DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
3. Claimed benefit of domestic priority U.S. Provisional Application 63/427,621 filed on 23 November 2022 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 23 November 2022. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
4. The information disclosure statements (IDSs) submitted on 31 October 2023 is being considered by the examiner.
Drawings
5. The drawings submitted on 20 July 2023 are accepted by the examiner.
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.
6. This application includes one or more claim limitations that use the word “means” or “step” and are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112. Such claim limitations are:
In claim 17, the limitation: means for accessing the dense storage system to retrieve the DNA sequence and the at least one copy of the DNA sequence” is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “The memory device may also include address space. The address space can serve as at least a portion of an address space used by the processor. In an example, the address space can store data at a byte-addressable level that can be accessed by the processor (e.g., via the communication interface)” (para. 00109), and equivalents thereof.
In claim 17, the limitation “means for dividing the DNA sequence and the at least one copy of the DNA sequence into corresponding DNA segments, each of the DNA segments comprising at least one DNA symbol” is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “a control system operable to: divide a plurality of copies of a DNA sequence into multiple DNA segments” (para. 00114), and “Each DNA sequence is divided into one or more DNA segments having a segment length n (where n is equal to or greater than one)” (para. 0007), and equivalents thereof.
In claim 17, the limitation: means for comparing the corresponding DNA segments to determine at least one of: a ratio of the DNA symbols in agreement between the corresponding DNA segments; and a length consensus between the corresponding DNA segments is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: 1) “a control system operable to compare corresponding DNA segments in each of the plurality of copies of the DNA sequences to determine at least one of: a ratio of DNA symbols in agreement between corresponding DNA segments of the multiple DNA segments; and a length consensus between corresponding DNA segments of the multiple DNA segments” (para. 00114); and 2) “Corresponding DNA segments for each DNA sequence are compared to determine: 1) a ratio of DNA symbols in agreement in each of the DNA segments; and/or 2) whether a length of each of the DNA segments are the same (e.g., whether each DNA segment has the same number of DNA symbols)” (para. 0007), and equivalents thereof.
In claim 17, the limitation: means for generating soft information associated with each of the DNA segments based, at least in part, on the at least one of the ratio of the DNA symbols in agreement and the length consensus is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “a control system operable to generate soft information associated with each of the multiple DNA segments based, at least in part, on the at least one of the ratio of DNA symbols in agreement and the length consensus” (para. 00114), and “the present application describes a DNA-based storage system that generates and updates soft bits, or soft information. In order to generate soft bits, the DNA-based storage system generates multiple copies of a particular DNA string or DNA sequence. Each DNA sequence is divided into one or more DNA segments having a segment length n (where n is equal to or greater than one). Corresponding DNA segments for each copy of the DNA sequence are compared to determine a ratio of DNA symbols in agreement in each of the DNA segments and/or if a length of each of the DNA segments are the same. Using this information, soft information (or soft bits) are generated” and equivalents thereof.
In claim 18, the limitation: means for determining a bit error rate (BER) associated with each of the DNA segments is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “a control system operable to generate soft information associated with each of the multiple DNA segments based, at least in part, on the at least one of the ratio of DNA symbols in agreement and the length consensus. In an example, generating soft information comprises determining a bit error rate (BER) associated with each of the multiple DNA segments” (para. 00114), and “Corresponding DNA segments for each DNA sequence are compared to determine: 1) a ratio of DNA symbols in agreement in each of the DNA segments; and/or 2) whether a length of each of the DNA segments are the same (e.g., whether each DNA segment has the same number of DNA symbols). The ratio and/or the length information may be used to determine a bit error rate (BER) of a particular DNA segment” (para. 0007-0008), and equivalents thereof.
In claim 19, the limitation: means for generating a log likelihood ratio associated with the DNA segments based, at least in part, on the bit error rate associated with each of the DNA segments is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “the control system is further operable to generate a log likelihood ratio (LLR) associated with each of the multiple DNA segments based, at least in part, on the determined bit error rate associated with each of the multiple DNA segments.” (para. 00114), and “log-likelihood ratio (LLR) inputs indicate a data value and a probability that the data value is correct” (para. 0024) using the LLR equation in para. 0053, and equivalents thereof.
In claim 20, the limitation: means for providing the soft information to a decoding means associated with the DNA-based storage system is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: “Updating the soft information or the log likelihood ratio (LLR) values may be completed by one or more subsystems of the data storage device 100 shown and described with respect to FIG. 1. (para. 0071), and equivalents thereof.
In claim 20, the limitation: “a decoding means” is interpreted under 35 U.S.C. 112(f). The corresponding structure disclosed in the specification is: 1) The string of ones and zeros is then provided to decoder that implements a low-density parity-check (LDPC) code error correction scheme (para. 0005), and 2) Referring to Fig. 1, once the DNA sequence has been read, a decoding system 125 maps the DNA symbols back to digital data. For example, if the decoding system 125 receives CATGCA as the DNA sequence, the decoding process performed by the decoding system 125 would return 010010110100 to a requesting computing device (e.g., computing device 150 (para. 0040) and equivalents thereof.
For each of the limitations listed above, further corresponding structure is shown in Figure 5 of the drawings and comprises a system 500 that includes a host device 505 and a data storage device 510. The host device may be similar to the computing device 150 shown and described with respect to FIG. 1. The host device 505 includes a processor 515 and a memory device 520 (e.g., main memory). The memory device 520 may include an operating system 525, a kernel 530 and/or an application 535. This system is also described in the specification in para. 0097-0099. Furthermore, FIG. 3 illustrates an example method 300 for generating soft bit information for a DNA-based storage systems according to an example. In an example, one or more of the operations shown and described with respect to FIG. 3 may be performed by one or more subsystems of the DNA-based storage system 100 shown and described with respect to FIG. 1. (see para. 0062), and equivalents thereof.
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.
7. Claims 17-20 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.
The term “dense” in claim 17 is a relative term which renders the claim indefinite. The term “dense” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. A person having ordinary skill in the art would not know what density of storage is claimed in the invention. For the purposes of review and with broadest reasonable interpretation, the dense storage system will be interpreted to mean any storage system capable of storing numerical and bitwise representations of DNA fragments. Claims 18-20 are rejected by virtue of dependence on claim 17 and for failing to resolve the indefiniteness issue.
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.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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: dividing the first copy of the DNA sequence into a first DNA segment and a second DNA segment, each of the first DNA segment and the second DNA segment of the first copy of the DNA sequence including at least one DNA symbol
Claim 1 recites: dividing the second copy of the DNA sequence into a first DNA segment and a second DNA segment, each of the first DNA segment and the second DNA segment of the second copy of the DNA sequence including at least one DNA symbol
Claim 1 recites: comparing the first DNA segment of the first copy of the DNA sequence to the first DNA segment of the second copy of the DNA sequence to determine first consensus information
Claim 1 recites: comparing the second DNA segment of the first copy of the DNA sequence to the second DNA segment of the second copy of the DNA sequence to determine second consensus information
Claim 1 recites: determining, based at least in part, on the first consensus information, a first bit error rate (BER) associated with the first DNA segment of the first copy of the DNA sequence
Claim 1 recites: determining, based at least in part, on the second consensus information, a second bit error rate (BER) associated with the second DNA segment of the first copy of the DNA sequence
Claim 2 recites: the method of claim 1, wherein the first consensus information comprises a determination of a ratio of DNA symbols in agreement between the first DNA segment of the first copy of the DNA sequence and the first DNA segment of the second copy of the DNA sequence
Claim 3 recites: the method of claim 1, wherein the first consensus information comprises length information of the first DNA segment of the first copy of the DNA sequence compared to length information of the first DNA segment of the second copy of the DNA sequence
Claim 4 recites: The method of claim 1, further comprising associating a reliability bin with a log likelihood ratio (LLR), the log likelihood ratio being associated with the first DNA segment of the first copy of the DNA sequence
Claim 5 recites: the method of claim 4, wherein the log likelihood ratio associated with the first DNA segment of the first copy of the DNA sequence is determined based, at least in part, on the first bit error rate
Claim 6 recites: the method of claim 4, wherein the log likelihood ratio is determined in an offline environment
Claim 7 recites: the method of claim 4, further comprising updating the log likelihood ratio associated with the first DNA segment of the first copy of the DNA sequence
Claim 8 recites: the method of claim 1, further comprising providing the soft information to a low-density parity-check (LDPC) decoder
Claim 9 recites: divide a plurality of copies of a DNA sequence into multiple DNA segments
Claim 9 recites: compare corresponding DNA segments in each of the plurality of copies of the DNA sequences
Claim 9 recites: determine at least one of: a ratio of DNA symbols in agreement between corresponding DNA segments of the multiple DNA segments; and a length consensus between corresponding DNA segments of the multiple DNA segments
Claim 9 recites: generate soft information associated with each of the multiple DNA segments based, at least in part, on the at least one of the ratio of DNA symbols in agreement and the length consensus
Claim 10 recites: the DNA-based storage system of claim 9, wherein generating soft information comprises determining a bit error rate (BER) associated with each of the multiple DNA segments.
Claim 11 recites: the DNA-based storage system of claim 10, wherein the control system is further operable to generate a log likelihood ratio (LLR) associated with each of the multiple DNA segments based, at least in part, on the determined bit error rate associated with each of the multiple DNA segments
Claim 12 recites: the DNA-based storage system of claim 11, wherein the control system is further operable to update the log likelihood ratio associated with each of the multiple DNA segments
Claim 13 recites: the DNA-based storage system of claim 10, wherein the control system is further operable to associate a reliability bin to each of the multiple DNA segments
Claim 14 recites: the DNA-based storage system of claim 13, wherein the control system is further operable to assign a particular log likelihood ratio (LLR) to a particular reliability bin
Claim 15 recites: the DNA-based storage system of claim 10, wherein the control system is further operable to determine an initial log likelihood ratio in an offline environment
Claim 16 recites: the DNA-based storage system of claim 10, wherein the control system is further operable to provide the soft information to a low-density parity-check (LDPC) decoder associated with the DNA-based storage system
Claim 17 recites: accessing the dense storage system to retrieve the DNA sequence and the at least one copy of the DNA sequence
Claim 17 recites: dividing the DNA sequence and the at least one copy of the DNA sequence into corresponding DNA segments, each of the DNA segments comprising at least one DNA symbol
Claim 17 recites: comparing the corresponding DNA segments to determine at least one of: a ratio of the DNA symbols in agreement between the corresponding DNA segments; and a length consensus between the corresponding DNA segments
Claim 17 recites: generating soft information associated with each of the DNA segments based, at least in part, on the at least one of the ratio of the DNA symbols in agreement and the length consensus
Claim 18 recites: the DNA-based storage system of claim 17, wherein the means for generating soft information comprises means for determining a bit error rate (BER) associated with each of the DNA segments
Claim 19 recites: the DNA-based storage system of claim 18, further comprising means for generating a log likelihood ratio associated with the DNA segments based, at least in part, on the bit error rate associated with each of the DNA segments
The limitations regarding ‘determination of a ratio’, ‘updating the log likelihood ratio (which is considered to be recalculating a log likelihood ratio)’, ‘determining a first or second bit error rate’, ‘generate soft information’, ‘determining a bit error rate, generate a log likelihood ratio’, ‘determine an initial log likelihood ratio’ are verbal equivalents that describe a mathematical calculation that is performed as the limitation and are so simple that they could be performed in the human mind or with pen and paper. Therefore, these limitations fall under the "Mathematical concepts" and "Mental processes" groupings of abstract ideas.
The limitations regarding ‘providing the soft information to a low-density parity-checker (LDPC) decoder’ and ‘providing the soft information to a decoding means’ are considered to fall into the "Mathematical concepts" groupings of abstract ideas above because it is interpreted to be equivalent to executing the decoder, which involves mathematical calculations using linear algebra, graph theory and probability calculations.
The limitations directed to ‘dividing DNA sequence into segments’, ‘comparing DNA segments’, ‘determine consensus information’, ‘associating a reliability bin with a log likelihood ratio’, ‘determine a length consensus’, ‘associate a reliability bin to each of the multiple DNA segments’, ‘assign a particular log likelihood ratio to a particular reliability bin’ are generically recite data analysis steps that can be practically performed in the human mind because the human mind is capable of identifying relevant information, comparing values, and determining information from other values. Therefore, these limitations fall under the "Mental processes" groupings of abstract ideas
The limitations of claim 3 that further limits the content of the consensus information, the limitations of claims 5 and 6, that further limit how and where the log likelihood ratio is determined, merely further limit the judicial exceptions but do not change their positions as abstract ideas.
While claims 8-20 recite performing some aspects of the analysis with a controller or a decoder, there are no additional limitations that indicate that this controller or decoder requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the "Mental processes" grouping of abstract ideas.
As such, claims 1-20 recite an abstract idea (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 or not (Step 2A, Prong 2). This 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 or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 1 recites: a method for generating soft information for a DNA-based storage system, comprising: receiving, from a DNA storage medium of the DNA-based storage system, a first copy of a DNA sequence and a second copy of the DNA sequence
Claim 9 recites: a DNA-based storage system, comprising: a control system
Claim 17 recites: a DNA-based storage system, comprising: a dense storage system
Claim 17 recites: storing a DNA sequence and at least one copy of the DNA sequence
Claim 17 recites: a control system operably coupled to the dense storage system
Claim 17 recites: accessing the dense storage system to retrieve the DNA sequence and the at least one copy of the DNA sequence
The additional element of claims 1 and 17 directed to ‘receiving a first copy of a DNA sequence and a second copy of the DNA sequence’ and ‘retrieve the DNA sequence and the at least one copy of the DNA sequence’ merely serve to gather data that is used an input for the judicial exception. Mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application (In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)). Similarly storing data that is input to or output from the judicial exception, are pre-solution and post-solution "extra-solution activity" as they are activities that are incidental to the primary process or product that are merely a nominal or tangential addition to the claim (MPEP 2106.05(g)).
There are no limitations that indicate that the control system or decoder, or dense storage system requires anything other than a generic computing system. There was no limiting definition of the ‘dense storage system’ found in the specification. In paragraph 0004, it is recited that DNA-based storage systems are more dense than traditional electronic data storage systems, but no further information is given regarding any particular configuration or density of the storage; therefore, the storage system is claimed generically as one with capacity to store data representing DNA sequences. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitation of data types to a ‘DNA-based’ storage system is an additional element, but it doesn’t amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-20 are directed to an abstract idea (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 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, and conventional activity.
As set forth in MPEP section 2106.05(g), the courts have decided that limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). As evidenced by Heinis et al. (2023, AMC Computing Surveys, Vol. 56, p. 1-30), there were at least 12 control systems with storage used before the effective filing date to perform methods for error-based processing in DNA storage, which included receiving representations of DNA sequences and copies of DNA sequences (Table 1).
The limitations of claims 9 and 17 pertaining to the computer system used to execute the method, are directed to performing judicial exceptions with a generic computing system on a generic computer. These limitations are not sufficient to amount to significantly more than the judicial exception because, as set forth in the MPEP section 2106.05(d)(II)), using a generic computing environment or generic computer to perform the judicial exception, has been deemed well-understood, routine and conventional activity including receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362), performing repetitive calculations (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)), 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)). Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more.
Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 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)(1) 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.
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.
9. Claims 9, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Chandak et al. (2019 5th Annual Allerton conference on communication, control and computing, Monticello, IL, USA, September 24-27, 2019, p. 147-157), as evidenced by Lassman et al. (Nucleic Acids Research 2009, Vol. 37, p. 858-865), Heinis et al. (ACM Computing Surveys, 2023, Vo. 56, p. 1-30) and Chiaromonte et al. (Pacific Symposium on Biocomputing, 2002, Vol. 7, p. 115-126). The italicized text corresponds to the instant claim limitations.
With respect to claim 9, Chandak et al. teach a DNA-based storage system comprising a control system. Chandak et al. disclose the current implementation is written in Python with the libraries for LDPC codes, BCH codes, barcode removal and multiple sequence alignment written in C/C++. All experiments were done on a server with 40-core Intel Xeon processor (2.20GHz) and 256 GB RAM. (Fig. 5; p. 155, col. 2, para. 2; A DNA-based storage system, comprising: a control system).
Regarding claim 9, Chandak et al. teaches that encodings are segmented and mapped to DNA; next, a synchronization marker is added in the middle of each DNA fragment (which is used for detection of insertion/deletion errors based on length), followed by adding an index tag. This addition of a synchronization marker segments each DNA fragment. Chandak et al. further teaches that this step is followed by collecting reads corresponding to the same index, performing multiple sequence alignment and taking a majority vote (i.e. a consensus) at each position; divide a plurality of copies of a DNA sequence into multiple DNA segments; compare corresponding DNA segments in each of the plurality of copies of the DNA sequences).
Regarding claim 9, Chandak et al. discloses using Kalign 2 to perform multiple sequence alignment to calculate consensus information and that their method detects and corrects for both errors and erasures. Regarding the ratio limitation, as evidenced by Lassman et al. Kalign 2 multiple sequence alignment algorithm uses the HOXD substitution matrix (Lassman et al. p. 863, col. 1, para. 2). As evidenced by Chiaramonte et al., the equation for the ratios of aligned (homologous) to misaligned (chance) nucleotide pairs for the HOXD substitution matrix (and other substitution matrices) is defined by the fundamental log-odds ratio formula used in sequence alignment (Chiaramonte et al. p. 116, para. 5 – p. 118, para. 1). Inherent to this equation is the ratio of aligned to misaligned pairs (the odds ratio). Regarding length information, as evidenced by Lassman et al., Kalign 2 has the standard gap open and close penalties plus other gap parameters to take into consideration alignments of sequences of different lengths with deletions (erasures) (p. 859, col. 2, para. 5 – p. 860, col. 1, para. 4) (Chandak et al. abstract; p. 151, col. 2, para. 4; determine at least one of: a ratio of DNA symbols in agreement between corresponding DNA segments of the multiple DNA segments; and a length consensus between corresponding DNA segments of the multiple DNA segments).
Regarding claim 9, as described above, Chandak et al. discloses performing multiple sequence alignments in determining consensus, which involves using length discrepancies/gaps between multiple copies to perform sequence alignment. Chandak et al. further discloses that the multiple sequence alignment step provides the counts of 0's and 1's for each position in the oligonucleotide, and rather than using just the consensus sequence, they utilize the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model. Chandak et al. further discloses associating the substitution error rate with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
n
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
n
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. The sum k0 + k1 is then the total number of used reads of the sequence, which the authors model as a Poisson-distributed random variable. As further evidenced by Heinis et al., the disagreements between the strands are modelled by Chandak et al. to result from transmitting each of the k0 + k1 sequences over a binary symmetric channel with a predefined substitution probability ϵ (ϵ is set to 0.04 in the experiments). Under these assumptions, Chandak et al. derive a formula for channel log-likelihood ratios and use them as input to belief propagation decoding of the LDPC code. The equation shown above is equivalent to the equation shown in para. 0053 of the specification of the instant application describing the relationship between LLR and error rate in one embodiment (Chandak et al. p. 152, para. 2; p. 151, col. 2, para. 4 – p. 152, col. 1, para. 3; Heinis et al. p. 24, para. 3- p. 25, para. 1; generate soft information associated with each of the multiple DNA segments based, at least in part, on the at least one of the ratio of DNA symbols in agreement and the length consensus).
Regarding claim 17, the term “dense storage system” does not have a limiting definition in the specification. In para. 0030, an example is given wherein “the data storage system may be used to store data that is “more dense” when compared to data that is stored in a traditional electronic storage medium such as, for example, hard disks, optical disks, flash memory, and the like. For example, the data storage system may be used to store synthetic DNA-based data. Under broadest reasonable interpretation, a “dense storage system” will be a storage systems that is capable of storing DNA sequences.
Regarding claim 17, Chandak et al. teach a DNA-based storage system comprising a control system. Chandak et al. disclose the current implementation is written in Python with the libraries for LDPC codes, BCH codes, barcode removal and multiple sequence alignment written in C/C++. All experiments were done on a server with 40-core Intel Xeon processor (2.20GHz) and 256 GB RAM. Chandak et al. does not specifically teach “a dense storage system”, however, this computer as used to perform the method to encode and decode an image, the system disclosed inherently has a storage medium with the capacity to store the data and a means for the processor to access the stored data (Fig. 5; p. 155, col. 2, para. 2; Fig. 7; a DNA-based storage system, comprising: a dense storage system storing a DNA sequence and at least one copy of the DNA sequence; and a control system operably coupled to the dense storage system and comprising: means for accessing the dense storage system to retrieve the DNA sequence and the at least one copy of the DNA sequence; and a means for performing the [method described below]).
Regarding claim 17, Chandak et al. teaches a method wherein encodings are segmented and mapped to DNA; next, a synchronization marker is added in the middle of each DNA fragment (which is used for detection of insertion/deletion errors based on length), followed by adding an index tag. This addition of a synchronization marker segments each DNA fragment. Chandak et al., further teaches that this step is followed by collecting reads corresponding to the same index, performing multiple sequence alignment and taking a majority vote (i.e. a consensus) at each position. Chandak et al. further discloses using Kalign 2 to perform multiple sequence alignment to calculate consensus information and that their method detects and corrects both errors and erasures. Regarding the ratio limitation, as evidenced by Lassman et al. Kalign 2 multiple sequence alignment algorithm uses the HOXD substitution matrix (Lassman et al. p. 863, col. 1, para. 2). As evidenced by Chiaramonte et al., the equation for the ratios of aligned (homologous) to misaligned (chance) nucleotide pairs for the HOXD substitution matrix (and other substitution matrices) is defined by the fundamental log-odds ratio formula used in sequence alignment (Chiaramonte et al. p. 116, para. 5 – p. 118, para. 1). Inherent to this equation is the ratio of aligned to misaligned pairs (the odds ratio). Regarding the length consensus limitation, as evidenced by Lassman et al., Kalign 2 has the standard gap open and close penalties plus other gap parameters to take into consideration alignments of sequences of different lengths with deletions (erasures) (p. 859, col. 2, para. 5 – p. 860, col. 1, para. 4) (Chandak et al. abstract; Fig. 5; p. 149, col. 2, para. 3 – p. 150, col. 1, para. 1; p. 151, col. 1, para. 3; p. 151, col. 2, para. 3 – p. 152, col. 1, para. 1; dividing the DNA sequence and the at least one copy of the DNA sequence into corresponding DNA segments, each of the DNA segments comprising at least one DNA symbol; comparing the corresponding DNA segments to determine at least one of: a ratio of the DNA symbols in agreement between the corresponding DNA segments; and a length consensus between the corresponding DNA segments).
Regarding claim 17, Chandak et al. discloses that the multiple sequence alignment step provides the counts of 0's and 1's for each position in the oligonucleotide, and rather than using just the consensus sequence, they utilize the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model. Chandak et al. further discloses associating the substitution error rate with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
n
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
n
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. The sum k0 + k1 is then the total number of used reads of the sequence, which the authors model as a Poisson-distributed random variable. As further evidenced by Heinis et al., the disagreements between the strands are modelled by Chandak et al. to result from transmitting each of the k0 + k1 sequences over a binary symmetric channel with a predefined substitution probability ϵ (ϵ is set to 0.04 in the experiments). Under these assumptions, Chandak et al. derive a formula for channel log-likelihood ratios and use them as input to belief propagation decoding of the LDPC code. The equation shown above is equivalent to the equation shown in para. 0053 of the specification of the instant application describing the relationship between LLR and error rate in one embodiment (Chandak et al. p. 152, para. 2; Heinis et al. p. 24, para. 3- p. 25, para. 1; generating soft information associated with each of the DNA segments based, at least in part, on the at least one of the ratio of the DNA symbols in agreement and the length consensus).
Regarding claim 20, Chandak et al. discloses the MSA step provides the counts of 0's and 1's for each position in the oligonucleotide. Chandak et al. further discloses rather than using just the consensus sequence, their method utilizes the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model (p. 152, col. 1, para. 2; Fig. 5; the DNA-based storage system of claim 17; further comprising providing the soft information to a decoding means associated with the DNA-based storage system).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
10. Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chandak et al. (2019 5th Annual Allerton conference on communication, control and computing, Monticello, IL, USA, September 24-27, 2019, p. 147-157), as evidenced by Lassman et al. (Nucleic Acids Research 2009, Vol. 37, p. 858-865), Heinis et al. (ACM Computing Surveys, Vo. 56, p. 1-30) and Chiaromonte et al. (Pacific Symposium on Biocomputing, 2002, Vol. 7, p. 115-126), in view of Lu et al. (IEEE Access, 2020, Vol. 8, p. 162892-162993) and Yazdi et al. (2017, Scientific Reports, Vol. 7, p. 1-36). The italicized text corresponds to the instant claim limitations The italicized text corresponds to the instant claim limitations.
Regarding claim 1, Chandak et al. discloses a method for DNA-based storage including detection of error in synthesis and sequencing of DNA and using low-density parity check (LDPC) codes for error correction. Chandak et al. further discloses testing the method by performing 9 experiments over 5 months testing data storage and retrieval in DNA with synthesis done by CustomArray, and that the data stored in the DNA sequences were images and text. According to Chandak et al., the DNA fragments were amplified by PCR and then sequenced, and primers were removed. Chandak et al. further discloses that there are multiple sequences representing each position (abstract; Fig. 5; p. 152, col. 1, para. 3-col. 2, para. 1; Fig. 7; p. 149, col. 2, para. 3 – p. 150, col. 1, para. 1; a method for generating soft information for a DNA-based storage system, comprising: receiving, from a DNA storage medium of the DNA-based storage system, a first copy of a DNA sequence and a second copy of the DNA sequence).
Regarding claim 1, Chandak et al. teaches that encodings are segmented and mapped to DNA; next, a synchronization marker is added in the middle of each DNA fragment (which is used for detection of insertion/deletion errors based on length), followed by adding an index tag. This addition of a synchronization marker segments each DNA fragment. Chandak et al., further teaches that this step is followed by collecting reads corresponding to the same index, performing multiple sequence alignment and taking a majority vote (i.e. a consensus) at each position (p. 151, col. 1, para. 3; p. 149, col. 2, para. 3 – p. 150, col. 1, para. 1; p. 151, col. 2, para. 3 – p. 152, col. 1, para. 1; dividing the first copy of the DNA sequence into a first DNA segment and a second DNA segment, each of the first DNA segment and the second DNA segment of the first copy of the DNA sequence including at least one DNA symbol; dividing the second copy of the DNA sequence into a first DNA segment and a second DNA segment, each of the first DNA segment and the second DNA segment of the second copy of the DNA sequence including at least one DNA symbol; comparing the first DNA segment of the first copy of the DNA sequence to the first DNA segment of the second copy of the DNA sequence to determine first consensus information; comparing the second DNA segment of the first copy of the DNA sequence to the second DNA segment of the second copy of the DNA sequence to determine second consensus information).
Pertaining to claim 1, Chandak et al. further discloses calculating a substitution error rate (equivalent to a bit error rate) at particular positions in the sequence. Chandak et al. further discloses doing this by using a probabilistic error model using the counts of 0’s and 1’s at particular positions in the sequence (i.e. the consensus information). Chandak et al. also discloses performing their method with different parameters and calculating different bit errors detected and corrected depending on the parameters (p. 152, col. 1, para. 2; Table 3; p. 154, col. 2, para. 2; determining a bit error rate (BER)).
Regarding claim 1, Chandak et al. is silent to determining, based at least in part, on the first consensus information, a first bit error rate (BER) associated with the first DNA segment of the first copy of the DNA sequence. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Lu et al.
Regarding claim 1, Lu et al. teaches an error rate-based log-likelihood ratio processing for low-density parity checking in DNA storage. Lu et al. further teaches calculating/scaling LLRs based on observed error rates rather than a fixed value like Chandak et al. Lu et al. teaches calculating LLR using the ratio of occurrences of each base obtained from the experiments using two scaling strategies to estimate the substitution error rates. (p. 162894, col. 2, para. 2; p. 162895, col. 1, para. 2 – p. 162897, col. 1, para. 4; determining, based at least in part, on the first consensus information, a first bit error rate (BER) associated with the first DNA segment of the first copy of the DNA sequence).
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. Lu et al. taught that due to the mismatch between the real channel and the observed statistics and also the limit of maximum decoder input value, scaling the magnitude of the log likelihood ratio (LLR) can lead to a better error correcting performance (abstract). Therefore, one of ordinary skill in the art would have been motivated to utilize the improved LLR processing schemes based on observed statistics for low-density parity-check (LDPC) code decoding to improve error correcting performance. Furthermore, one of ordinary skill in the art would predict that the data and data acquisition methods taught by Lu et al. could be readily added to the system of Chandak et al. with a reasonable expectation of success because they both pertain to DNA-based data storage using LDPC codes. Furthermore, Lu et al. uses similar encoding, multiple sequence alignment (MSA), and decoding as Chandak et al. in their method. The invention is therefore prima facie obvious.
Regarding claim 1, Chandak et al. and Lu et al. are silent to determining, based at least in part, on the second consensus information, a second bit error rate (BER) associated with the second DNA segment of the first copy of the DNA sequence. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Yazdi et al. 2017.
Regarding claim 1, Yazdi et al. teaches a DNA-based storage system with error detection and correction. Yazdi et al. further disclose that raw images were compressed, encoded and synthesized into DNA blocks (i.e. segments) and in one implementation there were 17 DNA blocks out of which 16 blocks were of length 1,000 bp and one single block was of length 880 bp. Yazdi et al. further teaches determining the number of each type of error (substitutions, insertions and deletions) for each block (both per consensus and per read average) and that the error rate can be very different depending on the DNA block/segment (Fig. 3; p. 4, para. 2; Table 2; the DNA-based storage system of claim 17, wherein the means for generating soft information comprises means for determining a bit error rate (BER) associated with each of the DNA segments).
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. Chandak et al. and Lu et al. taught that accurate error-rate information improves the reliability of LLR soft information used by the LDPC decoder (Lu et al. p. 162896, col. 1, para. 2, abstract). Yazdi et al. taught that different DNA blocks/consensus sequences have different observed error characteristics (Table 2). Therefore, one of ordinary skill in the art would have been motivated to apply the error-rate-dependent LLR calculation of Chandak et al. and Lu et al. using the error information associated with the particular block/segment being decoded taught by Yazdi et al. (rather than applying a single average error statistic to all segments) because doing so would more accurately reflect the reliability of the corresponding consensus information. Furthermore, one of ordinary skill in the art would predict that the DNA segment-specific error analysis taught by Yazdi et al. could be readily added to the method of Chandak et al. and Lu et al. with a reasonable expectation of success because they both pertain to detecting and correcting errors in using DNA as storage media. The invention is therefore prima facie obvious.
Regarding claim 2, Chandak et al. discloses using Kalign 2 to perform multiple sequence alignment to calculate consensus information. As evidenced by Lassman et al. Kalign 2 multiple sequence alignment algorithm uses the HOXD substitution matrix (Lassman et al. p. 863, col. 1, para. 2). As evidenced by Chiaramonte et al. The equation for the ratios of aligned (homologous) to misaligned (chance) nucleotide pairs for the HOXD substitution matrix (and other substitution matrices) is defined by the fundamental log-odds ratio formula used in sequence alignment (Chiaramonte et al. p. 116, para. 5 – p. 118, para. 1). Inherent to this equation is the ratio of aligned to misaligned pairs (the odds ratio) (p. 151, col. 2, para. 4; the method of claim 1, wherein the first consensus information comprises a determination of a ratio of DNA symbols in agreement between the first DNA segment of the first copy of the DNA sequence and the first DNA segment of the second copy of the DNA sequence).
With respect to claim 3, Chandak et al. discloses performing multiple sequence alignments in determining consensus, which involves using length discrepancies/gaps between multiple copies to perform sequence alignment and derive consensus information. Chandak et al. further discloses that if the consensus sequence does not have the correct length, they attempt to recover part of the oligonucleotide using the synchronization marker. For example, if the synchronization marker is shifted left by 1 base, then they retain only the right half of the sequence and consider the left half as an erasure for the next step in the decoding (p. 151, col. 2, para. 4 – p. 145, col. 1, para. 1; the method of claim 1, wherein the first consensus information comprises length information of the first DNA segment of the first copy of the DNA sequence compared to length information of the first DNA segment of the second copy of the DNA sequence).
Regarding claim 4, there is no limiting definition of the term “reliability bin” in the specification, so under broadest reasonable interpretation, the term will be interpreted to mean any reliability score of the DNA sequence.
Pertaining to claim 4, Chandak et al. discloses that the multiple sequence alignment step provides the counts of 0's and 1's for each position in the oligonucleotide, and rather than using just the consensus sequence, they utilize the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model. Chandak et al. further discloses associating the substitution error rate with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
n
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
n
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. The sum k0 + k1 is then the total number of used reads of the sequence, which the authors model as a Poisson-distributed random variable. As further evidenced by Heinis et al., the disagreements between the strands are modelled by Chandak et al. to result from transmitting each of the k0 + k1 sequences over a binary symmetric channel with a predefined substitution probability ϵ (ϵ is set to 0.04 in the experiments). Under these assumptions, Chandak et al. derive a formula for channel log-likelihood ratios and use them as input to belief propagation decoding of the LDPC code. The equation shown above is equivalent to the equation shown in para. 0053 of the specification of the instant application describing the relationship between LLR and error rate in one embodiment (Chandak et al. p. 152, para. 2; Heinis et al. p. 24, para. 3- p. 25, para. 1; the method of claim 1, further comprising associating a reliability bin with a log likelihood ratio (LLR), the log likelihood ratio being associated with the first DNA segment of the first copy of the DNA sequence).
Pertaining to claim 6, according to the specification, an “offline” environment includes performing the operation is performed for example in a laboratory, whereas “online” is when the DNA-based storage system is being used in the field (para. 0009).
Regarding claim 6, Chandak et al. discloses that all experiments were done on a server with 40-core Intel Xeon processor (2.20GHz) and 256 GB RAM (p. 155, col. 2, para. 2; the method of claim 4, wherein the log likelihood ratio is determined in an offline environment).
Regarding claim 7, the specification recites that in one example, updating of the reliability characteristic values is a refining of the initial memory error model for a given read codeword that may be different that the initially assumed memory error model (para. 0086).
Pertaining to claim 7, Chandak et al. and Lu et al. do not specifically disclose updating the LLR by repeating the calculation, but it would be obvious to try because Lu et al. discloses that efficient data duplication of DNA storage based on PCR is needed and the error rates during DNA synthesis and sequencing are high. One of ordinary skill in the art could have pursued updating the LLRs with reasonable expectation of success because Lu et al. shows that since their error rates are based on observed error statistics, the error rates (SE) (and thus also LLRs) are different with different experimental datasets. These data show that repeating or updating the error analysis (and LLR calculation) after data duplication by PCR would yield new values as the error is based on the newly generated data (p. 162892, col. 1, para. 1 – col. 2, para. 2; Table 12; the method of claim 4, further comprising updating the log likelihood ratio associated with the first DNA segment of the first copy of the DNA sequence).
Regarding claim 8, Chandak et al. discloses that rather than using just the consensus sequence, their method utilizes the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model (p. 152, col. 1, para. 2; The method of claim 1, further comprising providing the soft information to a low-density parity-check (LDPC) decoder).
Regarding claim 5, Chandak et al. is silent to the method of claim 4, wherein the log likelihood ratio associated with the first DNA segment of the first copy of the DNA sequence is determined based, at least in part, on the first bit error rate. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Lu et al.
Pertaining to claim 5, Lu et al. discloses using the same LLR formula developed by Chandak et al. but improving it by making the LLR calculation based on observed error statistics (including scaling the LLR itself and scaling pairwise substitution error rates), thus in the method of Lu et al. the error rate is determined from the observed DNA data. Specifically, Lu et al. discloses that LDPC codes and substitution error rate are provided to assign proper scaling values in LLR calculations. Lu et al. further discloses the linear relationship between the LLR scaling coefficient (alpha) and the error rate (SE) (p. 162893, col. 1, para. 2 – para. 4; p. 162899, col. 1, para. 2; Figs. 5 and 8; the method of claim 4, wherein the log likelihood ratio associated with the first DNA segment of the first copy of the DNA sequence is determined based, at least in part, on the first bit error rate).
11. Claims 10-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chandak et al. (2019 5th Annual Allerton conference on communication, control and computing, Monticello, IL, USA, September 24-27, 2019, p. 147-157), as evidenced by Lassman et al. (Nucleic Acids Research 2009, Vol. 37, p. 858-865), Heinis et al. (ACM Computing Surveys, Vo. 56, p. 1-30) and Chiaromonte et al. (Pacific Symposium on Biocomputing, 2002, Vol. 7, p. 115-126), as applied to claims 9, 17 and 20 above, in view of Lu et al. (IEEE Access, 2020, Vol. 8, p. 162892-162993) and Yazdi et al. (2017, Scientific Reports, Vol. 7, p. 1-36). The italicized text corresponds to the instant claim limitations.
The limitations of claims 9, 17 and 20 were taught by Chandak et al. above.
Pertaining to claim 10, Chandak et al. is silent to the DNA-based storage system of claim 9, wherein generating soft information comprises determining a bit error rate (BER) associated with [the] DNA segments. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Lu et al.
Pertaining to claim 10, Lu et al. discloses using the same LLR formula developed by Chandak et al. but improving it by making the LLR calculation based on observed error statistics (including scaling the LLR itself and scaling pairwise substitution error rates), thus in the method of Lu et al. the error rate is determined from the observed DNA data. Specifically, Lu et al. discloses that LDPC codes and substitution error rate are provided to assign proper scaling values in LLR calculations. Lu et al. further discloses the linear relationship between the LLR scaling coefficient (alpha) and the error rate (SE) (p. 162893, col. 1, para. 2 – para. 4; p. 162899, col. 1, para. 2; Figs. 5 and 8; the DNA-based storage system of claim 9, wherein generating soft information comprises determining a bit error rate (BER) associated with the multiple DNA segments).
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. Lu et al. taught that due to the mismatch between the real channel and the observed statistics and also the limit of maximum decoder input value, scaling the magnitude of the log likelihood ratio (LLR) can lead to a better error correcting performance (abstract). Therefore, one of ordinary skill in the art would have been motivated to utilize the improved LLR processing schemes based on observed statistics for low-density parity-check (LDPC) code decoding to improve error correcting performance. Furthermore, one of ordinary skill in the art would predict that the data and data acquisition methods taught by Lu et al. could be readily added to the system of Chandak et al. with a reasonable expectation of success because they both pertain to DNA-based data storage using LDPC codes. Furthermore, Lu et al. discloses using similar encoding, multiple sequence alignment (MSA), and decoding as Chandak et al. in their method. The invention is therefore prima facie obvious.
Regarding claim 10, Chandak et al. and Lu et al. are silent to wherein a bit error rate (BER) is associated with each of the multiple DNA segments. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Yazdi et al.
Regarding claim 10, Yazdi et al. teaches a DNA-based storage system with error detection and correction. Yazdi et al. further disclose that raw images were compressed, encoded and synthesized into DNA blocks (i.e. segments) and in one implementation there were 17 DNA blocks out of which 16 blocks were of length 1,000 bp and one single block was of length 880 bp. Yazdi et al. further teaches determining the number of each type of error (substitutions, insertions and deletions) for each block (both per consensus and per read average) and that the error rate can be very different depending on the DNA block/segment (Fig. 3; p. 4, para. 2; Table 2; wherein a bit error rate (BER) is associated with each of the multiple DNA segments).
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. Chandak et al. and Lu et al. taught that accurate error-rate information improves the reliability of LLR soft information used by the LDPC decoder (Lu et al. p. 162896, col. 1, para. 2, abstract). Yazdi et al. taught that different DNA blocks/consensus sequences have different observed error characteristics (Table 2). Therefore, one of ordinary skill in the art would have been motivated to apply the error-rate-dependent LLR calculation of Chandak et al. and Lu et al. using the error information associated with the particular block/segment being decoded taught by Yazdi et al. (rather than applying a single average error statistic to all segments) because doing so would more accurately reflect the reliability of the corresponding consensus information. Furthermore, one of ordinary skill in the art would predict that the DNA segment-specific error analysis taught by Yazdi et al. could be readily added to the method of Chandak et al. and Lu et al. with a reasonable expectation of success because they both pertain to detecting and correcting errors in using DNA as storage media. The invention is therefore prima facie obvious.
Regarding claims 11-14, Chandak et al. is silent to: the DNA-based storage system of claim 10, wherein the control system is further operable to generate a log likelihood ratio (LLR) associated with each of the multiple DNA segments based, at least in part, on the determined bit error rate associated with each of the multiple DNA segments (claim 11); the DNA-based storage system of claim 11, wherein the control system is further operable to update the log likelihood ratio associated with each of the multiple DNA segments (claim 12); the DNA-based storage system of claim 10, wherein the control system is further operable to associate a reliability bin to each of the multiple DNA segments (claim 13); the DNA-based storage system of claim 13, wherein the control system is further operable to assign a particular log likelihood ratio (LLR) to a particular reliability bin (claim 14). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Lu et al. and Yazdi et al.
Pertaining to claim 11, Lu et al. discloses associating the error probability with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
o
g
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
o
g
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. Li et al. discloses that they do not assume any specified channel model in computing LLR and instead compute LLR using the ratio of occurrences of each base obtained from the experiments using one of two methods: 1) directly scaling LLRs (by multiplying by a scaling coefficient which correlates with the substitution error rate and 2) scaling pairwise substitution error rates, which changes the magnitude of LLRs (abstract; p. 62894, col. 2, para. 2; p. 162896, col. 1, para. 3- p. 162897, col. 1, para. 4; Fig. 5 and 8; the DNA-based storage system of claim 10, wherein the control system is further operable to generate a log likelihood ratio (LLR) associated with each of the multiple DNA segments based, at least in part, on the determined bit error rate associated with each of the multiple DNA segments).
Regarding claim 12, Lu et al. does not specifically disclose updating the LLR by repeating the calculation, but it would be obvious to try because Lu et al. discloses that efficient data duplication of DNA storage based on PCR is needed and the error rates during DNA synthesis and sequencing are high. One of ordinary skill in the art could have pursued updating the LLRs with reasonable expectation of success because Lu et al. shows that since their error rates are based on observed error statistics, the error rates (SE) (and thus also LLRs) are different with different experimental datasets. These data show that repeating or updating the error analysis (and LLR calculation) after data duplication by PCR would yield new values as the error is based on the newly generated data (p. 162892, col. 1, para. 1 – col. 2, para. 2; Table 12; the DNA-based storage system of claim 11, wherein the control system is further operable to update the log likelihood ratio associated with each of the multiple DNA segments.
Regarding claim 13, there is no limiting definition of reliability bin in the specification. According to para. 0054, a reliability bin may correspond to a particular DNA segment and have a corresponding percentage or reliability (or bit error rate). Therefore, claim 13 is interpreted to comprise assigning an error rate to a DNA segment.
With respect to claim 13, Yazdi et al. further teaches determining the number of each type of error (substitutions, insertions and deletions) for each block/segment (both per consensus and per read average) and that the error rate can be very different depending on the DNA block/segment (Fig. 3; p. 4, para. 2; Table 2; the DNA-based storage system of claim 10, wherein the control system is further operable to associate a reliability bin to each of the multiple DNA segments).
Regarding claim 14, Lu et al. discloses associating the error probability with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
o
g
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
o
g
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. Li et al. discloses that they do not assume any specified channel model in computing LLR and instead compute LLR using the ratio of occurrences of each base obtained from the experiments using one of two methods: 1) directly scaling LLRs (by multiplying by a scaling coefficient which correlates with the substitution error rate and 2) scaling pairwise substitution error rates, which changes the magnitude of LLRs (abstract; p. 62894, col. 2, para. 2; p. 162896, col. 1, para. 3- p. 162897, col. 1, para. 4; Fig. 5 and 8; the DNA-based storage system of claim 13, wherein the control system is further operable to assign a particular log likelihood ratio (LLR) to a particular reliability bin).
Pertaining to claim 15, according to the specification, an “offline” environment includes performing the operation is performed for example in a laboratory, whereas “online” is when the DNA-based storage system is being used in the field (para. 0009).
Regarding claim 15, Chandak et al. discloses that all experiments were done on a server with 40-core Intel Xeon processor (2.20GHz) and 256 GB RAM (p. 155, col. 2, para. 2; the DNA-based storage system of claim 10, wherein the control system is further operable to determine an initial log likelihood ratio in an offline environment).
Regarding claim 16, Chandak et al. discloses that rather than using just the consensus sequence, their method utilizes the counts for LDPC decoding by converting the counts into log-likelihood ratios (LLR) using an appropriate probabilistic error model (p. 152, col. 1, para. 2; The method of claim 1, further comprising providing the soft information to a low-density parity-check (LDPC) decoder). (the DNA-based storage system of claim 10, wherein the control system is further operable to provide the soft information to a low-density parity-check (LDPC) decoder associated with the DNA-based storage system).
Regarding claims 18 and 19, Chandak et al. is silent to: the DNA-based storage system of claim 17, wherein the means for generating soft information comprises means for determining a bit error rate (BER) associated with each of the DNA segments (claim 18) and the DNA-based storage system of claim 18, further comprising means for generating a log likelihood ratio associated with the DNA segments based, at least in part, on the bit error rate associated with each of the DNA segments (claim 19). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Lu et al. and Yazdi et al.
Regarding claim 18, Yazdi et al. teaches a DNA-based storage system with error detection and correction. Yazdi et al. further disclose that raw images were compressed, encoded and synthesized into DNA blocks (i.e. segments) and in one implementation there were 17 DNA blocks out of which 16 blocks were of length 1,000 bp and one single block was of length 880 bp. Yazdi et al. further teaches determining the number of each type of error (substitutions, insertions and deletions) for each block (both per consensus and per read average) and that the error rate can be very different depending on the DNA block/segment (Fig. 3; p. 4, para. 2; Table 2; the DNA-based storage system of claim 17, wherein the means for generating soft information comprises means for determining a bit error rate (BER) associated with each of the DNA segments).
Regarding claim 19, Lu et al. discloses associating the error probability with the log-likelihood ratio according to this equation:
L
L
R
k
o
,
k
1
=
l
o
g
P
(
(
k
o
,
k
1
|
0
)
P
(
(
k
o
,
k
1
|
1
)
=
k
0
-
k
1
l
o
g
1
-
∈
∈
(where 𝜖 is the substitution error rate, which was set according to the error rate after consensus and LLR is the log-likelihood ratio). As evidenced by Heinis et al., in the method of Chandak et al., k0 and k1 are the numbers of sequences that have 0 and 1 in the corresponding position after alignment respectively. Li et al. discloses that they do not assume any specified channel model in computing LLR and instead compute LLR using the ratio of occurrences of each base obtained from the experiments using one of two methods: 1) directly scaling LLRs (by multiplying by a scaling coefficient which correlates with the substitution error rate and 2) scaling pairwise substitution error rates, which changes the magnitude of LLRs (abstract; p. 62894, col. 2, para. 2; p. 162896, col. 1, para. 3- p. 162897, col. 1, para. 4; Fig. 5 and 8; the DNA-based storage system of claim 18, further comprising means for generating a log likelihood ratio associated with the DNA segments based, at least in part, on the bit error rate associated with each of the DNA segments).
Conclusion
12. No claims are allowed.
E-mail Communications Authorization
13. Per updated USPTO Internet usage policies, Applicant and/or applicant's representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300): "Recognizing that Internet communications are not secure, / hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. / understand that a copy of these communications will be made of record in the application file."
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273- 8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER J SMITH whose telephone number is (571)272-7801. The examiner can normally be reached Monday-Friday 7:00 AM - 3:00 PM.
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.
/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights