DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-11 are currently pending and under exam herein.
Claims 1-11 are rejected.
Claims 1 and 7 are objected to.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of the foreign priority application was received on August 12, 2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. At this point in the examination, the effective filing date of claims 1-11 is October 15, 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 3, 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Figure 11 doesn’t have reference signs 101 a, 101 b, and 101 c mentioned in paragraph 0079 of the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 7 are objected to because of the following informalities:
Claim 1 has a typo on page 64, line 6 where it recites “…associated with of the suffix strings…”
Claim 7 has a typo on page 68, line 8 where it recites “…behind of the sorting unit…
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1:
A string generating module… said string generating module is configured to generate a number (N) of partial strings from the suffix strings.
An encoding module that is coupled to said string generating module… said encoding module is configured to use binary values to encode the partial strings to generate a number (N) of encoded partial strings, to encode the short-reads to generate a plurality of to-be-tested encoded strings, and to encode the reference DNA sequence to generate a reference encoded string.
A string selecting module that is coupled to said encoding module… said string selecting module is configured to select a number (P*Q) of the encoded partial strings using an upsampling process… and said string selecting module is configured to select, using a downsampling process, a number (P) of the encoded partial strings from the number (P*Q) of the encoded partial strings that have been sorted as separation strings.
A sorting engine that is coupled to said encoding module and said string selecting module… said sorting engine is configured to perform a sorting operation on the number (P*Q) of the encoded partial strings to sort the encoded partial strings in an ascending order… said sorting engine is configured to perform a grouping operation on the number (N) of the encoded partial strings, using the number (P) of the separation strings, to sort the encoded partial strings into a number (P+1) of groups, and to perform a sorting operation on the encoded partial strings included in each of the number (P+1) of groups, so as to obtain a sorted list of the number (N) of the encoded partial strings… said sorting engine is configured to construct an encoded assembled sequence based on the to-be-tested encoded strings and the reference encoded string and the mapping locations for the short-reads, the encoded assembled sequence indicating a haplotype sequence.
A suffix string array generating module that is coupled to said sorting engine… said suffix string array generating module is configured to generate a suffix string array based on the sorted list of the number (N) of the encoded partial strings.
A data structure generation module that is coupled to said suffix string array generating module… said data structure generation module is configured to generate, based on the suffix string array and the associated indices, a data structure associated with the reference DNA sequence, the data structure including a CNT table, an SA table, an F table, an L table and an OCC table, the F table including a column that lists the first characters of the suffix strings included in rows of the suffix string array, the L table including a column that lists the last characters of the suffix strings included in the rows of the suffix string array, the SA table including a column that lists the indices associated with of the suffix strings included in the rows of the suffix string array, the CNT table including a column that lists, for each of the characters, a row address of a prior row immediately before a row at which the character first appears, the OCC table including columns that correspond respectively to the characters and that each list cumulative numbers of appearances of the corresponding one of the characters in the rows of the L table.
A location generating module… said location generating module is configured to divide each of the short-reads into a plurality of seeds, and, for each of the seeds thus acquired as a result of the division, determine, based on the data structure, at least one candidate row address that is associated with a candidate index indicating a position of the seed in the to-be-tested DNA sequence.
A dynamic processing engine that is coupled to said location generating module… said dynamic processing engine is configured to implement a similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence that is indicated by the candidate indices associated with the seeds of the short-reads, so as to obtain a similarity score for the short-read… said dynamic processing engine is configured to perform the similarity algorithm with respect to the haplotype sequence and the reference DNA sequence.
A mapping module that is coupled to said dynamic processing engine and said sorting engine… said mapping module is configured to, for each of the short-reads, determine, based on the similarity score, a mapping location for the short-read.
A variant calling module that is coupled to said dynamic processing engine… said variant calling module is configured to evaluate a location and a type of a variant in the haplotype sequence based on the result of the similarity algorithm.
Claim 3:
…said sorting module is configured to use the number (P) of the separation strings stored in said memory device to sort the encoded partial strings into the number (P+1) of groups.
Claim 4:
A suffix string generating module that is coupled to said memory device, and that is configured to generate the number (N) of suffix strings and to assign an index to each of the suffix strings.
Claim 6:
…said data structure generation module is configured to generate a partial data structure based on the data structure, and is coupled to said memory device so as to store the partial data structure therein.
…said location generating module is coupled to said memory device so as to access said memory device to obtain the partial data structure, and is configured to reconstruct the data structure based on the partial data structure before implementing the determination of the at least one candidate row address.
Claim 7:
…said sorting engine includes a plurality of sorting units that are arranged in a plurality of series connections, and each of said sorting units includes: a first data input node for receiving a data signal from other parts of the data processing system; a second data input node for receiving data from a preceding one of the sorting units that is connected in front of the sorting unit in the same series connection; a first control input node for receiving a first control signal from the preceding one of the sorting units; a second control input node for receiving a second control signal from an external source; a first output node for transmitting data to a succeeding one of the sorting units that is connected behind of the sorting unit in the same series connection; a second output node for transmitting the first control signal to the succeeding one of the sorting units; a third output node; a fourth output node; a register that includes a clock input node, a data input node, and a data output node that is connected to the first output node of said sorting unit; a comparator that includes two input nodes connected to the first data input node and the data output node of said register, respectively, and an output node connected to the third output node and the second output node of said sorting unit; a first 2*1 multiplexor (MUX) that includes a first input node connected to the first data input node of said sorting unit, a second input node connected to the second data input node of said sorting unit, a control node connected to the first control input node of said sorting unit, and an output node; a 3*1 MUX that includes a first input node connected to the first output node of the preceding sorting unit, a second input node connected to the output node of said first 2*1 MUX, a third input node connected to the first output node the succeeding sorting unit, a control node connected to the second control input node of said sorting unit, and an output node; a second 2*1 MUX that includes a first input node connected to the output node of said 3*1 MUX, a second input node connected to the output node of said register, a control node connected to the output node of said comparator, and an output node connected to the input node of said register; an inverter that is connected to the first control input node of said sorting unit; and an AND gate that includes two input nodes connected to said inverter and the output node of said comparator, respectively, and an output node connected to the fourth output node of said sorting unit.
Claim 8:
…said sorting engine further includes an adder that includes a plurality of input nodes that are connected respectively to the third output nodes respectively of said sorting units, and an output node; in the preprocessing mode, each of the number (P) of the separation strings is stored in one of the sorting units, each of the number (N) of the encoded partial strings is fed into the sorting units via the first data input nodes respectively of said sorting units.
Claim 9:
…after each of the number (N) of the encoded partial strings has been grouped, said sorting engine is configured to perform the sorting operation on the encoded partial strings included in each of the number (P+1) of groups, so as to obtain the sorted list of the number (N) of the encoded partial strings.
Claim 10:
…said sorting units are controlled to store data of a reference encoded sub-sequence that corresponds with a read with consecutive same characters and with a largest binary value; the first data input nodes of said sorting units are configured to receive a plurality of encoded sub-sequences that are associated with consecutive same characters included in one of the to-be-tested encoded string which is encoded from the short-reads and the reference encoded string which is encoded from the reference DNA sequence, and the encoded sub-sequences are stored in the corresponding ones of said sorting units, so as to create a De Bruijn graph; an encoded sub-string that is associated with first to kth characters of one of the short-reads with a smallest mapping location is used as an input to the first data input nodes of each of said sorting units, where k is an integer said sorting units are configured to compare the binary values of the encoded sub-sequence and the encoded sub-string, resulting in the data stored in the one of the sorting units being outputted for reassembly of the encoded string that corresponds with the short-read.
Claim 11:
…said dynamic processing engine includes a plurality of operating units that are configured to perform the similarity algorithm, wherein the similarity algorithm is a Smith-Waterman algorithm; each of the operating units includes three input nodes, and an output node for outputting an output signal, and each of the three input nodes is connected to the output node of another one of the operating units; in the short-read mapping mode, said dynamic processing engine is configured to implement the similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence, so as to obtain a scoring matrix, and a highest score included in the scoring matrix is used as the similarity score associated with the short-read and the candidate index; and in the variant calling mode, said dynamic processing engine is configured to perform the similarity algorithm with respect to each of the haplotype sequences and the reference DNA sequence, so as to obtain a similarity score matrix and a scoring direction matrix that contains information related to the similarity score matrix.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Paragraphs 0053-0055 of the published specification indicate the above recited limitations deemed to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph are components of a computer system. MPEP 2181(II)(B) requires a structure and an algorithm be disclosed in the specification for computer-implemented means-plus-function limitations. The sections in the published specification disclosing the structure and algorithms for the above recited means-plus-function limitations are:
Claim 1:
The algorithm for the string generating module generating partial strings from the suffix strings is in paragraphs 0057, 0087-0088 and Figure 3.
The algorithm for the encoding module to encode partial strings is in paragraphs 0058-0060.
The algorithm for the string selecting module to select encoded partial strings by upsampling is in paragraphs 0090-0091. The algorithm for selecting sorted encoded partial strings by downsampling is in paragraph 0095.
The algorithm for the sorting engine performing a sorting operation on the number (P*Q) of encoded partial strings is in paragraphs 0092-0094 and Figure 12. The algorithm for the sorting engine to perform a grouping operation on the number (N) of encoded partial strings and to sort the encoded partial strings into a number (P+1) groups is in paragraphs 0096-0100 and Figures 12 and 13. The algorithm for the sorting engine to construct an encoded assembled sequence indicating a haplotype is in paragraphs 0132-0152 and Figures 15-25.
The algorithm for the suffix string array generating module to generate a suffix string array is in paragraphs 0101-0103 and Figure 4.
The algorithm for the data structure generation module to generate a data structure associated with the reference DNA sequence is in paragraphs 0104-0108 and Figure 5.
The algorithm for the location generating module to divide short-reads into seeds and determine candidate row addresses associated with a candidate index indicating a position of the seeds is in paragraphs 0111-0120.
The algorithm for the dynamic processing engine to implement a similarity algorithm with respect to the short-reads and content in the part of the reference DNA sequence indicated by candidate indices is in paragraphs 0121-0129 and Figure 14. The algorithm for the dynamic processing engine to perform the similarity algorithm with respect to the haplotype sequence and the reference DNA sequence is in paragraphs 0155-0157.
The algorithm for the mapping module to determine a mapping location for the short-reads based on similarity scores in in paragraph 0130.
The algorithm for the variant calling module to evaluate a location and a type of variant in the haplotype sequence is in paragraphs 0153-0154, 0158-0166 and Figures 26-27.
Claim 3:
The algorithm for the sorting module to use the number (P) of separation strings to sort encoded partial strings into the number (P+1) of groups is in paragraphs 0096-0100 and Figures 12 and 13.
Claim 4:
The algorithm for the suffix string generating module to generate the number (N) of suffix strings and to assign an index to each suffix string in paragraph 0086 and Figure 2.
Claim 6:
The algorithm for the data generation module to generate a partial data structure base on the data structure and store the partial data structure in memory is in paragraphs 0104-0109 and Figures 5-6.
The algorithm for the location generating module to reconstruct the data structure from the partial data structure is in paragraph 0112.
Claim 7:
No structure is provided for the plurality of sorting units, the various data input nodes, control input nodes, data output nodes, clock input nodes, or control nodes.
Claim 8:
No structure is provided for the plurality of input nodes, output nodes, or the sorting units.
Claim 9:
The algorithm for the sorting engine to perform a sorting operation on the grouped number (N) of encoded partial strings in each of a number (P+1) groups is in paragraphs 0096-0100 and Figures 12 and 13.
Claim 10:
The algorithm for controlling the sorting units to store data of a reference encoded sub-sequence that corresponds with a read with consecutive same characters and with a largest binary value is in paragraph 0134-0135 and Figure 15, but no physical structure is provided for the sorting units. No structure is provided for the data input nodes receiving a plurality of encoded sub-sequences that are associated with consecutive same characters included in one of the to-be-tested encoded strings to create a De Bruijn graph. The algorithm for the sorting units to compare binary values of the encoded sub-sequence end the encoded sub-string is in paragraphs 0142-0151 and figures 22-24.
Claim 11:
The algorithm for the operating units performing a Smith-Waterman algorithm is in paragraphs 0077-0082 and Figures 10-11, but no physical structure is proved for the operating units. No structure is provided for the input nodes or the output nodes outputting an output signal. The algorithm for the dynamic processing engine to implement the similarity algorithm in short-read mapping mode is in paragraphs 0121-0129 and figure 14. The algorithm for the dynamic processing engine to perform the similarity algorithm with respect to haplotype sequences is in paragraphs 0155-0157.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 7-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "…said sorting module…" on page 66, line 8. There is insufficient antecedent basis for this limitation in the claim. It is recommended to amend the claims to recite “said sorting engine” to overcome the rejection. For the purposes of examination, the limitation will be interpreted as the sorting engine.
Regarding claims 7-11, the claim limitations of sorting units, data input nodes, control input nodes, output nodes, clock input nodes, control nodes, and operating units recited in claims 7-8 and 10-11 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of any structure that performs the functions of the nodes in the claims which renders the claims indefinite. Claim 9 is also indefinite because it depends from claim 7 and does not resolve the issue of indefiniteness. For the purposes of examination, the nodes will be interpreted as connection points where wires of the circuit are connected and can transmit electrical current to or from the nodes, the sorting and operating units will be interpreted as components of a computer chip with circuitry connecting them and their various parts.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 7-11, the claims are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as discussed above in the 35 U.S.C. 112(b) rejection of the claims. The node, sorting unit, and operating unit limitations recited in the claims to not have corresponding structure in the specification to perform the claimed functions. MPEP 2163.03(VI) discussed that indefinite claims interpreted under 112(f) also lack written description because unbounded functional limitations would cover all ways of performing a function and indicate the inventor has not provided sufficient disclosure to show possession of the invention. Claim 9 is also rejected because it depends from claim 7.
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 1-6 and 11 are rejected under 35 U.S.C. because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls withing any statutory
category (See MPEP 2106.03). Claims 1-6 and 11 recite components of a data processing system configured to analyze DNA sequence data. The claims are directed to a computer system, which is a machine, and falls within one of the statutory categories of invention (Step 1: YES).
Step 2A, prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES)
are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of
nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the
following limitations that equate to an abstract idea:
Claim 1 recites: A data processing system for processing gene sequencing data…; and …the data processing system comprising: a string generating module; an encoding module that is coupled to said string generating module; a string selecting module that is coupled to said encoding module; a sorting engine that is coupled to said encoding module and said string selecting module; a suffix string array generating module that is coupled to said sorting engine; a data structure generation module that is coupled to said suffix string array generating module; a location generating module; a dynamic processing engine that is coupled to said location generating module; a mapping module that is coupled to said dynamic processing engine and said sorting engine; and a variant calling module that is coupled to said dynamic processing engine; wherein the data processing system is configured to operate in one of the following modes: a preprocessing mode, in which said string generating module is configured to generate a number (N) of partial strings from the suffix strings, respectively, each of the partial strings including first to Kth characters of the respective one of the suffix strings, N being a positive integer greater than 2 and K being a positive integer greater than 2, and N>K, said encoding module is configured to use binary values to encode the partial strings to generate a number (N) of encoded partial strings, to encode the short-reads to generate a plurality of to-be-tested encoded strings, and to encode the reference DNA sequence to generate a reference encoded string, said string selecting module is configured to select a number (P*Q) of the encoded partial strings using an upsampling process, and said sorting engine is configured to perform a sorting operation on the number (P*Q) of the encoded partial strings to sort the encoded partial strings in an ascending order, and said string selecting module is configured to select, using a downsampling process, a number (P) of the encoded partial strings from the number (P*Q) of the encoded partial strings that have been sorted as separation strings, wherein P and Q are integers, said sorting engine is configured to perform a grouping operation on the number (N) of the encoded partial strings, using the number (P) of the separation strings, to sort the encoded partial strings into a number (P+1) of groups, and to perform a sorting operation on the encoded partial strings included in each of the number (P+1) of groups, so as to obtain a sorted list of the number (N) of the encoded partial strings, said suffix string array generating module is configured to generate a suffix string array based on the sorted list of the number (N) of the encoded partial strings, and said data structure generation module is configured to generate, based on the suffix string array and the associated indices, a data structure associated with the reference DNA sequence, the data structure including a CNT table, an SA table, an F table, an L table and an OCC table, the F table including a column that lists the first characters of the suffix strings included in rows of the suffix string array, the L table including a column that lists the last characters of the suffix strings included in the rows of the suffix string array, the SA table including a column that lists the indices associated with of the suffix strings included in the rows of the suffix string array, the CNT table including a column that lists, for each of the characters, a row address of a prior row immediately before a row at which the character first appears, the OCC table including columns that correspond respectively to the characters and that each list cumulative numbers of appearances of the corresponding one of the characters in the rows of the L table, a short-read mapping mode, in which said location generating module is configured to divide each of the short-reads into a plurality of seeds, and, for each of the seeds thus acquired as a result of the division, determine, based on the data structure, at least one candidate row address that is associated with a candidate index indicating a position of the seed in the to-be-tested DNA sequence, said dynamic processing engine is configured to implement a similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence that is indicated by the candidate indices associated with the seeds of the short-reads, so as to obtain a similarity score for the short-read, and said mapping module is configured to, for each of the short-reads, determine, based on the similarity score, a mapping location for the short- read, a sequence assembly mode, in which said sorting engine is configured to construct an encoded assembled sequence based on the to-be-tested encoded strings and the reference encoded string and the mapping locations for the short-reads, the encoded assembled sequence indicating a haplotype sequence, and a variant calling mode, in which said dynamic processing engine is configured to perform the similarity algorithm with respect to the haplotype sequence and the reference DNA sequence, and said variant calling module is configured to evaluate a location and a type of a variant in the haplotype sequence based on the result of the similarity algorithm.
Claim 3 recites: …wherein: in the preprocessing mode, said sorting module is configured to use the number (P) of the separation strings stored in said memory device to sort the encoded partial strings into the number (P+1) of groups.
Claim 4 recites: …further comprising a suffix string generating module that is coupled to said memory device, and that is configured to generate the number (N) of suffix strings and to assign an index to each of the suffix strings.
Claim 6 recites: wherein: said data structure generation module is configured to generate a partial data structure based on the data structure…; and …said location generating module… is configured to reconstruct the data structure based on the partial data structure before implementing the determination of the at least one candidate row address.
Claim 11 recites: wherein: said dynamic processing engine includes a plurality of operating units that are configured to perform the similarity algorithm, wherein the similarity algorithm is a Smith-Waterman algorithm…; and …in the short-read mapping mode, said dynamic processing engine is configured to implement the similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence, so as to obtain a scoring matrix, and a highest score included in the scoring matrix is used as the similarity score associated with the short-read and the candidate index; and in the variant calling mode, said dynamic processing engine is configured to perform the similarity algorithm with respect to each of the haplotype sequences and the reference DNA sequence, so as to obtain a similarity score matrix and a scoring direction matrix that contains information related to the similarity score matrix.
The limitations recited in claim 1 of processing gene sequencing data by a preprocessing mode which generates partial strings from suffix strings, encoding partial strings with binary values, selecting partial strings with an upsampling process, sorting partial stings, selecting partial strings with a downsampling process, grouping partial strings, generating a suffix array, and generating a data structure are verbal equivalents of mathematical calculations necessary to preprocess DNA sequence data for follow-on analysis. Additionally, selecting partial strings encompasses mental processes of observation and judgment in which strings to select. The claim 1 limitations of processing gene sequencing data by a short-read mapping mode which divides short-reads into seeds, determines one candidate row address, implements a similarity algorithm, and determines a mapping location for the short-read are verbal equivalents of mathematical calculations necessary to perform read mapping to a reference sequence. The claim 1 limitations of a sequence assembly mode which constructs an encoded assembled sequence and a variant calling mode which performs a similarity algorithm and evaluates a location and type of variant based on the results are also verbal equivalents of mathematical calculations necessary to assemble short-reads and call variants. The remaining elements of the processing system, various modules, and engines merely recite that the judicial exception is being performed in a generic computer environment which does not preclude the mathematical calculations from being performed in the human mind or with pen and paper as claimed.
Similarly, the limitations recited in claims 3, 4, and 6 of a sorting module configured to use the number (P) of the separation strings stored in a memory device to sort the encoded partial strings into groups, a suffix string generating module configured to generate suffix strings and assign an index to each string, a data structure generation module configured to generate partial a partial data structure, and a location generating module configured to reconstruct the data structure are verbal equivalents of mathematical calculations necessary to perform the claimed functions on a generic computer. In the same manner, the limitations recited in claim 11 of a dynamic processing engine with a plurality of operating units configured to perform the Smith-Waterman algorithm in the short-read mapping mode and variant calling mode is also a verbal equivalent of performing mathematical calculations.
Therefore, these limitations fall under the “Mathematical concepts” and “Mental processes” groupings of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to
determine if the claims as a whole integrate the recited judicial exception into a practical application
(Step 2A, prong 2). The claims recite the following additional elements:
Claim 1 recites: …the gene sequencing data including a reference DNA sequence, a plurality of suffix strings, a plurality of indices and a plurality of short-reads, the reference DNA sequence including characters that represent nitrogen-containing nucleobases, the suffix strings being associated with a reference sequence that includes the reference DNA sequence, each of the indices indicating a location of the ending character in the reference sequence and being assigned to a corresponding one of the suffix strings, the short-reads being extracted from a to-be-tested DNA sequence…
Claim 2 recites: …further comprising a memory device that is coupled to said encoding module, said string selecting module, said sorting engine and said dynamic processing engine, and that is configured to store the gene sequencing data and other information that is generated during operations of the data processing system.
Claim 5 recites: …wherein: said data structure generation module is coupled to said memory device so as to store the data structure generated by said data structure generation module therein; said location generating module is coupled to said memory device so as to access said memory device to obtain the data structure for implementing the determination of the at least one candidate row address.
Claim 6 recites: …and is coupled to said memory device so as to store the partial data structure therein; said location generating module is coupled to said memory device so as to access said memory device to obtain the partial data structure…
Claim 11 recites: …each of the operating units includes three input nodes, and an output node for outputting an output signal, and each of the three input nodes is connected to the output node of another one of the operating units…
The additional element in claim 1 of the gene sequencing data is merely selecting a particular data source to be manipulated and doesn’t impose and significant limits on the claims which amounts to insignificant extra-solution activity (MPEP 2106.05g). Likewise, the additional elements in claims 2, 5, and 6 of a memory device configured to store gene sequencing data, a data structure generating module coupled to the memory device to store the data structure, a location generating module coupled to the memory device to access it and obtain the data structure, the data structure generating module coupled to the memory device to store a partial data structure, and the location generating module coupled to the memory device to access it and obtain the partial data structure are insignificant extra-solution activity that amounts to data gathering and output. Lastly, the additional element in claim 11 of the operating units having three input nodes and an output node where the input nodes are connected to an output node of another operating unit is an invocation of a computer to perform the judicial exception and amounts to instructions to apply the judicial exception in a generic computer environment (MPEP 2106.05f).
Therefore, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if
the claims recite an inventive concept that provides significantly more than the judicial exception itself
(Step 2B). The claims recite additional elements that equate to mere instructions to apply the recited
judicial exception in a generic computing environment. Claims that amount to nothing more than
instructions to apply the judicial exception using a generic computer do not render an abstract idea
eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The additional elements recited in the claims amount to well-understood, routine and conventional
(WURC) activity because the specification in paragraphs 0081-0082 discusses operating units of the invention and states “Since the circuitry structure capable of performing the Smith-Waterman algorithm is well known in the related art, details thereof are omitted herein for the sake of brevity.” Also, the specification in paragraph 0005 discusses the field of gene sequencing and the exponential growth of the data generated for analysis. Additionally, the additional elements recite computer functions and laboratory techniques the courts have ruled to be WURC such as: Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546; and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
As such, the combination of additional elements recited in the claims is well-understood,
routine and conventional. The additional elements do not comprise an inventive concept when
considered individually or as an ordered combination that transform the claimed judicial exception into
a patent-eligible application of the judicial exception. Therefore, the claims do not amount to
significantly more than the judicial exception itself (Step 2B: NO) and claims 1-6 and 11 are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hung et al. (US20190213301A1) in view of Rooyen et al. (US10068052B2) and Al Junid et al. (2009 Third UKSim European Symposium on Computer Modeling and Simulation, pp. 181-186). The italicized text corresponds to the instant claim limitations.
Regarding claim 1, Hung et al. teach a DNA sequence alignment system adapted to process DNA sequencing data that includes a reference DNA sequence represented by characters representing a nucleotide base, suffixes with assigned indices representing locations of the characters in the reference DNA sequence (paragraph 0008). This teaching reads on the claim 1 limitations of: A data processing system for processing gene sequencing data, the gene sequencing data including a reference DNA sequence, a plurality of suffix strings, a plurality of indices… the reference DNA sequence including characters that represent nitrogen-containing nucleobases, the suffix strings being associated with a reference sequence that includes the reference DNA sequence, each of the indices indicating a location of the ending character in the reference sequence and being assigned to a corresponding one of the suffix strings… because sequence alignment is a specific example of processing gene sequence data.
Hung et al. teach that their system comprises a string generation unit, a splitter string determining module, a grouping module, a string sorting module, a suffix array generation module, a FM-index data generation module, and a searching module (paragraph 0021; Figure 1). Hung et al. teach that the string generation unit includes a string generation module and that it is configured to acquire the first K number of characters from the suffixes where N > K (paragraph 0023). As an example embodiment, Figure 3 shows the acquired strings where K=4. Since the strings only contain a portion of the entire suffix strings, this teachings reads on the claim 1 limitations of …the data processing system comprising: a string generating module… said string generating module is configured to generate a number (N) of partial strings from the suffix strings, respectively, each of the partial strings including first to Kth characters of the respective one of the suffix strings, N being a positive integer greater than 2 and K being a positive integer greater than 2, and N>K…
Hung et al. further teach that one implementation of their system may have the string generation module include an encoder module configured to convert the characters of the strings into digital codes (paragraph 0024) which reads on the claim 1 limitations of an encoding module that is coupled to said string generating module… said encoding module is configured to use binary values to encode the partial strings to generate a number (N) of encoded partial strings… Additionally, the system comprises a splitter string determining module which includes an up-sampling module that acquires PxQ number of the partial strings generated by the string generating module (Id.) and is coupled to the encoding module in Figure 1, which reads on the claim 1 limitations of a string selecting module that is coupled to said encoding module… said string selecting module is configured to select a number (P*Q) of the encoded partial strings using an upsampling process… The system taught by Hung et al. also includes a string sorting module coupled to the up-sampling module which is configured to sort the PxQ number of strings in lexicographical order and the sorting module can sort the strings in ascending order based on the digital codes, generated by the encoder module, representing the strings (Id.) teaching the claim 1 limitations of a sorting engine that is coupled to said encoding module and said string selecting module… said sorting engine is configured to perform a sorting operation on the number (P*Q) of the encoded partial strings to sort the encoded partial strings in an ascending order. Lastly, Hung et al. teach the splitter string determining module also includes a down-sampling module configured to receive the sorted PxQ number of strings and choose a P number of splitter strings arranged in order (Id.; Figure 3) and a grouping module coupled to the splitter string generation module further configured to group the partial strings into P+1 groups based on the splitter strings (paragraph 0025) and then the string sorting module sorts the grouped partial strings in each of their respective groups (paragraph 0026; Figure 5). Under the broadest reasonable interpretation of the claim 1 limitation of a sorting engine, which is interpreted under 112f, according to paragraph 0055 of the specification the combined grouping module and sorting module taught in Hung et al. encompasses the claimed sorting engine in claim 1 since the modules and engines can be a combination of executable software as a set of logic instructions and/or hardware circuits. Together, these teachings read on the claim 1 limitations of …said string selecting module is configured to select, using a downsampling process, a number (P) of the encoded partial strings from the number (P*Q) of the encoded partial strings that have been sorted as separation strings, wherein P and Q are integers, said sorting engine is configured to perform a grouping operation on the number (N) of the encoded partial strings, using the number (P) of the separation strings, to sort the encoded partial strings into a number (P+1) of groups, and to perform a sorting operation on the encoded partial strings included in each of the number (P+1) of groups, so as to obtain a sorted list of the number (N) of the encoded partial strings.
Hung et al. teach their suffix array generation module is coupled to the string sorting module and creates a suffix array from the sorted partial strings (paragraph 0032) teaching the claim 1 limitations of a suffix string array generating module that is coupled to said sorting engine… said suffix string array generating module is configured to generate a suffix string array based on the sorted list of the number (N) of the encoded partial strings. Hung et al. teach their FM-index data generation module is coupled to the suffix array generation module and establishes a FM-index data structure based on the suffix array associated with the reference DNA sequence (paragraph 0033). The data structure includes the suffix array, an F table, a CNT table, an L table and an OCC table (Figure 9) therefore teaching the claim 1 limitations of a data structure generation module that is coupled to said suffix string array generating module… said data structure generation module is configured to generate, based on the suffix string array and the associated indices, a data structure associated with the reference DNA sequence, the data structure including a CNT table, an SA table, an F table, an L table and an OCC table, the F table including a column that lists the first characters of the suffix strings included in rows of the suffix string array, the L table including a column that lists the last characters of the suffix strings included in the rows of the suffix string array, the SA table including a column that lists the indices associated with of the suffix strings included in the rows of the suffix string array, the CNT table including a column that lists, for each of the characters, a row address of a prior row immediately before a row at which the character first appears, the OCC table including columns that correspond respectively to the characters and that each list cumulative numbers of appearances of the corresponding one of the characters in the rows of the L table.
Furthermore, Hung et al. teach the searching module aligns a target string to the reference DNA sequence based on the FM-index data structure and outputs at least one target index that is one of the location indices and that indicates a location (one of the suffix array addresses) in the reference DNA sequence where the target string is present (paragraphs 0035, 0040) thus teaching the claim 1 limitations of a location generating module… said location generating module is configured to… determine based on the data structure, at least one candidate row address that is associated with a candidate index indicating a position… Here the teachings of Hung et al. are only teaching the claimed process of finding a location of a target string in the reference DNA sequence.
Regarding claim 2, Hung et al. teach their system comprises a storage module and that the string generation unit, splitter string determining module, and grouping module are coupled to it (paragraph 0021). The storage module stores the reference DNA sequence, location indices (paragraph 0022), splitter strings (paragraph 0024), and the FM-index data (paragraph 0034). Since the string generation unit contains the encoding module (paragraph 0024) and the grouping module is coupled with the sorting module (paragraph 0021) which as explained above perform the function of the sorting engine, these teachings read on the claim 2 limitations of the data processing system of Claim 1, further comprising a memory device that is coupled to said encoding module, said string selecting module, said sorting engine… and that is configured to store the gene sequencing data and other information that is generated during operations of the data processing system.
Regarding claim 3, Hung et al. teach the grouping module uses the splitter strings stored in the storage module to group the partial strings into P+1 groups (paragraph 0025), which reads on the claim 3 limitations of the data processing system of Claim 2, wherein: in the preprocessing mode, said sorting module is configured to use the number (P) of the separation strings stored in said memory device to sort the encoded partial strings into the number (P+1) of groups.
Regarding claim 4, Hung et al. teach the system has a suffix generation module that is coupled to the storage module and is configured to generate suffixes and corresponding indices (paragraph 0023; Figure 2). This teaching reads on the claim 4 limitations of the data processing system of Claim 2, further comprising a suffix string generating module that is coupled to said memory device, and that is configured to generate the number (N) of suffix strings and to assign an index to each of the suffix strings.
Regarding claim 5 and 6, Hung et al. teach the FM-index data generation module may store the entire data structure in the storage module or only a part of it (paragraph 0034). The searching module is coupled to the storage module and acquires the FM-index data structure from the storage module prior to performing a sequence alignment (paragraph 0035). Alternatively, the searching module receives the partial FM-index data structure from the storage module and reconstructs the full data structure prior to performing an alignment (Id.). These teaching read on the claim 5 limitations of the data processing system of Claim 2, wherein: said data structure generation module is coupled to said memory device so as to store the data structure generated by said data structure generation module therein; said location generating module is coupled to said memory device so as to access said memory device to obtain the data structure for implementing the determination of the at least one candidate row address and the claim 6 limitations of the data processing system of Claim 2, wherein: said data structure generation module is configured to generate a partial data structure based on the data structure, and is coupled to said memory device so as to store the partial data structure therein; said location generating module is coupled to said memory device so as to access said memory device to obtain the partial data structure, and is configured to reconstruct the data structure based on the partial data structure before implementing the determination of the at least one candidate row address.
Hung et al. are silent of the claim 1 limitations of the gene sequencing data including …a plurality of shot-reads… the short-reads being extracted from a to-be-tested DNA sequence…, the sorting engine being… configured to construct an encoded assembled sequence based on the to-be-tested encoded strings and the reference encoded string and the mapping locations for the short-reads, the encoded assembled sequence indicating a haplotype sequence, a dynamic processing engine that is coupled to said location generating module… said dynamic processing engine is configured to implement a similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence that is indicated by the candidate indices associated with the seeds of the short-reads, so as to obtain a similarity score for the short-read… said dynamic processing engine is configured to perform the similarity algorithm with respect to the haplotype sequence and the reference DNA sequence…, a mapping module that is coupled to said dynamic processing engine… said mapping module is configured to, for each of the short-reads, determine, based on the similarity score, a mapping location for the short-read…, and a variant calling module that is coupled to said dynamic processing engine… said variant calling module is configured to evaluate a location and a type of a variant in the haplotype sequence based on the result of the similarity algorithm…, the location generating module is configured to divide each of the short-reads into a plurality of seeds, and, for each of the seeds thus acquired as a result of the division determining a location of the seed in the to-be-tested DNA sequence and the encoding module configured to encode the short-reads to generate a plurality of to-be-tested encoded strings, and to encode the reference DNA sequence to generate a reference encoded string.
Hung et al. are also silent on the claim 2 limitations of the memory device being coupled to said dynamic processing engine and the limitations of claim 11. However, these limitations were known in the art at the effective filing date of the invention, as taught by Rooyen et al. and Al Junid et al.
Regarding claim 1, Rooyen et al. teach a computer architecture that, in one aspect, processes short read sequences to reconstruct whole genomes (p. 181, column 215, lines 39-45) which reads on the claim 1 limitation of the gene sequencing data including …a plurality of shot-reads… the short-reads being extracted from a to-be-tested DNA sequence… because the short reads are obtained from the whole genome of an organism. Rooyen et al. further teach their system executing a mapping analysis on a plurality of reads of genetic data using an index of genetic reference data where the reads and genetic reference data is represented by a sequence of nucleotides stored in memory (p. 75, column 3, lines 38-44). The mapping module includes one or more processing engines and is configured to receive a read and extract a portion of the read to generate seeds and determine an address within an index data structure (p. 75, column 3, line 57 - column 4, line 4). These teachings read on the claim 1 limitations of the location generating module is configured to divide each of the short-reads into a plurality of seeds, and, for each of the seeds thus acquired as a result of the division determining a location of the seed in the to-be-tested DNA sequence. Here, the mapping module in Rooyen et al. is performing the function of the location generating module of claim 1. Rooyen et al. continue to teach that the addresses of the seeds may be accessed and used to determine one or more matching positions from the read to the genetic reference sequence (p. 75, column 4, lines 8-16). Their system includes an alignment module composed of a set of processing engines which receive one or more mapped positions for the read data and a segment of the reference sequence corresponding to the mapped position (p. 75, column 4, lines 25-27). An alignment of the read to each retrieved reference segment may be calculated along with a score for the alignment, then at least one best-scoring alignment of the read may be selected and output (p. 75, column 4, lines 27-31). The alignment module may implement a dynamic programming algorithm when calculating the alignment such as the Smith-Waterman algorithm (p. 75, column 4, lines 31-34). These teachings read on the claim 1 limitations of a dynamic processing engine that is coupled to said location generating module… said dynamic processing engine is configured to implement a similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence that is indicated by the candidate indices associated with the seeds of the short-reads, so as to obtain a similarity score for the short-read… and a mapping module that is coupled to said dynamic processing engine… said mapping module is configured to, for each of the short-reads, determine, based on the similarity score, a mapping location for the short-read… The processing engine executing the dynamic programming algorithm in Rooyen et al. is the dynamic processing engine in claim 1 and the alignment module outputting the best-scoring alignment is the mapping module in claim 1 because they are performing the same functions, respectively.
Furthermore, Rooyen et al. teach after a subject’s genetic code is sequenced, producing a machine-readable digital representation of the genetic code, it may be useful to further process the digitally encoded genetic sequence data in order to assemble an entire genomic profile where the composition of the entire genome is determined (p. 82, column 17, lines 6-20). Such assembly may be performed by comparison to a reference genome and can be used to determine how the assembled genome differs from the reference (p. 82, column 17, lines 20-25). Particularly, software or hardware components perform operations in localized haplotype assembly where overlapping reads are assembled into haplotype sequences and aligned, by the Smith-Waterman algorithm, to the reference genome which can determine the type of variation from the reference is implied (p. 99, column 51, lines 11-30). These teachings read on the claim 1 limitations of the sorting engine being… configured to construct an encoded assembled sequence based on the to-be-tested encoded strings and the reference encoded string and the mapping locations for the short-reads, the encoded assembled sequence indicating a haplotype sequence because as claimed in claim 1 the sorting engine may be a combination of hardware and software as in paragraph 0055 of the specification. Additionally, the teachings read on the claim 1 limitations of said dynamic processing engine is configured to perform the similarity algorithm with respect to the haplotype sequence and the reference DNA sequence… and a variant calling module that is coupled to said dynamic processing engine… said variant calling module is configured to evaluate a location and a type of a variant in the haplotype sequence based on the result of the similarity algorithm…
Regarding claim 1, Al Junid et al. teach a data compression technique to accelerate the Smith-Waterman algorithm on a hardware-based acceleration device (Abstract). Al Junid et al. teach a module subdivided into four submodules, one of which is called initialization (p. 182, III. S-W algorithm module; Figure 2). In the initialization module, DNA sequences (i.e., search and target sequences) are prepared for the alignment process by first performing data width reduction where characters of the sequences in ASCII format are changed into binary values (p. 183, IV. Initialization Module; A. Reduction Data Width; Table II). The search and target sequences being the sequence being aligned (e.g., short-read or haplotype sequence) and the reference sequence, respectively. This teaching by Al Junid et al. reads on the claim 1 limitation of the encoding module configured to encode the short-reads to generate a plurality of to-be-tested encoded strings, and to encode the reference DNA sequence to generate a reference encoded string.
Regarding claim 2, as discussed above in the teachings of Rooyen et al. for the computer system executing a mapping analysis on a plurality of reads with a processing engine where the reads and genetic reference data is represented by a sequence of nucleotides stored in memory (p. 75, column 3, lines 38-56), Rooyen et al. also teaches the mapping module of the processing engines may access sequence reads and index of a reference sequence from memory (p. 75, column 3, lines 8-13). This teaching reads on the claim 2 limitation of the memory device being coupled to said dynamic processing engine.
Regarding claim 11, Rooyen et al. teach the hardwired digital logic circuits of their system may be arranged as a set of processing engines to perform the steps in the sequence analysis pipeline (p. 75, column 3, lines 50-56) and, as discussed above, the alignment module of the processing engines can perform the Smith-Waterman algorithm (p. 75, column 4, lines 19-36). These teachings read on the claim 11 limitations of said dynamic processing engine includes a plurality of operating units that are configured to perform the similarity algorithm, wherein the similarity algorithm is a Smith-Waterman algorithm. Rooyen et al. teach the alignment module of the processing engines may configured to receive data via one or more of a plurality of physical interconnects (p. 75, column 4, lines 21-24) and to output at least one best-scoring alignment (p. 75, column 4, lines 30-31). Although Rooyen et al. doesn’t explicitly recite 3 input nodes and an output node the recited teachings of sets of processing engines arranged together to perform analysis steps, receiving data from a plurality of physical interconnects, and output data enable one of ordinary skill in the art to arrive at the configuration through routine experimentation, therefore the teachings read on the claim 11 limitations of …each of the operating units includes three input nodes, and an output node for outputting an output signal, and each of the three input nodes is connected to the output node of another one of the operating units.
As discussed above, Rooyen et al. teach their system includes an alignment module composed of a set of processing engines which receive one or more mapped positions for the read data and a segment of the reference sequence corresponding to the mapped position (p. 75, column 4, lines 25-27). An alignment of the read to each retrieved reference segment may be calculated along with a score for the alignment, then at least one best-scoring alignment of the read may be selected and output (p. 75, column 4, lines 27-31). The alignment module may implement a dynamic programming algorithm when calculating the alignment such as the Smith-Waterman algorithm (p. 75, column 4, lines 31-34). Additionally, Rooyen et al. teach in hardware implementation of performing a sequence alignment scoring cells create an alignment matrix (p. 92, column 38, lines 3-20). These teachings read on the claim 11 limitations of …in the short-read mapping mode, said dynamic processing engine is configured to implement the similarity algorithm with respect to each of the short-reads and the content included in the part of the reference DNA sequence, so as to obtain a scoring matrix, and a highest score included in the scoring matrix is used as the similarity score associated with the short-read and the candidate index. Rooyen et al. teach that after the alignment matrix is calculated, there are several different pathways through it and, with respect to the Smith-Waterman algorithm, the maximum score is backtraced through the matrix (p. 93, column 40, line 61 – p. 94, column 41, line 15). A backtrace finds the pathway that was taken to achieve a particular similarity score; usually the highest score (p. 94, column 41, lines 22-27). This is accomplished by moving backwards following the best score alignment arrows thus retracing the pathway that led to achieving the maximum score (p. 94, column 41, lines 27-34). These teachings read on the claim 11 limitations of …and in the variant calling mode, said dynamic processing engine is configured to perform the similarity algorithm with respect to each of the haplotype sequences and the reference DNA sequence, so as to obtain a similarity score matrix and a scoring direction matrix that contains information related to the similarity score matrix because, as taught above, the variant call module performs an alignment using the Smith-Waterman algorithm on the haplotype sequence (p. 99, column 51, lines 11-30).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Rooyen et al. teach that all the operations of processing DNA sequence data can be slow and expensive when implemented on traditional computer platforms due in part to the very large input and output datasets (p.134, column 121, lines 29-35). The system of Rooyen et al. overcomes these problems in various way such as configuration of hardware processing engines (p. 133, column 121, lines 35-39). Hung et al. teach that as the amount of DNA sequencing data grows, the time to process such data becomes ever more time consuming (paragraph 0003) and existing methods require too much memory (paragraph 0004) and their invention is meant to alleviate the drawbacks (paragraph 0005). A person having ordinary skill in the art would be motivated to combine the teachings of Hung et al. with those of Rooyen et al. to increase the sequence data processing speed and reduce the required memory to perform such operations. The ordinary artisan would have a reasonable expectation of success because both Hung et al. and Rooyen et al. use hardware such as field programmable gate arrays to achieve the acceleration of data processing. Additionally, the skilled artisan would be motivated to combine the teachings of Al Junid et al. with the combined system taught by Hung et al. and Rooyen et al. for the same reason of increasing processing speeds and reduce memory requirements. There would be a reasonable expectation of success because Al Junid et al. also achieve their speed optimization and memory reduction through hardware acceleration methods (Abstract). The invention is therefore prima facie obvious.
Conclusion
No claims are allowed.
Claims 7-10 are eligible subject matter under 35 U.S.C. 101 at step 2A, prong 2 because claim 7 incorporates the recited judicial exception into a particular machine (i.e., the claimed sorting engine). The elements of the sorting engine in claim 7 are particularly claimed and are integral for the data processing system of claim 1. Claims 8-10 are patent eligible because they depend upon claim 7.
Claims 7-10 are free of the prior because there are no teachings indicating all the claimed limitations of the sorting engine in claim 7. The closest prior art is Hung et al. (US20190213301A1), now patent US11302419B2, which discloses a sorting module with a plurality of sorting elements (Figures 6 and 7). However, the configuration of the sorting elements is different and there are no prior art teachings or suggestions to modify the sorting module and sorting elements to obtain the sorting engine of claim 7. Claim 8-10 depend on claim 7 and are therefore free of the prior art.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMUR Y OLJUSKIN whose telephone number is (571)272-4006. The examiner can normally be reached Mon - Fri; 0800-1630 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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