations (i.e., mental processes).
Dependent claim 2 further recites:
wherein generating, from the pair of encoded target bitmap sequences, the pair of sparse encoded target bitmap sequences comprises: sampling the pair of encoded target bitmap sequences to generate a pair of sparse target bitmap sequences (i.e., mental processes);
wherein generating, from the pair of encoded target bitmap sequences, the pair of sparse encoded target bitmap sequences comprises: comparing the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences (i.e., mental processes).
Dependent claims 3 and 16 further recite:
wherein identifying the matched segment between the target dataset and one of the other datasets based on homogeneous mismatched locations identified comprises: using the comparison between the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences as a pre-scan to eliminate mismatches (i.e., mental processes);
wherein identifying the matched segment between the target dataset and one of the other datasets based on homogeneous mismatched locations identified comprises: comparing, responsive to one of the other datasets passing the pre-scan, the target dataset and said one of the other datasets to identify the matched segment (i.e., mental processes).
Dependent claims 4 and 17 further recite:
wherein comparing the pair of encoded target bitmap sequences with one of the other pairs of encoded bitmap sequences to identify the homogeneous mismatched locations further comprises: identifying a seed range of match between the target dataset and another dataset corresponding to said one of the other pairs of encoded bitmap sequences (i.e., mental processes);
wherein comparing the pair of encoded target bitmap sequences with one of the other pairs of encoded bitmap sequences to identify the homogeneous mismatched locations further comprises: comparing the pair of sparse target bitmap sequences with one of the other pairs of sparse bitmap sequences upstream and downstream of the seed range to identify the homogeneous mismatched locations (i.e., mental processes).
Dependent claim 6 further recites:
wherein identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified comprises: comparing the pair of encoded target bitmap sequences and a pair of encoded bitmap sequences corresponding to said one of the other datasets location-by-location to identify the homogeneous mismatched locations (i.e., mental processes);
wherein identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified comprises: identifying a candidate segment that is between two homogeneous mismatched locations (i.e., mental processes);
wherein identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified comprises: determining a length of the candidate segment (i.e., mental processes);
wherein identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified comprises: determining, responsive to the length being larger than a threshold, that the candidate segment is a matched segment (i.e., mental processes).
Dependent claims 8 and 18 further recites:
wherein comparing the pair of encoded target bitmap sequences with another pair of encoded bitmap sequences to identify homogeneous mismatched locations comprise: comparing the first encoded target bitmap sequence that encodes the first type of homogeneous locations of the target dataset to a second encoded bitmap sequence of said another pair, the second encoded bitmap sequence encoding the second type of homogeneous locations of another dataset (i.e., mental processes);
wherein comparing the pair of encoded target bitmap sequences with another pair of encoded bitmap sequences to identify homogeneous mismatched locations comprise: identifying a common location that indicates the target dataset and the other dataset in comparison are both homogeneous (i.e., mental processes).
Dependent claims 9 and 19 further recites:
wherein comparing the first encoded target bitmap sequence that encodes the first type of homogeneous locations of the target dataset to the second encoded bitmap sequence of said another pair comprising running both the first encoded target bitmap sequence of the target dataset and the second encoded bitmap sequence of said another pair through a bitwise AND operation (i.e., mental processes, mathematical concepts).
Dependent claim 15 further recites:
wherein comparing the pair of sparse encoded target bitmap sequences with sparse encoded bitmap sequences of other datasets as a pre-scan comprises: sampling the pair of encoded target bitmap sequences to generate a pair of sparse target bitmap sequences (i.e., mental processes);
wherein comparing the pair of sparse encoded target bitmap sequences with sparse encoded bitmap sequences of other datasets as a pre-scan comprises: comparing the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 5, 7, and 10-13 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1-4, 6, 8-9, and 14-20 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 1, 14, and 20 recite the additional non-abstract elements of data gathering:
generating, from the pair of encoded target bitmap sequences, a pair of sparse encoded target bitmap sequences that each preserve homozygosity values from selected positions of a corresponding one of the pair of encoded target bitmap sequences (claims 1, 14, and 20).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 14 and 20 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a system comprising: a computing device comprising one or more processors and memory configured to store instructions (claim 14);
a graphical user interface configured to present result related to the identified matched segment to a user (claim 14);
a non-transitory computer-readable medium configured to store instructions (claim 20).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
All limitations in claims 1-20 have been considered as a whole, and are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 14, and 20 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1, 14, and 20: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 14 and 20: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-20 are patent ineligible under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments, see pages 1-2, filed 4/8/2026 have been fully considered but they are not persuasive.
Applicant asserts that claim 1 recites a computer-implemented sequence-processing technique that integrates any alleged abstract idea into a practical application by improving the manner in which the computer performs large-scale segment matching across a database (pg. 13, para. 6 of Applicant’s Remarks). This argument is not persuasive as claim 1 does not recite any additional elements that can integrate the abstract ideas into a practical application.
With respect to the Applicant’s arguments that the recited pre-scan is a technical solution that improves throughput and resource usage in database-wide scanning, and that compact bitmap encodings and bitwise operations improves computer performance in segment scanning and database search (pg. 14, para. 1-2 of Applicant’s Remarks), this argument is not persuasive. Performing a pre-scan by comparing pairs of bitmap sequences and using bitwise AND operations for mismatch detection as recited in claim 1 were identified as mental processes and/or mathematical concepts and do not contain additional elements that can integrate the claim into a practical application. See MPEP 2106.04(II).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-6 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ball et al. (Discovering genetic matches across a massive, expanding genetic database, 31 March 2016, AncestryDNA Matching White Paper, ancestryDNA, 1-46, https://www.ancestry.com/cs/dna-help/matches/whitepaper), in view of Layer et al. (Nature Methods, 2015, 13(1), 63-65) and Naseri et al. (Genome Biology, 2019, 20(143), 1-15).
With respect to claims 1, 14, and 20:
Regarding the recited identifying one or more segments of a target dataset that match segments of other datasets in a database in claim 1, Ball et al. discloses “The first goal of DNA matching is to accurately identify the DNA segments on the 22 chromosome pairs that are identical-by-descent between pairs of individuals. Importantly, we would like to identify these IBD segments for every pair of customers in our database.” (Pages 5-6, paragraph 2, lines 1-4). DNA segments are identical-by-descent if they are matching segments shared between two or more individuals. Therefore, this suggests that more than one IBD segment is identified in pairs of individuals, which indicates a target dataset and other datasets in a database.
Regarding the recited for each candidate dataset not eliminated by the pre-scan, comparing the pair of encoded target bitmap sequences with a corresponding pair of encoded bitmap sequences of the candidate dataset to identify homogeneous mismatched locations, wherein a homogeneous mismatched location is a location where the target dataset and the candidate dataset are both homogeneous but have different types of homogeneity at the location, Ball et al. discloses “For each pair of individuals, identify windows in which the alleles at all SNPs in one of the individual’s two phased haplotypes are identical to all the alleles at the same positions in one of the other individual’s phased haplotypes. We call these “seed matches” (see Figure 3.1, section D).” (Pages 16-17, 3.1. Matching Algorithm, step 2, lines 1-3). Also, further discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). The windows of two phased haplotypes in one individual compared to a window of two phased haplotypes in another individual suggests a comparison of a pair of encoded target bitmap sequences with other pairs of encoded bitmap sequences as this is done in each pair of individuals. The seed match resulting from this comparison is used to identify homogeneous mismatched locations, where the pairs of genotypes at this location are both homogeneous and have the same alleles, but have different types of homogeneity as described in the example.
Regarding the recited identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified, wherein the matched segment is contained between two homogeneous mismatched locations, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). Also, further discloses “If the segment is longer than 6 cM, we store that segment as a match in the database.” (Page 17, 3.1. Matching Algorithm, step 5, lines 1-2). The extended seed match indicates the matched segment identified between the target dataset and one of the other datasets. This is based on homogeneous mismatched locations because the extension is cut off when a homozygous mismatch is detected.
Ball et al. does not disclose encoding the target dataset to generate a pair of encoded target bitmap sequences based on an encoding scheme, wherein the target dataset comprises a pair of data value sequences, the encoding scheme defines encoding values based on homogeneity between the pair of data value sequences, and the pair of encoded target bitmap sequences comprises a first encoded target bitmap sequence that encodes a first type of homogeneous locations and a second encoded target bitmap sequence that encodes a second type of homogeneous locations.
However, Layer et al. discloses “For VCF, which encodes diploid genotypes as 0/0 for homozygotes of the reference allele, 0/1 for heterozygotes, 1/1 for homozygotes of the alternate allele and ./. for unknown genotypes (Supplementary Fig. 2a), comparing the genotypes of two or more individuals requires iterative tests of each genotype for each individual.” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 1, lines 8-13). This demonstrates an encoding scheme where diploid genotypes are the target datasets comprising a pair of data values encoded to generate a pair of bitmap sequences. The scheme defines encoding values based on homogeneity between the pair of data value sequences, where 0/0 for homozygotes of the reference allele represents the first type of homogeneous locations and 1/1 for homozygotes of the alternate allele represents the second type of homogeneous locations.
Regarding the recited wherein identifying the homogeneous mismatched locations comprises, for each of a plurality of windows, performing a bitwise AND operation between the first encoded target bitmap sequence and a second encoded bitmap sequence of the candidate dataset and determining that the window includes a homogeneous mismatched location when the bitwise AND operation produces a nonzero result for the window, Ball et al. discloses “For each pair of individuals, identify windows in which the alleles at all SNPs in one of the individual’s two phased haplotypes are identical to all the alleles at the same positions in one of the other individual’s phased haplotypes. We call these “seed matches” (see Figure 3.1, section D).” (Pages 16-17, 3.1. Matching Algorithm, step 2, lines 1-3). Also, further discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). The windows of two phased haplotypes in one individual compared to a window of two phased haplotypes in another individual suggests a comparison of a pair of encoded target bitmap sequences with other pairs of encoded bitmap sequences as this is done in each pair of individuals. The seed match resulting from this comparison is used to identify homogeneous mismatched locations, where the pairs of genotypes at this location are both homogeneous and have the same alleles, but have different types of homogeneity as described in the example.
Ball et al. does not disclose performing a bitwise AND operation between bitmap sequences and producing a nonzero result.
However, Layer et al. discloses “For VCF, which encodes diploid genotypes as 0/0 for homozygotes of the reference allele, 0/1 for heterozygotes, 1/1 for homozygotes of the alternate allele and ./. for unknown genotypes (Supplementary Fig. 2a), comparing the genotypes of two or more individuals requires iterative tests of each genotype for each individual.” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 1, lines 8-13). This demonstrates an encoding scheme where diploid genotypes are the target datasets comprising a pair of data values encoded to generate a pair of bitmap sequences. The scheme defines encoding values based on homogeneity between the pair of data value sequences, where 0/0 for homozygotes of the reference allele represents the first type of homogeneous locations and 1/1 for homozygotes of the alternate allele represents the second type of homogeneous locations. Also, further discloses “For the 24 genotypes given here (3 individuals, 8 genotypes each), the ASCII-base algorithm executes the “if” statement 24 times, while the bit-wise algorithm executes the logical AND (“&”) only three times, with both algorithms producing equivalent results.” (Supplementary Figure 3b, lines 6-8). This teaches performing a bitwise AND operation between bitmap sequences and producing a nonzero result for the comparisons.
Ball et al. and Layer et al. do not disclose generating, from the pair of encoded target bitmap sequences, a pair of sparse encoded target bitmap sequences that each preserve homozygosity values from selected positions of a corresponding one of the pair of encoded target bitmap sequences.
However, Naseri et al. discloses dividing haplotype sequences into windows of equal sizes by using a projection function that translates a long bitstring vector representing the alleles into a scaled-down lower-resolution sequence (pg. 9, col. 2, para. 4). This teaches generating sparse encoded bitmap sequences that preserves values from selected positions.
Ball et al. and Layer et al. do not disclose comparing the pair of sparse encoded target bitmap sequences with sparse encoded bitmap sequences of other datasets as a pre-scan to eliminate, from further comparison, candidate datasets that include at least one homogeneous mismatched location indicated by the pre-scan.
However, Naseri et al. discloses running PBWT through projected panels of sequences to collect matches, and matches with less than the set number of successes after running for a set number of times were discarded (pg. 3, Fig. 1). This teaches comparing sparse representations of sequences and eliminating sets of sequences that fail to contain greater than a set number of successes for exact matches.
It would have been prima facie obvious to one of ordinary skill in the art to modify the matching segments method disclosed Ball et al. to incorporate the bitwise AND operation disclosed by Layer et al. and the pre-scan disclosed by Naseri et al. One would be motivated to incorporate the bitwise AND operation and the pre-scan into the matching segments method because Layer et al. discloses that bitmaps allow for fast and efficient comparisons of many genotypes in a single operation by means of bitwise logical operations (Online Methods, Section “Overview of the GQT genotype-indexing strategy”, paragraph 1, lines 8-11). This means the matching segments method would be fast and efficient when incorporating the bitwise AND operation. The method disclosed by Naseri et al. can detect IBD segments across a wide-length spectrum very efficiently while achieving superior accuracy and power compared to existing methods (pg. 9, col. 1-2, para. 5, lines 1-6). This also suggests that the matching segments method will be highly accurate and efficient when incorporating the pre-scan. There is a likelihood of success, since the teachings are about identifying variants in sequence comparisons, which are well known techniques in the field of computational genomics.
With respect to claim 2:
Ball et al. and Layer et al. do not disclose wherein generating, from the pair of encoded target bitmap sequences, the pair of sparse encoded target bitmap sequences.
However, Naseri et al. discloses dividing haplotype sequences into windows of equal sizes by using a projection function that translates a long bitstring vector representing the alleles into a scaled-down lower-resolution sequence (pg. 9, col. 2, para. 4). This teaches generating sparse encoded bitmap sequences from encoded bitmap sequences.
Ball et al. and Layer et al. do not disclose sampling the pair of encoded target bitmap sequences to generate a pair of sparse target bitmap sequences.
However, Naseri et al. discloses generating multiple random projections of a genotype panel by selecting a random variant site per window (pg. 3, Fig. 1). This teaches sampling to generate sparse representations of sequences.
Ball et al. and Layer et al. do not disclose comparing the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences.
However, Naseri et al. discloses running PBWT through projected panels of sequences to identify and collect exact matches of subsequences (pg. 3, Fig. 1). This teaches comparing sparse representations of sequences.
With respect to claims 3 and 16:
Layer et al. and Naseri et al. do not disclose wherein identifying the matched segment between the target dataset and one of the other datasets based on homogeneous mismatched locations identified.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). Also, further discloses “If the segment is longer than 6 cM, we store that segment as a match in the database.” (Page 17, 3.1. Matching Algorithm, step 5, lines 1-2). The extended seed match indicates the matched segment identified between the target dataset and one of the other datasets. This is based on homogeneous mismatched locations because the extension is cut off when a homozygous mismatch is detected.
Layer et al. and Naseri et al. do not disclose using the comparison between the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences as a pre-scan to eliminate mismatches.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). The seed match resulting from the comparison between pairs of sparse segments is used to eliminate mismatches by extending both directions until a mismatch is detected, which is then excluded from the overall extended seed match segment.
Layer et al. and Naseri et al. do not disclose comparing, responsive to one of the other datasets passing the pre-scan, the target dataset and said one of the other datasets to identify the matched segment.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). Also, further discloses “If the segment is longer than 6 cM, we store that segment as a match in the database.” (Page 17, 3.1. Matching Algorithm, step 5, lines 1-2). This seed-and-extend method is happening at the same time as the pre-scan. While the pre-scan filters out the mismatches when they are reached, the extended seed match segment is identified as a matched segment if it is longer than 6 cM.
With respect to claims 4 and 17:
Layer et al. and Naseri et al. do not disclose wherein comparing the pair of encoded target bitmap sequences with one of the other pairs of encoded bitmap sequences to identify the homogeneous mismatched locations.
However, Ball et al. discloses “For each pair of individuals, identify windows in which the alleles at all SNPs in one of the individual’s two phased haplotypes are identical to all the alleles at the same positions in one of the other individual’s phased haplotypes. We call these “seed matches” (see Figure 3.1, section D).” (Pages 16-17, 3.1. Matching Algorithm, step 2, lines 1-3). Also, further discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). The windows of two phased haplotypes in one individual compared to a window of two phased haplotypes in another individual suggests a comparison between pairs of encoded bitmap sequences. Ball et al. indicates that comparisons are made for each pair of individuals, which encompasses comparisons between one pair of encoded target bitmap sequences and one of the other pairs of encoded bitmap sequences. The seed match resulting from this comparison is used to identify homogeneous mismatched locations, where the pairs of genotypes at this location are both homogeneous and have the same alleles, but have different types of homogeneity as described in the example.
Layer et al. and Naseri et al. do not disclose identifying a seed range of match between the target dataset and another dataset corresponding to said one of the other pairs of encoded bitmap sequences.
However, Ball et al. discloses “For each pair of individuals, identify windows in which the alleles at all SNPs in one of the individual’s two phased haplotypes are identical to all the alleles at the same positions in one of the other individual’s phased haplotypes. We call these “seed matches” (see Figure 3.1, section D).” (Pages 16-17, 3.1. Matching Algorithm, step 2, lines 1-4). This suggests that the windows between pairs of individuals are identified as a seed range of match, where the range depends on the number of alleles that are identical at the same positions.
Layer et al. and Naseri et al. do not disclose comparing the pair of sparse target bitmap sequences with one of the other pairs of sparse bitmap sequences upstream and downstream of the seed range to identify the homogeneous mismatched locations.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). The seed matches are the pairs of sparse segments extended in both directions, suggesting upstream and downstream of the seed range. Homogeneous mismatched locations are identified when a homozygous mismatch is detected, stopping the extension of the seed matches.
With respect to claim 5:
Layer et al. and Naseri et al. do not disclose wherein comparing the pair of encoded target bitmap sequences with one of the other pairs of encoded bitmap sequences upstream and downstream of the seed range stops at a threshold range.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). This seed-and-extend method indicates a stopping point when the beginning or end of the chromosome is reached or a homozygous mismatch is detected, which suggests a threshold range.
With respect to claim 6:
Layer et al. and Naseri et al. do not disclose wherein identifying a matched segment between the target dataset and one of the other datasets based on the homogeneous mismatched locations identified.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). Also, further discloses “If the segment is longer than 6 cM, we store that segment as a match in the database.” (Page 17, 3.1. Matching Algorithm, step 5, lines 1-2). The extended seed match indicates the matched segment identified between the target dataset and one of the other datasets. This is based on homogeneous mismatched locations because the extension is cut off when a homozygous mismatch is detected.
Layer et al. and Naseri et al. do not disclose comparing the pair of encoded target bitmap sequences and a pair of encoded bitmap sequences corresponding to said one of the other datasets location-by-location to identify the homogeneous mismatched locations.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). The seed match resulting from the comparison between pairs of segments is used to identify homogeneous mismatched locations by utilizing the described seed-and-extend method. The estimated IBD region defined by start and end positions suggests that the seed match was compared or extended location-by-location to find the homogeneous mismatch locations.
Layer et al. and Naseri et al. do not disclose identifying a candidate segment that is between two homogeneous mismatched locations.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected.” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-3). This suggests that the extended seed match is the candidate segment identified between two homogeneous mismatched locations because the seed match is extended on both ends until it reaches a homozygous mismatch.
Layer et al. and Naseri et al. do not disclose determining a length of the candidate segment.
However, Ball et al. discloses “Calculate the length of the candidate matching segment in terms of genetic distance, measured in centimorgans (cM).” (Page 17, 3.1. Matching Algorithm, step 4, lines 1-2). Calculating the length of the candidate matching segment in centimorgans indicates a determination of the length of the candidate segment.
Layer et al. and Naseri et al. do not disclose determining, responsive to the length being larger than a threshold, that the candidate segment is a matched segment.
However, Ball et al. discloses “If the segment is longer than 6 cM, we store that segment as a match in the database.” (Page 17, 3.1. Matching Algorithm, step 5, lines 1-2). This suggests that 6 centimorgans is the threshold length that should be exceeded for the candidate segment to be a matched segment.
With respect to claims 8 and 18:
Layer et al. and Naseri et al. do not disclose wherein comparing the pair of encoded target bitmap sequences with another pair of encoded bitmap sequences to identify homogeneous mismatched locations.
However, Ball et al. discloses “For each pair of individuals, identify windows in which the alleles at all SNPs in one of the individual’s two phased haplotypes are identical to all the alleles at the same positions in one of the other individual’s phased haplotypes. We call these “seed matches” (see Figure 3.1, section D).” (Pages 16-17, 3.1. Matching Algorithm, step 2, lines 1-3). Also, further discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). The windows of two phased haplotypes in one individual compared to a window of two phased haplotypes in another individual suggests a comparison between pairs of encoded bitmap sequences. Ball et al. indicates that comparisons are made for each pair of individuals, which encompasses comparisons between one pair of encoded target bitmap sequences and another pair of encoded bitmap sequences. The seed match resulting from this comparison is used to identify homogeneous mismatched locations, where the pairs of genotypes at this location are both homogeneous and have the same alleles, but have different types of homogeneity as described in the example.
Layer et al. and Naseri et al. do not disclose identifying a common location that indicates the target dataset and the other dataset in comparison are both homogeneous.
However, Ball et al. discloses “For each seed match, we attempt to extend the seed match in both directions along the chromosome until (a) the beginning or end of the chromosome is reached, or (b) a homozygous mismatch is detected. A homozygous mismatch is a pair of genotypes at the same SNP that are incompatible regardless of how they are phased (for example, AA and GG). The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 1-7). This suggests that the estimated IBD region from extending the seed match is the common location where the target dataset and the other dataset in comparison are both homogeneous. The location is identified between homozygous mismatch positions.
Ball et al. and Naseri et al. do not disclose comparing the first encoded target bitmap sequence that encodes the first type of homogeneous locations of the target dataset to a second encoded bitmap sequence of said another pair, the second encoded bitmap sequence encoding the second type of homogeneous locations of another dataset.
However, Layer et al. discloses “For VCF, which encodes diploid genotypes as 0/0 for homozygotes of the reference allele, 0/1 for heterozygotes, 1/1 for homozygotes of the alternate allele and ./. for unknown genotypes (Supplementary Fig. 2a), comparing the genotypes of two or more individuals requires iterative tests of each genotype for each individual.” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 1, lines 8-13). This encoding scheme defines encoding values based on homogeneity between the pair of data value sequences, where 0/0 for homozygotes of the reference allele represents the first type of homogeneous locations and 1/1 for homozygotes of the alternate allele represents the second type of homogeneous locations. These diploid genotypes are being encoded into bitmap sequences for each individual so that the first encoded target bitmap sequence of a target individual is compared to a second encoded bitmap sequence of another individual.
With respect to claims 9 and 19:
Ball et al. and Naseri et al. do not disclose wherein comparing the first encoded target bitmap sequence that encodes the first type of homogeneous locations of the target dataset to the second encoded bitmap sequence of said another pair comprising running both the first encoded target bitmap sequence of the target dataset and the second encoded bitmap sequence of said another pair through a bitwise AND operation.
However, Layer et al. discloses “For VCF, which encodes diploid genotypes as 0/0 for homozygotes of the reference allele, 0/1 for heterozygotes, 1/1 for homozygotes of the alternate allele and ./. for unknown genotypes (Supplementary Fig. 2a), comparing the genotypes of two or more individuals requires iterative tests of each genotype for each individual.” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 1, lines 8-13). This encoding scheme defines encoding values based on homogeneity between the pair of data value sequences, where 0/0 for homozygotes of the reference allele represents the first type of homogeneous locations and 1/1 for homozygotes of the alternate allele represents the second type of homogeneous locations. These diploid genotypes are being encoded into bitmap sequences for each individual so that the first encoded target bitmap sequence of a target individual is compared to a second encoded bitmap sequence of another individual. Layer et al. discloses “For the 24 genotypes given here (3 individuals, 8 genotypes each), the ASCII-base algorithm executes the “if” statement 24 times, while the bit-wise algorithm executes the logical AND (“&”) only three times, with both algorithms producing equivalent results.” (Supplementary Figure 3b, lines 6-8). This teaches running bitmap sequences through a bitwise AND operation.
With respect to claim 10:
Ball et al. and Naseri et al. do not disclose wherein the encoding scheme defines that the first encoded target bitmap sequence has a first value if the pair of data value sequences are homogeneous of the first type and has a second value otherwise, and the encoding scheme defines that the second encoded target bitmap sequence has the first value if the pair of data value sequences are homogeneous of the second type and has the second value otherwise.
However, Layer et al. discloses “A bitmap index (bitmap) is an efficient strategy for indexing attributes with discrete values that uses a separate bit array for each possible attribute value. In the case of an individual’s genotypes, a bitmap comprises four distinct bit arrays corresponding to each of the four (including ‘unknown’) possible diploid genotypes. The bits in each bit array are set to true (1) if the individual’s genotype at a given variant matches the genotype the array encodes (Supplementary Fig. 2a). Otherwise, the element is set to false (0).” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 2, lines 2-10). This bitmap index describes defining the first encoded target bitmap sequence a first value true (1) if the pair of data value sequences are homogeneous of the first type and has a second value false (0) otherwise.
With respect to claim 11:
Layer et al. and Naseri et al. do not disclose wherein the matched segment is an identity-by-descent (IBD) segment between two individuals.
However, Ball et al. discloses “The first goal of DNA matching is to accurately identify the DNA segments on the 22 chromosome pairs that are identical-by-descent between pairs of individuals.” (Page 5, paragraph 2, lines 1-2). Also, further discloses “The estimated IBD region is defined by the start and end positions of the SNPs included in the extended segment (see Figure 3.1, section D).” (Page 17, 3.1. Matching Algorithm, step 3, lines 5-7). This suggests that the matched extended segment identified after the seed-and-extend method is an IBD region, or identity-by-descent segment, between two individuals.
With respect to claim 12:
Layer et al. and Naseri et al. do not disclose wherein the target dataset corresponds to a target DNA dataset of a target individual and the other datasets correspond to other DNA datasets of other individuals.
However, Ball et al. discloses “The first goal of DNA matching is to accurately identify the DNA segments on the 22 chromosome pairs that are identical-by-descent between pairs of individuals. Importantly, we would like to identify these IBD segments for every pair of customers in our database.” (Pages 5-6, paragraph 2, lines 1-4). Also, further discloses “the second step is to identify identical DNA sequences between all pairs of individuals in the customer database.” (Page 8, 1.3. Finding matching segments, paragraph 1, lines 2-4). DNA segments that are identical-by-descent are identified in pairs of individuals, therefore one individual in the pair corresponds to a target individual with a target DNA dataset and another individual corresponds to other customers from the database with other DNA datasets.
With respect to claim 13:
Ball et al. and Naseri et al. do not disclose wherein the first type of homogeneous locations corresponds to major alleles, the second type of homogeneous locations corresponds to minor alleles, the pair of data value sequences of the target dataset corresponds to a pair of DNA sequences, and the homogeneity between the pair of data value sequences corresponds to homozygosity between the pair of DNA sequences.
However, Layer et al. does disclose “the GQT indexing strategy is fundamentally optimized for questions that involve comparisons of sample genotypes among many variant loci.” (Page 64, col. 2, paragraph 2, lines 1-3). Also, further discloses “For VCF, which encodes diploid genotypes as 0/0 for homozygotes of the reference allele, 0/1 for heterozygotes, 1/1 for homozygotes of the alternate allele and ./. for unknown genotypes (Supplementary Fig. 2a), comparing the genotypes of two or more individuals requires iterative tests of each genotype for each individual.” (Online Methods, Section “Representing sample genotypes with bitmap indices”, paragraph 1, lines 8-13). The Genotype Query Tools (GQT) indexing strategy is used to make comparisons between genotypes in various locations. Therefore, this suggests that encoding the first type of homogeneous locations as 0/0 corresponds to major alleles, and encoding the second type of homogeneous locations as 1/1 corresponds to minor alleles. The diploid genotypes are the target datasets comprising a pair of data values encoded to generate a pair of bitmap sequences. The encoding scheme defines encoding values based on homogeneity between the pair of data value sequences, which subsequently corresponds to homozygosity between the pair of DNA sequences.
Claim 14 recites a graphical user interface configured to present result related to the identified matched segment to a user. This is considered an aesthetic design change as the display is only used for showcasing information output and nothing more. See MPEP 2144.04 (I).
Claim 14 recites a system comprising a computing device comprising one or more processors and memory configured to store instructions. Claim 20 recites a non-transitory computer-readable medium configured to store instructions.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
With respect to claim 15:
Ball et al. and Layer et al. do not disclose wherein comparing the pair of sparse encoded target bitmap sequences with sparse encoded bitmap sequences of other datasets as a pre-scan.
However, Naseri et al. discloses running PBWT through projected panels of sequences to identify and collect exact matches of subsequences (pg. 3, Fig. 1). This teaches comparing sparse representations of sequences.
Ball et al. and Layer et al. do not disclose sampling the pair of encoded target bitmap sequences to generate a pair of sparse target bitmap sequences.
However, Naseri et al. discloses generating multiple random projections of a genotype panel by selecting a random variant site per window (pg. 3, Fig. 1). This teaches sampling to generate sparse representations of sequences.
Ball et al. and Layer et al. do not disclose comparing the pair of sparse target bitmap sequences to other pairs of sparse bitmap sequences.
However, Naseri et al. discloses running PBWT through projected panels of sequences to identify and collect exact matches of subsequences (pg. 3, Fig. 1). This teaches comparing sparse representations of sequences.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ball et al. (Discovering genetic matches across a massive, expanding genetic database, 31 March 2016, AncestryDNA Matching White Paper, ancestryDNA, 1-46, https://www.ancestry.com/cs/dna-help/matches/whitepaper), Layer et al. (Nature Methods, 2015, 13(1), 63-65), and Naseri et al. (Genome Biology, 2019, 20(143), 1-15) as applied to claims 1-6 and 8-20 above, in view of Seidman et al. (The American Journal of Human Genetics, 2020, 106(4), 453-466).
Ball et al., Layer et al., and Naseri et al. are applied to claims 1-6 and 8-20 above.
With respect to claim 7:
Ball et al., Layer et al., and Naseri et al. do not disclose wherein the pair of encoded target bitmap sequences are generated from unphased data of the target dataset.
However, Seidman et al. discloses “Here we outline a method, Identical by Descent via Identical by State (IDIS), that efficiently and accurately detects IBD segments and infers degrees of relatedness in both admixed and unadmixed samples. IBIS works by identifying long stretches of allele sharing between samples using unphased genotype data and leverages bit sets to store which markers each sample carries in a homozygous state.” (Page 453, col. 2, paragraph 2, lines 1-7). This suggests that unphased genotype data and bit sets are used to generate encoded target bitmap sequences.
It would have been prima facie obvious to one of ordinary skill in the art to modify the matching segments method of Ball et al., Layer et al., and Naseri et al. to incorporate the unphased data of Seidman et al. One would be motivated to make this modification because Seidman et al. discloses that their IBIS method can reliably identify segments
≥
7 centiMorgans (cM) in length and can use these segments to infer sixth degree or closer relatives at comparable accuracy rates to Refined IBD and GERMLINE, two of the most accurate IBD segment-based approaches for relatedness classification (Pages 453-454, paragraph 4, lines 12-17). There is a likelihood of success, since all teachings are methods of identifying IBD segments between individuals and are well known in the art of genomics.
Response to Arguments
Applicant’s arguments, see pg. 15-16, filed 4/8/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ball et al. (Discovering genetic matches across a massive, expanding genetic database, 31 March 2016, AncestryDNA Matching White Paper, ancestryDNA, 1-46, https://www.ancestry.com/cs/dna-help/matches/whitepaper).
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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