DETAILED ACTION
Notice of 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 .
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 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 04/09/2026, has been fully considered.
In view of the amendment and remarks from 04/09/2026, the objection to the claims and the rejection of the following claims are withdrawn:
claims 1-9, 11 and 17-20 under 35 USC § 112(b)
The following rejections and/or objections are either maintained or newly applied for claims 1-20. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 01/13/2026.
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
This US Application 17/832,252 (06/03/2022) claims priority from Foreign Applications KR10-2021-0072711 (06/04/2021) and KR10-2022-0048190 (04/19/2022), as reflected in the filing receipt mailed on 06/13/2022. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-20 is 06/04/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/21/2026 was considered by the examiner.
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 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to an apparatus (claims 1-9), and a method (claims 10-20), each of which falls within one of the categories of statutory subject matter. [Step 1: claims 1-20: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "checking whether an exact match of the target nucleotide sequence is present in the reference genome based on the additional index" (independent claims 1 and 10);
• "calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome" (claim 7);
• "the program searches for an entry corresponding to a next value of the hash entry in the seed table and further performs checking whether a seed of the found entry matches the target nucleotide sequence" (claim 8);
• "finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index; measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, and when finding the maximal exact match is performed, the program accelerates an initial step of finding the maximal exact match based on a second index of the additional index" (claim 9);
• "calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome" (claim 14);
• "searching for an entry corresponding to a next value of the hash entry in the seed table and checking whether a seed of the found entry matches the target nucleotide sequence" (claim 15);
• "checking whether an exact match of the target nucleotide sequence is present in the reference genome based on a first index of the additional index" (independent claim 17);
• "finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index; measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, wherein, when finding the maximal exact match is performed, an initial step of finding the maximal exact match is accelerated based on a second index of the additional index" (claim 16);
• "finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index when it is determined that the exact match of the target nucleotide sequence is not found in the reference genome based on the first index of the additional index" (independent claim 17);
• "measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome" (independent claim 17);
• "calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome" (claim 19);
• "when it is determined that the extracted seed does not match the target nucleotide sequence as a result of checking whether the extracted seed matches the target nucleotide sequence, searching for an entry corresponding to a next value of the hash entry in the seed table, and checking whether a seed of the found entry matches the target nucleotide sequence" (claim 20).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "executing calculations comprising algorithmic rules for checking whether an exact match of the target nucleotide sequence is present", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the described steps. Further support for the mathematical techniques used in the claims is provided in the specification at [0006, 0106, 0110-0112, and 0130], which discloses algorithmic operations involving mathematical methods; which indicates the use of math. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Dependent claims 5-6, 12-13 and 18 recite further steps that limit the judicial exceptions in independent claims 1 and 17 and, as such, also are directed to those abstract ideas. For example, claims 5-6, 12-13 and 18 recite further details about the values/indexes being calculated.
[Step 2A Prong One: claims 1-20: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 11-14, 16, and 23-26 recite additional elements that are not abstract ideas:
• "memory in which at least one program is recorded; and a processor for executing the program" (independent claim 1);
• "memory" (claim 1-4, 7-9 and 11);
• "program" (claims 1-4, 10-11 and 17);
• "loading an essential index for a reference genome into memory" (independent claims 1, 10 and 17);
• "loading an additional index corresponding to an amount of available memory into memory" (independent claims 1, 10 and 17);
• "reading a target nucleotide sequence for which genome sequence alignment is to be performed" (independent claims 1, 10 and 17);
• "generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found" (independent claims 1, 10 and 17);
• "generating a result indicating the degree of matching, wherein when finding the maximal exact match is performed, an initial step of finding the maximal exact match is accelerated based on a second index of the additional index" (independent claim 17).
Dependent claims 2-4 and 11 recite further details about the loading step.
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-20 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
The recited claims regarding "loading and reading" data read on data gathering activities or the type of data being gathered; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means. These limitations are mere data gathering activity because these are used as input for the subsequent mathematical operations, reading on receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321. MPEP 2106.05(a) pertains
Claims directed to "generating a result" read on transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321 MPEP 2106.05(a); which constitutes just necessary data gathering and outputting and therefore correspond to insignificant extra-solution activity. The recited limitations in these claims are interpreted to require multiple computer parts (processor/ memory), not requiring specialized hardware other than a generic computer, which does not integrate the abstract idea into a practical application. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-20: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1-20 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Claims directed to "loading and reading" data read on performing a standard computer task, which the courts have identified as a conventional computer function in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). MPEP 2106.05(d) pertains.
Claims directed to "generating a result" read on electronically outputting data on a computer which is a conventional computer function (Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) MPEP 2106.05(d)).
With respect to the instant claims, the prior art review to Aluru ("A review of hardware acceleration for computational genomics." IEEE Design & Test 31(1):19-30 (2013); cited in the 01/13/2026 PTO-892 Form) discloses that the use of loading/reading steps to process genomic alignment data and generate an alignment result is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 25 col. 1 para. 1.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-20: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 04/09/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 10 para. 3:
Aa explained by the present specification, the improvements are directly related to the use of both an essential index and an additional index corresponding to an amount of available memory. … From the above disclosure, it is apparent that embodiments of the present claims in which both an essential index and an additional index are used increase processing time by up to 2.1 times. In addition, it is apparent from FIG. 5 and the associated disclosure that the use of an additional index corresponding to an amount of available memory allows embodiments to scale to a currently available amount of memory. This dynamic scaling allows embodiments of the present claims to operate quickly and efficiently across different systems, and within the same system as the amount of available memory changes. Accordingly, the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement, thereby satisfying the requirements of MPEP 2106.0S(a) for an improvement to technology. In addition, although it is not required, the preambles of the independent claims themselves recite an improvement (acceleration), and acceleration is also recited by claims 9, 16, and 17
It is respectfully submitted that this is not persuasive because the argued improvement related to "increased processing time - acceleration" does not commensurate in scope with the claimed invention. The supporting evidence regarding the improvement shows that there are two additional indexes required to achieve the increased speeds – "[014] the additional index comprises two or more additional indexes, the program may sequentially load the additional indexes, and the order in which the additional indexes are loaded may be determined based on the effect of each of the additional indexes on genome sequence alignment performance." which demonstrates an issue because the claim only recites loading a single additional index.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(l) the claimed invention was patented, described in a printed publication, or in public use, on sale,
or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-8 and 12-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aluru ("A review of hardware acceleration for computational genomics" IEEE Design & Test 31(1):19-30 (2013)), as cited on the 01/13/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment. Claims 5-8 and 12-15 are additionally evidenced by Nelson ("Shepard: A fast exact match short read aligner." Tenth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMCODE2012). IEEE, 2012)), as cited on the 01/13/2026 Form PTO-892.
Claim 1 recites an apparatus for accelerating genome sequence alignment, comprising: memory in which at least one program is recorded; and a processor for executing the program, wherein the program performs steps. Claim 10 recites a method a metho for accelerating genome sequence alignment comprising said steps.
The prior art to Aluru discloses an apparatus and a method related to hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein an exact string matcher (ESM) based on FM-index is proposed for reads that align perfectly and an approximate string matcher (ASM) based on seed and expand strategy is proposed to handle the cases remaining (pg. 25 col. 1 para. 2).
The steps performed by the apparatus of claim 1, and method of claim 10 comprise:
loading an additional index corresponding to an amount of available memory into memory; reading a target nucleotide sequence for which genome sequence alignment is to be performed;
checking whether an exact match of the target nucleotide sequence is present in the reference genome based on the additional index; and generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found when loading the additional index into memory, the program uses available memory, an amount of which is calculated by subtracting a size of the essential index from a total amount of memory to be used for indexes for genome sequence alignment, in order to load the additional index
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein an exact string matcher (ESM) based on FM-index (i.e. essential index) is proposed for reads (i.e. nucleotide sequences) that align perfectly (pg. 25 col. 1 para. 2); wherein the FM-index combines suffix array with Burrows-Wheeler Transform (BWT) to find all exact occurrences (pg. 25 col. 1 para. 2); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform lookups, wherein the hash table is created in software (i.e. inherent loading step, processor and memory) and minimizes the memory bandwidth overhead (i.e. corresponding to an amount of available memory), decreasing the constraint on the speed (pg. 25 col. 1 para. 1); wherein reads generated from a genome are mapped to a template or reference genome with specificity of where the reads map to the reference genome(i.e. generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found) (pg. 24 Fig. 3); wherein some of the reads map to more than one location and some others do not map to any location (i.e. checking whether an exact match of the target nucleotide sequence is present in the reference genome based on the additional index) (pg. 24 Fig. 3).
Claims 5 and 12 recite:
wherein: the additional index includes a first index that is used when checking whether the exact match of the target nucleotide sequence is present in the reference genome is performed, and
the first index includes a seed table including hash entries corresponding to respective seeds having a predetermined length, the seed s being extracted from the reference genome, and a multi-location table configured to collectively map two or more locations of an identical seed in the reference genome to a single index
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the hash table lookup pipeline is broken stages as evidenced by Nelson (pg. 3 Fig. 3), wherein if the read (i.e. nucleotide sequence) matches to the reference genome, the index of occurrence in the genome and number of occurrences are recorded (i.e. includes a first index that is used when checking whether the exact match of the target nucleotide sequence is present in the reference genome); wherein the unique index is calculated and used to load the value from the hash table (i.e. hash entry) (pg. 3 Fig. 3 Nelson); wherein the value is either a new seed value or an offset from an intermediate table (i.e. seed table); wherein the index in the genome loaded from the hash table is used to load the corresponding 100 base pairs from the reference genome (i.e. includes a seed table configured with hash entries corresponding to respective seeds having a predetermined length, which are extracted from the reference genome) (pg. 3 Fig. 3 Nelson); wherein finding all the entries that need to be stored in the hash table is done by hashing each 100 base pair word in the reference genome in rounds, with duplicate entries being identified and discarded (pg. 2 col. 1 para. 4 Nelson) (i.e. reading on multilocation table with two or more locations of an identical seed in the reference genome are collectively mapped to a single index).
Claims 6 and 13 recite:
wherein: the hash entry includes information about a location of a seed in the reference genome, information about whether the hash entry has a hash collision, an index number of a next hash entry having a same hash value as the hash entry, and information about an index in the multi-location table
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein an entry in the hash table contains the index in genome (i.e. information about a location of a seed in the reference genome) (pg. 2 Fig. 2 Nelson); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein finding all the entries that need to be stored in the hash table is done by hashing each 100 base pair word in the reference genome in rounds, with duplicate entries being identified and discarded (pg. 2 col. 1 para. 4 Nelson) (i.e. information about an index in the multi-location table).
Claims 7 and 14 recites:
wherein: when checking whether the exact match of the target nucleotide sequence is present in the reference genome based on the additional index, the program performs calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table (i.e. extracting a seed from the reference genome using location information stored in the found entry) as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein reseed values and offsets values are stored in an intermediate table (i.e. seed table) (pg. 2 col. 2 para. 1 Nelson) with the intermediate table providing a minimal perfect hash entry for the given key set, and can now be used to add values to the table to complete the key-value association (i.e. the step of completing a step reads on "when the hash value is less than a number of loaded hash entries of the seed table" – being interpreted as the need of completing the addition of values due to sufficient space remaining in said table) (pg. 2 col. 2 para. 2 Nelson); wherein a minimal perfect hash is one with no collisions (i.e. hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision) and no empty slots, but requires a fixed set of keys (i.e. short reads) known in advance (pg. 2 col. 1 para. 2 Nelson); wherein in stage 5 of the hash table lookup pipeline, the reference genome is compared to the read to search for a match (pg. 3 Fig. 3 Nelson) (i.e. searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome).
Claims 8 and 15 recites:
wherein: when checking whether the extracted seed matches the target nucleotide sequence is performed, if it is determined that the extracted seed does not match the target nucleotide sequence, the program searches for an entry corresponding to a next value of the hash entry in the seed table and further performs checking whether a seed of the found entry matches the target nucleotide sequence
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); minimal perfect hash function looks up at seed value in a seed intermediate table and uses it to compute an index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3), that in turn is used to see if the short read is an actual match to the reference genome (pg. 2 col. 2 para. 3 Nelson); wherein a check for existence is needed and no unnecessary data is stored in the hash table (i.e. if it is determined that the extracted seed does not match the target nucleotide sequence, the program searches for an entry corresponding to a next value) (pg. 2 col. 2 para. 3 Nelson).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 2-4 and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Aluru as applied to claims 1 and 10 in the 102 rejection above and further in view of Wang ("Accelerating FM-index search for genomic data processing." Proceedings of the 47th International Conference on Parallel Processing (2018)), as cited on the 01/13/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment. Claims 3-4 and 11 are additionally evidenced by Nelson ("Shepard: A fast exact match short read aligner." Tenth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMCODE2012). IEEE, 2012)), as cited on the 01/13/2026 Form PTO-892.
Claim 2 recites:
wherein, when loading the additional index into memory, the program uses available memory, an amount of which is calculated by subtracting a size of the essential index from a total amount of memory to be used for indexes for genome sequence alignment, in order to load the additional index
• Aluru does not teach the recited limitation above. However, Wang teaches it as an accelerator for FM-index (i.e. essential index) search in genomic sequence alignment (pg. 2 col. 1 para. 1) using data-level parallelism to improve memory bandwidth utilization (pg. 3 col. 2 para. 2); wherein to analyze the limitation of memory bandwidth, the model considers the total 64 bytes available (i.e. a total amount of memory to be used for indexes) and measures the utilization of the available memory bandwidth supplied by a single memory channel (i.e. reading on the step of an amount of memory to be used for indexes for genome sequence alignment) (pg. 9 col. 1 para. 2) with the use of memory operations (pg. 7 Table 2) to explore memory locality (pg. 6 col. 2 para. 2).
Claim 3 recites:
wherein: when loading the additional index into memory, if the additional index comprises two or more additional indexes, the program sequentially loads the additional indexes, and an order in which the additional indexes are loaded is determined based on an effect of each of the additional indexes on genome sequence alignment performance
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys, wherein buckets containing one key are loaded first buckets with one or more keys are reseeded and then loaded as evidenced by Nelson (pg. 2 col. 1 para. 5) (i.e. an order in which the additional indexes are loaded is determined based on an effect of each of the additional indexes on genome sequence alignment performance).
Claims 4 and 11 recite:
wherein: when loading the additional index into memory, the program loads all or part of the additional index depending on whether the amount of available memory is equal to or greater than a size of the additional index to be loaded, and when only part of the additional index is loaded, the program loads a portion of the additional index that is available within the amount of available memory
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the block ram available was greater than the amount used by the described method as evidenced by Nelson (pg. 4 Table 2).
Claims 9 and 16 recite:
wherein: when the exact match of the target nucleotide sequence is not found in the reference genome based on the additional index,
• Aluru teaches hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1).
the program performs finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index; measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, and when finding the maximal exact match is performed, the program accelerates an initial step of finding the maximal exact match based on a second index of the additional index
• Aluru does not teach the recitation above. However, Fujiki teaches an accelerator for read alignment consisting of a seeding and see-extension accelerator (pg. 69 col. 1 para. 2); wherein the seeding step finds a set of positions in the reference genome (hits) where a read could find a match (pg. 70 col. 2 para. 4); wherein the algorithm uses an index table that has one entry for each k-mer, which points to a list in a position table, wherein the list contains the hits where the k-mer occurs in the reference genome and, for each position (pivot) in the read, a right maximal exact match (RMEM) is found until the intersection returns an empty set of candidate hits (pg. 77 col. 2 para. 3); wherein the degree of hits per read are measured as seeding accelerator optimizations (pg. 80 Fig. 16).
Claim 17 recites:
loading an essential index for a reference genome into memory;
loading an additional index corresponding to an amount of available memory into memory;
reading a target nucleotide sequence for which genome sequence alignment is to be performed;
checking whether an exact match of the target nucleotide sequence is present in the reference genome based on a first index of the additional index;
generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found;
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein an exact string matcher (ESM) based on FM-index (i.e. essential index) is proposed for reads (i.e. nucleotide sequences) that align perfectly (pg. 25 col. 1 para. 2); wherein the FM-index combines suffix array with Burrows-Wheeler Transform (BWT) to find all exact occurrences (pg. 25 col. 1 para. 2); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform lookups, wherein the hash table is created in software (i.e. inherent loading step, processor and memory) and minimizes the memory bandwidth overhead (i.e. corresponding to an amount of available memory), decreasing the constraint on the speed (pg. 25 col. 1 para. 1); wherein reads generated from a genome are mapped to a template or reference genome with specificity of where the reads map to the reference genome(i.e. generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found) (pg. 24 Fig. 3); wherein some of the reads map to more than one location and some others do not map to any location (i.e. checking whether an exact match of the target nucleotide sequence is present in the reference genome based on the additional index) (pg. 24 Fig. 3).
finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index when it is determined that the exact match of the target nucleotide sequence is not found in the reference genome based on the first index of the additional index;
measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, wherein when finding the maximal exact match is performed, an initial step of finding the maximal exact match is accelerated based on a second index of the additional index
• Aluru does not teach the recitation above. However, Fujiki teaches an accelerator for read alignment consisting of a seeding and see-extension accelerator (pg. 69 col. 1 para. 2); wherein the seeding step finds a set of positions in the reference genome (hits) where a read could find a match (pg. 70 col. 2 para. 4); wherein the algorithm uses an index table that has one entry for each k-mer, which points to a list in a position table, wherein the list contains the hits where the k-mer occurs in the reference genome and, for each position (pivot) in the read, a right maximal exact match (RMEM) is found until the intersection returns an empty set of candidate hits (pg. 77 col. 2 para. 3); wherein the degree of hits per read are measured as seeding accelerator optimizations (pg. 80 Fig. 16).
Claim 18 recites:
wherein: the first index includes a seed table configured with hash entries corresponding to respective seeds having a predetermined length, which are extracted from the reference genome, and a multi-location table in which two or more locations of an identical seed in the reference genome are collectively mapped to a single index, and the hash entry includes information about a location of a seed in the reference genome, information about whether the hash entry has a hash collision, an index number of a next hash entry having a same hash value as the hash entry, and information about an index in the multi-location table
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein an entry in the hash table contains the index in genome (i.e. information about a location of a seed in the reference genome) (pg. 2 Fig. 2 Nelson); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein finding all the entries that need to be stored in the hash table is done by hashing each 100 base pair word in the reference genome in rounds, with duplicate entries being identified and discarded (pg. 2 col. 1 para. 4 Nelson) (i.e. information about an index in the multi-location table).
Claim 19 recites:
wherein: checking whether the exact match of the target nucleotide sequence is present in the reference genome based on the first index includes calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table (i.e. extracting a seed from the reference genome using location information stored in the found entry) as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein reseed values and offsets values are stored in an intermediate table (i.e. seed table) (pg. 2 col. 2 para. 1 Nelson)with the intermediate table providing a minimal perfect hash entry for the given key set, and can now be used to add values to the table to complete the key-value association (i.e. the step of completing a step reads on "when the hash value is less than a number of loaded hash entries of the seed table" – being interpreted as the need of completing the addition of values due to sufficient space remaining in said table)(pg. 2 col. 2 para. 2 Nelson) ; wherein a minimal perfect hash is one with no collisions (i.e. hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision) and no empty slots, but requires a fixed set of keys (i.e. short reads) known in advance (pg. 2 col. 1 para. 2 Nelson); wherein in stage 5 of the hash table lookup pipeline, the reference genome is compared to the read to search for a match (pg. 3 Fig. 3 Nelson) (i.e. searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome).
Claim 20 recites:
further comprising: when it is determined that the extracted seed does not match the target nucleotide sequence as a result of checking whether the extracted seed matches the target nucleotide sequence, searching for an entry corresponding to a next value of the hash entry in the seed table, and checking whether a seed of the found entry matches the target nucleotide sequence
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); minimal perfect hash function looks up at seed value in a seed intermediate table and uses it to compute an index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3), that in turn is used to see if the short read is an actual match to the reference genome (pg. 2 col. 2 para. 3 Nelson); wherein a check for existence is needed and no unnecessary data is stored in the hash table (i.e. if it is determined that the extracted seed does not match the target nucleotide sequence, the program searches for an entry corresponding to a next value) (pg. 2 col. 2 para. 3 Nelson).
Rationale for combining (MPEP §2142-2143)
Regarding claims 2-4 and 11, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Aluru in view of Wang because all references disclose methods for genomic sequence alignment. The motivation would have been to maximize memory throughput during the search in genomic sequence alignment (pg. 7 col. 1 para. 4 Wang).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the genomic sequence alignment method of Aluru to the methods by Wang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for genomic sequence alignment.
B. Claims 9 and 16-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Aluru as applied to claims 1 and 10 in the 102 rejection above and further in view of Fujiki ("GenAx: A genome sequencing accelerator." 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). IEEE (2018)), as cited on the 01/13/2026 Form PTO-892. Any newly recited portions are necessitated by claim amendment. Claims 18-20 are additionally evidenced by Nelson ("Shepard: A fast exact match short read aligner." Tenth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMCODE2012). IEEE, 2012)), as cited on the 01/13/2026 Form PTO-892.
Claims 9 and 16 recite:
wherein: when the exact match of the target nucleotide sequence is not found in the reference genome based on the additional index,
• Aluru teaches hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1).
the program performs finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index; measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, and when finding the maximal exact match is performed, the program accelerates an initial step of finding the maximal exact match based on a second index of the additional index
• Aluru does not teach the recitation above. However, Fujiki teaches an accelerator for read alignment consisting of a seeding and see-extension accelerator (pg. 69 col. 1 para. 2); wherein the seeding step finds a set of positions in the reference genome (hits) where a read could find a match (pg. 70 col. 2 para. 4); wherein the algorithm uses an index table that has one entry for each k-mer, which points to a list in a position table, wherein the list contains the hits where the k-mer occurs in the reference genome and, for each position (pivot) in the read, a right maximal exact match (RMEM) is found until the intersection returns an empty set of candidate hits (pg. 77 col. 2 para. 3); wherein the degree of hits per read are measured as seeding accelerator optimizations (pg. 80 Fig. 16).
Claim 17 recites:
loading an essential index for a reference genome into memory;
loading an additional index corresponding to an amount of available memory into memory;
reading a target nucleotide sequence for which genome sequence alignment is to be performed;
checking whether an exact match of the target nucleotide sequence is present in the reference genome based on a first index of the additional index;
generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found;
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein an exact string matcher (ESM) based on FM-index (i.e. essential index) is proposed for reads (i.e. nucleotide sequences) that align perfectly (pg. 25 col. 1 para. 2); wherein the FM-index combines suffix array with Burrows-Wheeler Transform (BWT) to find all exact occurrences (pg. 25 col. 1 para. 2); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform lookups, wherein the hash table is created in software (i.e. inherent loading step, processor and memory) and minimizes the memory bandwidth overhead (i.e. corresponding to an amount of available memory), decreasing the constraint on the speed (pg. 25 col. 1 para. 1); wherein reads generated from a genome are mapped to a template or reference genome with specificity of where the reads map to the reference genome(i.e. generating a result of alignment of the target nucleotide sequence using a location of the exact match in the reference genome when the exact match is found) (pg. 24 Fig. 3); wherein some of the reads map to more than one location and some others do not map to any location (i.e. checking whether an exact match of the target nucleotide sequence is present in the reference genome based on the additional index) (pg. 24 Fig. 3).
finding a maximal exact match between the target nucleotide sequence and the reference genome based on the essential index when it is determined that the exact match of the target nucleotide sequence is not found in the reference genome based on the first index of the additional index;
measuring a degree of matching between the target nucleotide sequence and the maximal exact match found in the reference genome; and generating a result indicating the degree of matching, wherein when finding the maximal exact match is performed, an initial step of finding the maximal exact match is accelerated based on a second index of the additional index
• Aluru does not teach the recitation above. However, Fujiki teaches an accelerator for read alignment consisting of a seeding and see-extension accelerator (pg. 69 col. 1 para. 2); wherein the seeding step finds a set of positions in the reference genome (hits) where a read could find a match (pg. 70 col. 2 para. 4); wherein the algorithm uses an index table that has one entry for each k-mer, which points to a list in a position table, wherein the list contains the hits where the k-mer occurs in the reference genome and, for each position (pivot) in the read, a right maximal exact match (RMEM) is found until the intersection returns an empty set of candidate hits (pg. 77 col. 2 para. 3); wherein the degree of hits per read are measured as seeding accelerator optimizations (pg. 80 Fig. 16).
Claim 18 recites:
wherein: the first index includes a seed table configured with hash entries corresponding to respective seeds having a predetermined length, which are extracted from the reference genome, and a multi-location table in which two or more locations of an identical seed in the reference genome are collectively mapped to a single index, and the hash entry includes information about a location of a seed in the reference genome, information about whether the hash entry has a hash collision, an index number of a next hash entry having a same hash value as the hash entry, and information about an index in the multi-location table
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein an entry in the hash table contains the index in genome (i.e. information about a location of a seed in the reference genome) (pg. 2 Fig. 2 Nelson); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein finding all the entries that need to be stored in the hash table is done by hashing each 100 base pair word in the reference genome in rounds, with duplicate entries being identified and discarded (pg. 2 col. 1 para. 4 Nelson) (i.e. information about an index in the multi-location table).
Claim 19 recites:
wherein: checking whether the exact match of the target nucleotide sequence is present in the reference genome based on the first index includes calculating a hash value of the target nucleotide sequence; searching for a hash entry corresponding to the hash value when the hash value is less than a number of loaded hash entries of the seed table; when the hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision, extracting a seed from the reference genome using location information stored in the found entry; checking whether the extracted seed matches the target nucleotide sequence; and when the extracted seed is determined to match the target nucleotide sequence, searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); wherein the minimal perfect hash table requires first looking up a seed or offset value in an intermediate table (i.e. seed table), then using this seed or offset to compute the actual index into the hash table (i.e. extracting a seed from the reference genome using location information stored in the found entry) as evidenced by Nelson (pg. 2 col. 1 para. 3); wherein the method hash and displace sorts buckets containing a number of keys (short reads) that represent the number of collisions with other keys (pg. 2 col. 1 para. 5 Nelson); wherein reseed values and offsets values are stored in an intermediate table (i.e. seed table) (pg. 2 col. 2 para. 1 Nelson) with the intermediate table providing a minimal perfect hash entry for the given key set, and can now be used to add values to the table to complete the key-value association (i.e. the step of completing a step reads on "when the hash value is less than a number of loaded hash entries of the seed table" – being interpreted as the need of completing the addition of values due to sufficient space remaining in said table)(pg. 2 col. 2 para. 2 Nelson) ; wherein a minimal perfect hash is one with no collisions (i.e. hash entry corresponding to the hash value is found and when the found entry is not an entry having a hash collision) and no empty slots, but requires a fixed set of keys (i.e. short reads) known in advance (pg. 2 col. 1 para. 2 Nelson); wherein in stage 5 of the hash table lookup pipeline, the reference genome is compared to the read to search for a match (pg. 3 Fig. 3 Nelson) (i.e. searching the multi-location table for all exact matches of the target nucleotide sequence in the reference genome).
Claim 20 recites:
further comprising: when it is determined that the extracted seed does not match the target nucleotide sequence as a result of checking whether the extracted seed matches the target nucleotide sequence, searching for an entry corresponding to a next value of the hash entry in the seed table, and checking whether a seed of the found entry matches the target nucleotide sequence
• Aluru teaches as hardware accelerators for applications in sequences alignment (pg. 19 col. 2 para. 1); wherein a fast, exact matching solution uses a minimal perfect hash table (i.e. additional index) to perform reads lookups (pg. 25 col. 1 para. 1); minimal perfect hash function looks up at seed value in a seed intermediate table and uses it to compute an index into the hash table as evidenced by Nelson (pg. 2 col. 1 para. 3), that in turn is used to see if the short read is an actual match to the reference genome (pg. 2 col. 2 para. 3 Nelson); wherein a check for existence is needed and no unnecessary data is stored in the hash table (i.e. if it is determined that the extracted seed does not match the target nucleotide sequence, the program searches for an entry corresponding to a next value) (pg. 2 col. 2 para. 3 Nelson).
Rationale for combining (MPEP §2142-2143)
Regarding claims 9 and 16-20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Aluru in view of Fujiki because all references disclose methods for genomic sequence alignment. The motivation would have been to generate cache-able indexes and seeding acceleration using right maximal exact match within each genome segment (pg. 70 col. 2 para. 3 Fujiki).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the genomic sequence alignment method of Aluru to the methods by Fujiki because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for genomic sequence alignment.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 102-103
The Remarks of 04/09/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 12 para. 3:
The present rejection alleges that Aluru discloses the essential index of claim 1 as an FMindex in p. 25 para. 2, and also discloses the additional index of claim 1 as a minimal perfect hash table in p. 25 para. 1. However, these are two mutually exclusive and incompatible approaches to mapping. The FM index of para. 2 is disclosed by Arram et al., and represents an approach using a Burrows-Wheeler Transform (BWT) to find exact occurrences. The key features of Arram are the use of "specialized processors for ESM and ASM in the FPGA. If a read fails ESM stage, it is forwarded to the ASM stage." In contrast, the minimal perfect hash table appears in a paper by Nelson et al., which is reference [43]. Nelson uses a minimal perfect hash table to perform lookups, which can minimize memory bandwidth overhead, but "exact matching requirement is a serious limitation of this approach." However, there is no disclosure or suggestion that the different approaches of Arram and Nelson could be combined in any fashion … Aluru does not disclose loading an additional index corresponding to available memory, nor selectively or partially loading such an index depending on memory capacity. Nelson's hash table described by Aluru is described as reducing memory bandwidth overhead, which is fundamentally different from the claimed memory-aware loading mechanism of the present application. Reducing memory bandwidth is not loading an additional index corresponding to available memory as recited by claim 1. Moreover, Aluru does not disclose the claimed processing flow in which an exact match is first determined using the additional index and alignment is subsequently performed based on that result. The rejection combines selected features of different papers reported by Aluru to reconstruct elements of the present claims. "[W]when evaluating the scope of a claim, every limitation in the claim must be considered. Examiners may not dissect a claimed invention into discrete elements and then evaluate the elements in isolation. Instead, the claim as a whole must be considered." MPEP 2103(I)(C).
It is respectfully submitted that this is not persuasive because every limitation in the claim was considered and Examiners did not dissect a claimed invention into discrete elements. Regarding the "exact matching requirement" and "loading an additional index corresponding to available memory" arguments, Aluru teaches a fast, exact matching solution using a minimal perfect hash table (i.e. additional index) to perform lookups, wherein the hash table is created in software (i.e. inherent loading step, processor and memory) and minimizes the memory bandwidth overhead (i.e. corresponding to an amount of available memory), decreasing the constraint on the speed (pg. 25 col. 1 para. 1). The teaching described anticipates the recited "exact matching requirement" and "loading an additional index corresponding to available memory" as described. The fact that the rejection lists different approaches to mapping does not negate the fact that the recited mapping step is being taught. the Examiner did apply evidentiary rejection correctly. Therefore, the cited references do not lack any teaching of the specific algorithmic logic that comprises the claimed invention. MPEP 2131.01.II pertains
The argued "claimed memory-aware loading mechanism of the present application" does not refer to the instantly recited claim set as there are no recitations referring to "memory-aware loading mechanism." However, the prior art does teach the recited " loading an additional index corresponding to an amount of available memory into memory" as described by Aluru in the paragraph above. Due to the described reasons above, it is interpreted that the claims do not patentably distinguish the claimed invention from the teachings found in the prior art. Furthermore, in this instant application, the amendments support existing claim rejections, in which the recited limitations are all addressed, see Claim Rejections above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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