Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending.
Claims 1-20 have been examined.
Claims 1-20 are rejected.
Priority
The present application claims priority to Provisional application 63325685 filed on 31 March 2022. As such, the effective filing date of the present application is 31 March 2022.
Information Disclosure Statement
The IDS filed on 16 September 2024 has been considered by the examiner.
Drawings
The drawings filed 31 March 2023 are accepted.
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 17 and 18 is 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 “all possible” in claim 17 is a relative term which renders the claim indefinite. The term “all possible” 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. The claim language does not clearly define the maximum number of trees that can be generated by the software package. The specification repeats usage of the phrase “all possible” in reference to generating trees [0019, 0140] but does not provide a specific definition for this phrase. As such, the claim is indefinite.
The term “substantially entire” in claim 18 is a relative term which renders the claim indefinite. The term “substantially entire” 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. The claim language does not clearly define the size of the desired polynucleotide. It is unclear what is meant by a “substantially” entire vector or genome. While the specification of the instant application states an example embodiment could be a polynucleotide of at least about 100 kb, this does not provide sufficient definition of the claim terminology. [0020] As such, the claim is indefinite.
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 1 and 10 is 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 claims 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.
Claim 1 recites that “the in silico graph structure is programmed for selecting an optimal combination of branches and vertices that specify the desired sequence and for directing the assembly of the polynucleotide molecule with the desired sequence.” However, the specification discloses “inputting a desired polynucleotide sequence into a computer system, e.g., as an in silico graph structure comprising a plurality of branches, each of which specifies a linear order of nucleic acids.” [0013] The specification is referring to the in silico graph structure as an exemplary way to input desired polynucleotides into a computer system, but not how it is “programmed for selecting an optimal combination of branches that specify the desired sequence and for directing the assembly of the polynucleotide molecule with the desired sequence.” [0013] The specification further states regarding the in silico graph that “[p]referably the branches correspond to subsets of the polynucleotide or oligos provided in separate compartments, such as wells of multi-well plates” and that it “may be resident in a computer system comprising program instructions executable to cause the system to: generate a plurality of trees, each tree representing an ordered combination of branches, resulting in the desired polynucleotide sequence; select the tree that provides the optimal combination of branches; and direct the assembly of the desired polynucleotide sequence using the selected tree structure.” [0013] These descriptions indicate a graph structure based on poly- and oligo-nucleotides that provides instruction to a computer system that will execute the selection process based on the information in said graph. Similar statements are found in [0017] of the specification that again describe the in silico graph structure as branches with linear orders of nucleic acids utilized by a computer system programmed for selecting optimal combinations. The specification further discloses that “software systems can automatically evaluate, compare, or score trees...[and]… can read a tree and execute programmed instructions to direct the operation of automated hardware, such as a liquid handling system in a laboratory,” but does not disclose how the in silico graph structure would directly be programmed to make selections or direct assembly of desired polynucleotide molecules. Although it is clear from the specification that there are algorithms involved in the scoring and selection of optimal combinations and the direction of sequence synthesis, the specification fails to describe how one of ordinary skill in the art would program the in silico graph structure to select optimal combinations and direct assembly of desired sequences.
Claim 10 recites that “the in silico graph structure executes a machine learning algorithm to select said optimal combination of branches.” However, the specification discloses that the “computer system may execute a machine learning algorithm to select said optimal combination of branches.”[0016] and “a computer system may execute a machine learning algorithm to select a tree with an optimal combination of branches” [0118]. As described above, the in silico graph structure is defined by the specification as branches that specify linear orders of nucleic acids utilized by computer systems, but does not describe how this structure would utilize a machine learning algorithm to make selections. Although the specification discloses an exemplary list of machine learning algorithms [0016, 0119, 0120] for tree and branch selections, the specification does not describe how one of ordinary skill in the art would be able to have an in silico graph structure execute a machine learning algorithm to select optional combinations.
Claim 10 recites the use of a machine learning algorithm. As stated above, the specification provides an exemplary list of machine learning algorithms that could be selected for use in the invention. [0016, 0119, 0120] In addition, the specification discloses that “[t]he particular machine learning algorithm maybe selected based on the particular problem” [0119] and options for training the machine learning algorithm [0120]. The specification fails to provide a specific structure for any machine learning algorithm nor how the machine learning algorithm would be applied to the specific limitation. The disclosure is not commensurate with the written description scope of the claim. (See MPEP 2161.01(I) regarding lack of written description regarding computer algorithms.)
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 1-14 and 16-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to abstract ideas 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 abstract ideas:
Claim 1, and dependent claims 2-10, recite:
Inputting a desired polynucleotide sequence in an in silico graph structure
Selecting an optimal combination of branches and vertices
Directing assembly of desired polynucleotide sequence
Claim 3, and dependent claims 4-6, recite:
Generating a plurality of trees
Selecting tree with optimal combination of operations
Directing assembly of desired nucleotide sequence
Claim 5, and dependent claim 6, recite:
Calculating a score for each tree
Claim 7 recites:
Identifying subgroups
Generating, storing, and selecting a branch
Combining optimal branches
Claim 9 recites:
Searching the graph structure to identify local maxima
Selecting optimal branches
Claim 11, and dependent claims 12-20, recite:
Receiving sequence information
Generating a plurality of trees
Selecting one tree
Claim 13 recites:
Calculating a score for each tree
Claim 14 recites:
Scoring the tree
Claim 15 recites:
Generating and selecting to form an intermediate tree
Claim 16, and dependent claim 17, recite:
Identifying subgroups
Generating, scoring, and selecting sub-trees
Joining together trees
Claim 17 recites:
Generating trees
Applying a scoring matrix
Selecting a best-scoring tree
The limitations for the listed claims are evaluations or judgements that can be made through mental observations or mathematical calculations which fall under the “mental processes” and “mathematical concepts” groupings of abstract ideas. Under the broadest reasonable interpretation, the abstract ideas recited in the claims are determined to cover performance either in the mind (calculations by hand or pen and paper) or by mathematical operation (calculations/algorithms). See MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. V. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person's mind" (see Versata Dev. Group V. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by
humans without a computer").
While claims 3, 9, 11, 17, and 19 (and dependent claims 4-6 and 12- 20) recite performing aspects of the methods with a “computer system” and/or “software”, there are no additional limitations that indicate that the computer system or software would require 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 on generic computer components, then it falls into the “mental processes” grouping of abstract ideas. As such, claims 1-20 recite 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 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 or insignificant extra-solution activity. Specifically,
the claims recite the following additional elements:
Claim 1, and dependent claims 2-10, recite:
An in silico graph structure
Claim 2 recites:
The branches of the graph correspond to oligonucleotides or polynucleotides in separated compartments
Claim 3, and dependent claims 4-6, recite:
A computer system
Claim 4 recites:
A tree comprised of branches and nodes
Branches represent sequences
Nodes represent attachments between sequences
Claim 6 recites:
The measure of success is based in stored scores or probabilities
Claim 8 recites:
A fluid handling system
A microfluidic apparatus
Claim 9 recites:
A software package
Claims 10 recites:
A machine learning algorithm
Claim 11, and dependent claims 12-20, recite:
A computer system
A liquid handling system
Claim 12 recites:
The leaves of the trees represent oligos
The nodes of the trees represent attachments between oligos
Claim 15 recites:
Greater than 10^30 trees
Claim 17 recites:
A software package
Claim 19 recites:
A software package
A liquid handling system
The limitations for defining terms describe mental processes with additional elements. This judicial exception is not integrated into a practical application because these additional
elements do not add any meaningful limitations. The claims do not include additional elements
that are sufficient to amount to significantly more than the judicial exception because they only
describe more specificity to the types of variables.
The limitations for the in silico graph structure and machine learning algorithm describe additional elements. However, the claims fail to incorporate these elements into practical applications.
The limitations for the fluid and liquid handling systems and the microfluidic apparatus are mere instructions. While claim 8 recites the use of a fluid handling system and a microfluidic apparatus, the claim does not recite how the judicial exception is informing the making of the polynucleotide. Claim 11 recites directing a liquid handling system to make the desired polynucleotide, but the claim fails to cite a limitation that requires the synthesis of the polynucleotide.
There are no limitations that indicate that the computer system or software would require anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract ideas on a generic computer that the courts have stated do 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 above recited additional elements pertaining to claims 1-14 and 16-20 do not provide a practical application of the recited judicial exception. As such, claims 1-14 and 16-20 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 are known and commonly used techniques in the art and are mere instructions to apply the recited exception in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed
method requires more than routine use of technology. Additionally, there are no additional limitations to indicate that the program requires anything other than generic computer components in order to carry out the recited abstract ideas in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. In addition, mere display of collected and analyzed information that could be performed by the human mind do not render an abstract idea eligible. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms 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). As such, claims 1-14 and 16-20 are not patent eligible.
Claim Interpretation
For the purposes of applying prior art:
Claim 17 is interpreted as generating a large number of trees.
Claim 18 is interpreted as a polynucleotide that is an expression vector or an organismal genome.
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.
Claims 1-9, 11-14, 16, 18-20 are rejected under 35 U.S.C. 102(a)(1) and 101(a)(2) as being anticipated by Shapiro et al. (US20150252362 A1, 28 January 2015) (Herein referred to as Shapiro.)
With respect to independent claim 1 and dependent claims 2-8, Shapiro teaches a system for manufacturing a polymer [Claim 1] that includes analyzing a plurality of polynucleotide sequences to inform construction polynucleotide molecules [0017]. Shapiro further teaches the division of long sequences in silico [abstract, 0053, 0087, 0129] and the use of a hierarchical tree [0015]. Shapiro discloses graph branches that specify a linear order of nucleic acids [Figures 1, 11, and 17]. Shapiro additionally discloses determining an efficient path for combining a plurality of subcomponents [0068-9, 0072-3] and searching for an optimal protocol [0069, Claim 24].
With respect to claim 2, Shapiro teaches that nodes represent intermediate sequences [Abstract, 0130].
With respect to claim 3, and dependent claims 4-6, Shapiro teaches a computational device for operating a program for constructing said polymers [Claim 1] and the computer program processes the sequence and designs the best synthesis protocol [0129]. Shapiro further teaches repeatedly using the divide and conquer algorithm to find optimal synthesis protocols (indicating a plurality of options) [0073] and that the hierarchical construction protocol is preferably performed by a computer program that determines the optimal synthesis protocol [0138]. Additionally, Shapiro discloses that the computational device sends instructions for oligonucleotide synthesis to an appropriate synthesizer [0243]. Shapiro further discloses that the operations of liquid handling robot are controlled by a computer which is operating software according to the methods for polynucleotide synthesis based on the selection of the optimal protocol [0245].
With respect to claim 4, Shapiro teaches that nodes represent intermediate sequences and internal nodes are created in elongation reactions [Abstract, 0130]. Shapiro further teaches that each node in the tree represents a biochemical process with a product and two precursors [0092].
With respect to claim 5, and dependent claim 6, Shapiro teaches a score corresponding to its cost of production and that the program finds a minimal cost protocol [0138]. Shapiro further teaches that the program choses the best and cheapest protocol according to the scoring function taking into account many parameters such as amounts of oligos synthesis and their cost, amounts of intermediate products, hybridization yield of the desired heterodimer in each step in the protocol, rate of success in each step and reaction cost [0154].
With respect to claim 6, Shapiro teaches a database for storing a plurality of synthetic schemes for synthesizing polymers and selects synthetic schemes according to cost as well as a storage for storing a library of potentially faulty subcomponents [Claim 1]. Shapiro further teaches that the smallest cost protocol is selected as the optimal protocol and a dynamic programming algorithm is used to keep previously computed sub-protocols in a cache which are then preferably reused when needed in a different search path [0070]. Shapiro additionally teaches an iterative process to reduce error rate [0220-0221] and storage of programs to direct synthesis [0244].
With respect to claim 7, Shapiro teaches that subcomponents can include oligonucleotides [claim 20, 0067] and that constraints for the polynucleotide synthesis protocol can include selecting preferences for the number of oligonucleotides [0071]. Shapiro further teaches that determining an efficient path can include preferred methods based on the least number of subcomponents [0068]. Shapiro additionally teaches that the hierarchical construction protocol includes semi-automatically combining at least two subcomponents [claim 5, Figure 22A-D]. Shapiro shows potential exemplary subgroup lengths of fewer than about 20 oligos in Figures 25, 26A, 26B, 27A, and 30A.
With respect to claim 8, Shapiro teaches an apparatus configuration with micro-fluidics and DNA chips and that a micro-fluidic device is provided in place of a liquid handling robot [0248]. Shapiro further teaches computer control with bidirectional communication between computer and either liquid handling robot or micro-fluidic device [0249]. Shapiro discloses that this bidirectional communication provides feedback for the proper operation of the construction process and for quantity and quality control during the construction process [0249].
With respect to claim 9, Shapiro teaches a hierarchical process to search for an optimal and valid division point and then selecting an optimal set of subprocesses to construct the biopolymer and the subprocesses are collectively optimized so at least one sub-process for combining a plurality of subcomponents is selected according to one or more criteria [Claim 23]. Shapiro further teaches a branch and bound algorithm is used to trim the search space when the intermediate cost show that the current best cost for a protocol cannot be improved [0070]. These references describe methods of determining the equivalent of a local maxima by finding relative optimal points.
With respect to claim 11, Shapiro teaches a system for manufacturing a polymer [Claim 1] that includes analyzing a plurality of polynucleotide sequences to inform construction polynucleotide molecules [0017]. Shapiro further teaches a computational device for operating a program for constructing said polymers [claim 1] and the computer program processes the sequence and designs the best synthesis protocol [0129]. Shapiro further teaches repeatedly using the divide and conquer algorithm to find optimal synthesis protocols (indicating a plurality of options) [0073] and that that the hierarchical construction protocol is preferably performed by a computer program that determines the optimal synthesis protocol [0138]. Additionally, Shapiro discloses that the computational device sends instructions for oligonucleotide synthesis to an appropriate synthesizer [0243]. Shapiro further discloses that the operations of liquid handling robot are controlled by a computer which is operating software according to the methods for polynucleotide synthesis based on the selection of the optimal protocol [0245]. Shapiro additionally teaches an apparatus configuration with micro-fluidics and DNA chips and that a micro-fluidic device is provided in place of a liquid handling robot [0248]. Shapiro further teaches computer control with bidirectional communication between computer and either liquid handling robot or micro-fluidic device [0249]. Shapiro discloses that this bidirectional communication provides feedback for the proper operation of the construction process and for quantity and quality control during the construction process [0249].
With respect to claim 12, Shapiro teaches that nodes represent intermediate sequences and internal nodes are created in elongation reactions [Abstract, 0130]. Shapiro further teaches that each node in the tree represents a biochemical process with a product and two precursors [0092]. Shapiro additionally teaches oligos and attachments between oligos as parts of a tree in exemplary Figures 26A, 30A, and 30B.
With respect to claim 13, Shapiro teaches a score corresponding to the cost of production and that the program finds a minimal cost protocol [0138]. Shapiro further teaches that the program choses the best and cheapest protocol according to the scoring function taking into account many parameters such as amounts of oligos synthesis and their cost, amounts of intermediate products, hybridization yield of the desired heterodimer in each step in the protocol, rate of success in each step and reaction cost [0154].
With respect to claim 14, Shapiro teaches determining probability of success is based on the oligo length and the overlap [0095]. Shapiro further teaches dividing DNA sequences into overlapping oligonucleotides in the process of searching for an optimal library construction protocol [0225]. Shapiro additionally teaches a database for storing a plurality of synthetic schemes for synthesizing said polymers [claim 1] and an algorithm that finds a specific valid overlap suitable for synthesizing the two adjacent regions [0107]. Shapiro additionally teaches that the overlap defines the building blocks of the library and each building block is than planned using a divide & conquer algorithm [0107].
With respect to claim 16, and dependent claim 17, Shapiro teaches that subcomponents can include oligonucleotides [claim 20; 0067] and that constraints for the polynucleotide synthesis protocol can include selecting preferences for the number of oligonucleotides [0071]. Shapiro further teaches that determining an efficient path can include preferred methods based on the least number of subcomponents [0068]. Shapiro additionally teaches that the hierarchical construction protocol includes semi-automatically combining at least two subcomponents [claim 5, Figure 22A-D]. Shapiro shows potential exemplary subgroup lengths of fewer than about 20 oligos in Figures 25, 26A, 26B, 27A, and 30A.
With respect to claim 18, Shapiro teaches that their method could be used for synthesizing new\synthetic\artificial genomes [0016] and generating modified\new\artificial genomes [0120].
With respect to claim 19, Shapiro teaches a laboratory liquid handling robot and a program that translates the D&C-DNA synthesis protocol to the script language of the robot [0180]. Shapiro further teaches that the robot performs all liquid handing operations, reaction incubation, and sample collection for quality control [0180]. Shapiro additionally teaches the operations of the liquid handling robot are controlled by a computer operating software for polynucleotide synthesis and the computer receives information regarding the polynucleotide sequence(s) to be constructed [0245].
With respect to claim 20, Shapiro teaches intermediate trees or subgroups from different branches that are connected to the tree by differing numbers of nodes [Figures 2 and 30A]. The instant application defines a leaf as an intermediate tree or subgroup [0019, 0130].
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shapiro as applied to claims 1-9, 11-14, 16, 18-20, and in further view of Halper et al. (IDS NPL reference 2, ACS Synthetic Biology, 19 June 2020, pages 1563-1571) (Herein referred to as Halper.)
With respect to claim 10, Shapira teaches all the limitations of claim 1 as described above. Shapiro does not teach the in silico graph structure executes a machine learning algorithm to select optimal combinations of branches.
Halper teaches a machine learning model to predict whether a long DNA fragment can be readily synthesized. [Abstract] Halper further teaches an automated train−validate−test pipeline to develop a random forest classifier and the finalized random forest classifier is called the Synthesis Success Calculator. (Page 1564, left column, lines 3-6)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to have applied the polynucleotide synthesis method of Shapiro with the random forest algorithm of Halper. Halper discloses that turnaround time for synthetic DNA fragments is significant and is often the rate-limiting step for engineering genetic systems. (Page 1563, left column, paragraph 1) Halper further discloses faster and more reliable turnaround times would therefore greatly accelerate the engineering of genetic systems. (Page 1563, left column, paragraph 1) Halper teaches a random forest classifier because these models use an ensemble of decision trees to predict how combinations of properties (“features”) and their values determine an outcome. (Page 1565, right column, lines 13-1) Additionally, Halper teaches that random forests require fewer data points for optimal training. (Page 1565, right column, lines 16-18) Halper further teaches that the Synthesis Success Calculator is fully
transparent with a nonproprietary data set, an open-source implementation, and a model trained to prioritize researchers’ needs by limiting false positives and it includes a pipeline for automatic retraining on any inputted sequence data set. (Page 1564, left column, lines 29-34) Additionally, Halper teaches enabling researchers to identify new sequence determinants affecting synthesis outcomes and can be directly embedded within other genetic system design algorithms. (Page 1564, left column, lines 34-38) The Synthesis Success Calculator also quantifies the sequence determinants’ effects on synthesis success so that design algorithms can more easily find the
shared sequence design space where a genetic system achieves both rapid synthesis and maximal function. (Page 1564, right column, lines 1-5) Therefore, one of ordinary skill in the art would have been motivated to incorporate the machine learning algorithm of Halper with the polynucleotide synthesis methods of Shapiro. It would have been obvious to incorporate a method that would improve efficiency and optimization to a polynucleotide synthesis method. The invention is therefore prima facie obvious.
Claims 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shapiro in view of Halper and Breiman and Cutler (Random Forests webpage pdf, 19 January 2004, pages 1-24) (Herein referred to as Breiman.)
With respect to claim 15, Shapiro teaches the limitations of claim 11 as described above. Shapiro further teaches an algorithm that finds an optimal library protocol while considering intermediate products. [0107] Shapiro further teaches design of a synthesis process where each node represents an intermediate sequence, internal nodes are created in elongation reactions from their daughter nodes, and after each elongation only one DNA strand passes to the next level in the tree. [0130, Figure 2]
Shapiro does not teach there are greater than 10^30 trees that give an order of attachments among the oligos to form the nucleic acid.
Halper teaches the use of a random forest model as described above. Breiman teaches the features of random forest on the webpage https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. (cited above) Breiman discloses that “Random Forests grows many classification trees…[and]… [y]ou can run as many trees as you want.” (Page 2: Overview, lines 1-2 and page 3: Remarks, line 1)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to have applied the polynucleotide synthesis method of Shapiro with the random forest model of Halper and utilizing the known features of random forest described by Breiman. It would have been obvious to try using a random forest model to generate a large number of trees, even greater than 10^30 trees, in order to find the optimal method to assemble a desired polynucleotide sequence. Repetition of previously recited steps and/or elements would have been obvious to try with a reasonable expectation of success. (See MPEP 2143 E., Example 9) The invention is therefore prima facie obvious.
With respect to claim 17, Shapiro teaches the limitations of claims 11 and 16 as described above.
Shapiro does not teach a software package generates all possible trees; stores all of the trees in memory; applies a scoring matrix to each of the trees in memory to score all of the trees; and selects a best-scoring tree for the sub-group.
Halper teaches the use of a random forest model as described above. Breiman discloses that “Random Forests grows many classification trees…[and]… [y]ou can run as many trees as you want.” (Page 2: Overview, lines 1-2 and page 3: Remarks, line 1) Breiman further teaches utilizing matrices and scores. (Pages 4, 5, 10, and 11) Breiman additionally teaches applications of random forest in analyzing gene expression and DNA data, indicating its applicability for genomics work as early as 2004. (Pages 9-19, A Case Study-Microarray Data and A Case Study-DNA Data)
It would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to have applied the polynucleotide synthesis method of Shapiro with the random forest model of Halper. Halper teaches that trained random forest classifiers can analyze the decision trees and sequence determinant cutoffs that lead to a successful classifier. (Page 1567, left column, lines 25-6) Halper discloses they trained and validated the Synthesis Success Calculator, a random forest classifier, that predicts when a DNA fragment can be readily
synthesized with a short turnaround time while utilizing contemporary synthesis technologies that have been commercialized, optimized, and scaled-up for higher economies of scale. (Page 1568, right column, lines 10-16) Therefore, one of ordinary skill in the art would have been motivated to incorporate the random forest model of Halper with the polynucleotide synthesis methods of Shapiro. It would have been obvious to utilize known features of random forest with methods of polynucleotide synthesis to generate the maximum number of trees and use a matrix to score the trees. It would have been obvious to incorporate a method that would improve efficiency and optimization to a polynucleotide synthesis method. The invention is therefore prima facie obvious.
Conclusion
The limitation in claim 15 for greater than 10^30 trees is an additional element that cannot be done as a mental process. As such, the rejection under 35 USC 101 does not apply to claim 15.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Volkel et al teaches a DNA synthesis method using a hierarchical strand assembly algorithm to optimize oligo combinations for more efficient and economical processes. (ACM Journal on Emerging Technologies in Computing Systems, 23 March 2022, pages 53:1 – 35)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARON LEVINE GRAFF whose telephone number is (571)317-0219. The examiner can normally be reached Mon - Fri 7:30 AM - 4 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, Karlheinz Skowronek can be reached at (571) 272-9047. 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.
/S.L.G./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
.
Prosecution Insights