DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-19 are currently pending and under examination herein.
Claims 1-19 are rejected.
Priority
The instant application claims priority to US Provisional Application 63429949 filed 02 December 2022. In this action, claims 1-19 are examined as though they had an effective filing date of 02 December 2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01 March 2024 and 08 May 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 01 December 2023 are accepted.
Claim Rejections - 35 USC § 112
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.
Claim 1-19 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claim 1 recites memory storing computer-executable instructions and processors in communication with the memory, wherein the processor is configured to execute the computer-executable instructions to sequence the genetic data for the first reference population of cells.
Claim 6, which depends from claim 1, recites wherein the processor is configured to execute the computer-executable instructions to sequence the genetic data using bulk DNA sequencing.
Claim 10 recites the method comprising memory storing computer-executable instructions and processors in communication with the, wherein the processor is configured to execute the computer-executable instructions to sequencing the genetic data.
Claim 15 recites the computer-implemented method of claim 10, the method comprises sequencing the genetic data using bulk DNA sequencing.
Claim 19 recites non-transitory computer-readable storage media having computer-executable instructions, wherein when executed by a processor, the computer-executable instructions cause the at least one processor to sequence the genetic data.
It is unclear from the specification and not described in the specification how a computer (which includes memory and processors) is able to accomplish DNA sequencing or bulk DNA sequencing as recited by the claims. Paragraph 0168 of the published disclosure (US 20240185953 A1) defines “genetic data” as information regarding the genetic material present in a cell. Paragraph 0161 of the published disclosure defines “bulk DNA sequencing” as sequencing DNA derived from a population of cells. Therefore, the claims are interpreted as encompassing both generating sequence or read data from cells (using a sequencer) and manipulating data previously generated by sequencing. If some structure, other than just a computer (or memory and processors), is doing the sequencing (generating data from cells), that information/structure should be incorporated into the claims, and would overcome the 112(a) rejection. Claims 2-9, which depend on claim 1, and claims 11-18, which depend on claim 10, thus contain the above issues due to said dependence.
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.
Claims 1-19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 10 recite identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data. It is unclear from this limitation if “to generate second reference data” is referring to “the first reference data” with the variant positions removed or “single cell RNA sequencing data”. Adding punctuations to the sentence may enhance its clarity, and would overcome the rejection if the “first reference data set” was clearly described by the claims. Claims 2-9, which depend on claim 1, and claims 11-18, which depend on claim 10, thus contain the above issues due to said dependence.
Claims 1, 10, and 19 recite a limitation “map identifiers for each cell of the target population of cells to the second reference data” and another limitation “map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data”. It is unclear if the “identifiers” mapped in these limitations are the same. Examples of identifiers in the specification include only unique molecular identifiers (UMIs) or the limitations recited by the claims. However, it is unclear if the all the identifiers recited by the claims are UMIs (see 112(b) rejection for claims 8 and 17 below) or if they are linked to the identifying step associated with the single cell RNAseq (“identify and remove variant positions”). The metes and bounds of the limitation are unclear rendering the claims indefinite. This rejection may be overcome by specifying what the identifiers are, what their source is, or if they are created by an already recited step. Claims 2-9, which depend on claim 1, and claims 11-18, which depend on claim 10, thus contain the above issues due to said dependence.
Claims 8 and 17 recite wherein the identifiers comprise UMIs. Claims 1 and 10, from which claims 8 and 17 depend, recite identifiers, first mapped identifiers, and second mapped identifiers, and mapped identifiers. It is unclear which the identifies in claims 1 and 10 comprise UMIs. The metes and bounds of the limitation are unclear rendering the claims indefinite. Addressing, the previous 112(b) rejection may overcome this rejection by clearly establishing the nature of the identifiers within the independent claims.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 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 or natural law (Step 2A, Prong 1). Claims 1-9 and 19 are directed to systems and Claims 10-18 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 recites the limitation - identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data; map identifiers for each cell of the target population of cells to the second reference data to generate first mapped identifiers; map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; predicting the prevalence of LOH in the target population of cells; and update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs. Based on the broadest reasonable interpretation, identifying, mapping, and updating data and predicting a prevalence could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites establish a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; apply one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population of cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and re-train the model using the updated historical data. Based on the broadest reasonable interpretation, determining position based on distance and using and training generic machine learning models encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 2 recites the limitation - wherein the target population of cells comprise genome-edited cells. This limitation specifies the information of the mapping judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 3 recite the limitation - wherein the genome edited cells comprise CRISPR-edited cells. This limitation specifies the information of the mapping judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 4 recite the limitation - wherein the target population of cells comprise cancer cells. This limitation specifies the information of the mapping judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 7 recites the limitation - wherein the second reference population of cells comprise the cells untreated with genome-editing tools. This limitation specifies the information of the mapping and identifying judicial exceptions of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 8 recites the limitation - wherein the identifiers comprise UMIs. This limitation specifies the information of the mapping judicial exceptions of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 9 recites the limitation - wherein the model comprises a logistic regression model. Based on the broadest reasonable interpretation, using logistic regression within the generic machine learning modeling encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - identifying and removing heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data; mapping identifiers for each cell of the target population of cells to the second reference data to generate first mapped identifiers; mapping identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; predicting the prevalence of LOH in the target population of cells; and updating the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs. Based on the broadest reasonable interpretation, identifying, mapping, and updating data and predicting a prevalence could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites establishing a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; applying one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population f cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and re-training the model using the updated historical data. Based on the broadest reasonable interpretation, determining position based on distance and using and training generic machine learning models encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - wherein the target population of cells comprise genome-edited cells. This limitation specifies the information of the mapping judicial exception of claim 10. The refined limitation indicated still represents a judicial expectation.
Claim 12 recites the limitation - wherein the genome-edited cells comprise CRISPR-edited cells. This limitation specifies the information of the mapping judicial exception of claim 10. The refined limitation indicated still represents a judicial expectation.
Claim 13 recites the limitation - wherein the target population of cells comprise cancer cells. This limitation specifies the information of the mapping judicial exception of claim 10. The refined limitation indicated still represents a judicial expectation.
Claim 16 recites the limitation - wherein the second reference population of cells comprise cells untreated with genome-editing tools. This limitation specifies the information of the mapping and identifying judicial exceptions of claim 10. The refined limitation indicated still represents a judicial expectation.
Claim 17 recites the limitation - wherein the identifiers comprise UMIs. This limitation specifies the information of the mapping judicial exceptions of claim 10. The refined limitation indicated still represents a judicial expectation.
Claim 18 recites the limitation - wherein the model comprises a logistic regression model. Based on the broadest reasonable interpretation, using logistic regression within the generic machine learning modeling encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 19 recites the limitation - identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data to generate second reference data; map identifiers for each cell of a target population of cells to the second reference data to generate first mapped identifiers; map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; predicting the prevalence of LOH in the target population of cells; and update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs. Based on the broadest reasonable interpretation, identifying, mapping, and updating data and predicting a prevalence could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites establish a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; apply one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population of cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and re-train the model using the updated historical data. Based on the broadest reasonable interpretation, determining position based on distance and using and training generic machine learning models encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of identifying, mapping, predicting, and determining information in conjunction with using and training generic machine learning models that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. A mathematical concept need not be expressed in mathematical symbols, because words used in a claim operating on data to solve a problem can serve the same purpose as a formula (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-19 recite an abstract idea (Step 2A, Prong 1: YES).
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). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites at least one memory storing computer-executable instructions; and at least one processor in communication with the at least one memory, wherein the at least one processor is configured to execute the computer-executable instructions to: receive genetic data for a first reference population of cells; sequence the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; receive one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells.
Claim 5 recites wherein the first reference population of cells comprise the same genotype as wild-type cells of the first reference population of cells.
Claim 6 recites wherein the at least one processor is configured to execute the computer-executable instructions to sequence the genetic data using bulk DNA sequencing.
Claim 10 recites at least one memory storing computer-executable instructions; and at least one processor in communication with the at least one memory, wherein the at least one processor is configured to execute the computer-executable instructions to: receiving genetic data for a first reference population of cells; sequencing the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; and receiving one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells.
Claim 14 recites wherein the first reference population of cells comprise the same genotype as wild-type cells of the first reference population of cells.
Claim 15 recites the method comprises sequencing the genetic data using bulk DNA sequencing.
Claim 19 recites At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: receive genetic data for a first reference population of cells; sequence the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; and receive one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells.
There are no limitations that indicate some of the claimed identifying, mapping, predicting, and determining information in conjunction with using and training generic machine learning models require anything other than generic computing systems. 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. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-19 are directed to an abstract idea (Step 2A, Prong 2: NO).
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 conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed identifying, mapping, predicting, and determining information in conjunction with using and training generic machine learning models require anything other than generic computer components in order to carry out the recited abstract idea 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 or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Bartha et al. (2019, Cancers, Vol. 11: 1-20) teach the use of computer (Page 7, Table 2) to receive and utilize sequencing data (Page 3, Figure 1), including the generation of sequencing data through bulk sequencing (Page 11, Paragraph 7: technical development of bulk sequencing); and utilizing the output of machine learning models (Page 6, Paragraph 4: The application of machine learning ensemble methods has become increasingly accepted) is well understood, routine, and conventional at the time of the effective filing date.
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 1-19 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Perera (US 20200258597 A1, IDS from 08 May 2024), in view of Williams et al. (2021, bioRxiv, 1-46, IDS from 08 May 2024), and in further view of Venkat et al. (US 20200255909 A1). Italicized text from reference art.
Applicable claims include:
Claim 1. A system for predicting a prevalence of loss of heterozygosity (LOH) in a target population of cells, the system comprising: at least one memory storing computer-executable instructions; and at least one processor in communication with the at least one memory, wherein the at least one processor is configured to execute the computer-executable instructions to: (Claim 1.i) receive genetic data for a first reference population of cells; (Claim 1.ii) sequence the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; (Claim 1.iii) identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data; (Claim 1.iv) establish a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; (Claim 1.v) map identifiers for each cell of the target population of cells to the second reference data to generate first mapped identifiers; map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; (Claim 1.vi) apply one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population of cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and (Claim 1.vii) receive one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells, thereby predicting the prevalence of LOH in the target population of cells; (Claim 1.viii) update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs; and (Claim 1.ix) re-train the model using the updated historical data.
Claim 2. The system of claim 1, wherein the target population of cells comprise genome-edited cells.
Claim 3. The system of claim 2, wherein the genome edited cells comprise CRISPR-edited cells.
Claim 4. The system of claim 1, wherein the target population of cells comprise cancer cells.
Claim 5. The system of claim 1, wherein the first reference population of cells comprise the same genotype as wild-type cells of the first reference population of cells.
Claim 6. The system of claim 1, wherein the at least one processor is configured to execute the computer-executable instructions to sequence the genetic data using bulk DNA sequencing.
Claim 7. The system of claim 1, wherein the second reference population of cells comprise the cells untreated with genome-editing tools.
Claim 8. The system of claim 1, wherein the identifiers comprise UMIs.
Claim 9. The system of claim 1, wherein the model comprises a logistic regression model.
Claim 10. A computer-implemented method for predicting a prevalence of loss of heterozygosity (LOH) in a target population of cells, the method comprising: at least one memory storing computer-executable instructions; and at least one processor in communication with the at least one memory, wherein the at least one processor is configured to execute the computer-executable instructions to: (Claim 10.i) receiving genetic data for a first reference population of cells; (Claim 10.ii) sequencing the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; (Claim 10.iii) identifying and removing heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data; (Claim 10.iv) establishing a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; (Claim 10.v) mapping identifiers for each cell of the target population of cells to the second reference data to generate first mapped identifiers; mapping identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; (Claim 10.vi) applying one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population f cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and (Claim 10.vii)receiving one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells, thereby predicting the prevalence of LOH in the target population of cells; (Claim 10.viii) updating the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs; and (Claim 10.ix) re-training the model using the updated historical data.
Claim 11. The computer-implemented method of claim 10, wherein the target population of cells comprise genome-edited cells.
Claim 12. The computer-implemented method of claim 11, wherein the genome-edited cells comprise CRISPR-edited cells.
Claim 13. The computer-implemented method of claim 10, wherein the target population of cells comprise cancer cells.
Claim 14. The computer-implemented method of claim 10, wherein the first reference population of cells comprise the same genotype as wild-type cells of the first reference population of cells.
Claim 15. The computer-implemented method of claim 10, the method comprises sequencing the genetic data using bulk DNA sequencing.
Claim 16. The computer-implemented method of claim 10, wherein the second reference population of cells comprise cells untreated with genome-editing tools.
Claim 17. The computer-implemented method of claim 10, wherein the identifiers comprise UMIs.
Claim 18. The computer-implemented method of claim 10, wherein the model comprises a logistic regression model.
Claim 19. At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: (Claim 19.i) receive genetic data for a first reference population of cells; (Claim 19.ii) sequence the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof; (Claim 19.iii) identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data to generate second reference data; (Claim 19.iv) establish a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data; (Claim 19.v) map identifiers for each cell of a target population of cells to the second reference data to generate first mapped identifiers; map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers; (Claim 19.vi) apply one or more inputs to a supervised machine learning model, the one or more inputs comprising the first mapped identifiers for each cell of the target population of cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH; and (Claim 19.vii) receive one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells, thereby predicting the prevalence of LOH in the target population of cells; (Claim 19.viii) update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs; and (Claim 19.ix) re-train the model using the updated historical data.
Regarding Claims 1, 10, and 19, Perera teaches (Claim 1.i) receive genetic data for a first reference population of cells (Paragraph 0052: FASTQ data obtained from the NGS Lab process). Perera teaches (Claim 1.ii) sequence the genetic data for the first reference population of cells to obtain first reference data, the first reference data comprising chromosome identification, nucleotide coordinates, nucleotide composition, or combinations thereof (Paragraph 0036: initially receiving genetic material (DNA or RNA) isolated from a patient specimen and sequenced, for example, using a NGS technique; Paragraph 0053: coordinates in the reference genome). Perera suggests (Claim 1.iii) identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data (Paragraph 0045: The sequence of nucleotides in the adapters may be specific to a sample in order to distinguish samples; Paragraph 0052: removing overrepresented sequences; Paragraph 0118: By performing variant calling against the initial HLA reference, positions where the NGS data does not support the initial chosen reference can be identified; Paragraph 0127: Tumor samples are generally heterogeneous and contain a mixture of tumor cells, healthy stroma and immune cells; Paragraph 0166: RNA expression data for each sample; Paragraph 0167: identifying common expression characteristics shared across RNA gene expressions; Paragraph 0178: The genetic profile may represent genetic sequences present in the tissue type, tumor, or organ and may include variants, expression levels). Perera teaches (Claim 1.iv) establish a set of homozygous positions that are a predetermined number of nucleotide distances away from each variant position of the second reference data to generate third reference data (Paragraph 0060: use reads mapped to the microsatellite loci with at least five, ten, fifteen, etc. bp flanking the microsatellite region; Paragraph 0130: adjusting LOH predictions based on the predictions of the neighboring HLA genes). Perera teaches (Claim 1.v) map identifiers for each cell of the target population of cells to the second reference data to generate first mapped identifiers; map identifiers for each cell of the second reference population of cells to the second reference data and/or the third reference data to generate second mapped identifiers (Paragraph 0075: positions indicating locations in the reference genome of mapped reads having a sequence that map to the reference genome is determined; Paragraph 0076: reads that map to either the original reference HLA or any known HLA genetic sequence; Paragraph 0077: reads are mapped against a carefully constructed HLA allele reference). Perera teaches (Claim 1.vi) apply one or more inputs to a supervised machine learning model, inputs comprising the first mapped identifiers for each cell of the target population of cells and the second mapped identifiers for each cell of the second reference population of cells, the model being previously trained using historical data, the historical data comprising mapped identifiers for each cell of the target population of cells and their corresponding LOH (Paragraph 0098: features are input into a machine learning classification model that returns a likelihood of LOH; Paragraph 0166: a machine learning algorithm (MLA) may be trained from a training data set). Perera teaches (Claim 1.vii) receive one or more outputs from the model, at least one of the one or more outputs including an LOH for the target population of cells (Paragraph 0098: features are input into a machine learning classification model that returns a likelihood of LOH). Perera teaches (Claim 1.viii) update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs (Paragraph 0087: updates the patient specific reference by replacing portions of the reference sequences with the variant sequences). Additionally, Perera teaches the methods are performed by a computing system, which include memory, processors, and computer readable media (Paragraphs 133-138). Claim 10 recites the limitations of claim 1 directed to a computer based method. Claim 19 recite the limitations of claim 1 directed to a computer readable medium.
Regarding Claims 2 and 11, Perera suggests the target population of cells comprise genome-edited cells (Paragraph 0065: an organoid may be genetically engineered to have the same HLA alleles present as a patient).
Regarding Claims 4 and 13, Perera teaches the target population of cells comprise cancer cells (Paragraph 0054: a cancer sample).
Regarding Claims 5 and 14, Perera teaches the first reference population of cells comprise the same genotype as wild-type cells of the first reference population of cells (Paragraph 0054: a normal sample). This use is consistent with definition of wild-type from Paragraph 0177 of the published specification (i.e. unedited and not the cancer type).
Regarding Claims 6 and 15, Perera teaches sequence the genetic data using bulk DNA sequencing (Paragraph 0158: bulk DNA sequencing of the tumor).
Regarding Claims 7 and 16, Perera teaches the second reference population of cells comprise the cells untreated with genome-editing tools (Paragraph 0054: a normal sample). Normal is interpreted to indicate unedited as no editing is discussed in relation to these samples.
Regarding Claims 8 and 17, Perera teaches the identifiers comprise UMIs (Paragraph 0044: UMI adapters).
Regarding Claims 9 and 18, Perera teaches the model comprises a logistic regression model (Paragraph 0166: MLAs include supervised algorithms using logistic regression).
Perera does not explicitly teach single cell RNA sequencing or retraining the machine learning model (Claims 1, 10, and 19). Perera does not explicitly teach CRISPR-edited cells (Claims 3 and 12). Related to Perera, Williams et al. disclose computer based methods for assessing loss of heterozygosity.
Regarding Claims 1, 10, and 19, Williams et al. (Claim 1.iii) identify and remove heterozygous variant positions having imbalanced allelic expression in the first reference data based on single cell RNA sequencing data generated from a second reference population of cells to generate second reference data (Page 7, Paragraph 1: excluding losses resulting in 1 copy; Page 10, Paragraph 1: We then clustered cells based on gene expression; Page 23, Paragraph 3: barcode filtering; Page 23, Paragraph 4: We called heterozygous SNPs in the scRNAseq).
Regarding Claims 2 and 11, Williams et al. teach the target population of cells comprise genome-edited cells (Page 21, Paragraph 5: generation of engineered cell lines).
Regarding Claims 3 and 12, Williams et al. teach genome edited cells comprise CRISPR-edited cells (Page 6, Paragraph 2: using CRISPR).
Regarding Claims 8 and 17, Williams et al. teach the identifiers comprise UMIs (Page 23, Paragraph 3: UMI quantification).
Williams et al. do not explicitly teach retraining the machine learning model (Claims 1, 10, and 19). Related to Perera and Williams et al., Venkat et al. disclose computer based methods for assessing loss of heterozygosity.
Regarding Claims 1, 10, and 19, Venkat et al. teach (Claim 1.viii) update the historical data to include the genetic data for the target population of cells and the corresponding one or more outputs (Paragraph 0174: Any new features can easily be incorporated into this model, and the training set can be easily expanded for re-training and prediction). Venkat et al. teach (Claim 1.ix) re-train the model using the updated historical data (Paragraph 0174: Any new features can easily be incorporated into this model, and the training set can be easily expanded for re-training and prediction).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Williams et al. and Venkat et al. with Perera. Williams et al. teach methods for enhancing the utilizing of sequencing data to assess heterogeneity at different scales, especially as applied to cancer (Page 17, Paragraph 1-2: Schnapps uses a hidden Markov model for inference and facilitates integration with single cell RNA sequencing data. schnapps also provides an order of magnitude greater resolution than previous methods. our study shows how haplotype resolved copy number at single cell resolution can be used to infer instability rates, dissect complex structural rearrangements and identify parallel copy number events. we envisage that these approaches will enable accurate tracking and high resolution characterization of intra-tumour heterogeneity), which are features of Perera and the instant application. Relatedly, Venkat et al. teach their methods related to evaluating loss of heterozygosity and machine leaning with sequencing data improve predictive capacity, especially related to cancer patients (Paragraph 0032: The present disclosure provides systems and methods for using information derived from DNA sequencing of cancerous tissue to predict the homologous recombination status of a cancer, to improve treatment predictions and outcomes. Sequencing data from matched cancerous tissue and germline tissue are used together to improve the accuracy of the predictions; Paragraph 0172: results may be enhanced and personalized through the use of artificial intelligence), which are features of Perera and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all are within the same technical field – using sequencing data and machine learning algorithms to investigate genetic heterogeneity and its relation to cancer.
Double Patenting
No double patenting rejections are issued.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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