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 .
Claims 15-18, 21-22, 24-26, 28-32, 34-35, 37-38 are pending in this application.
Claims 1-14, 19-20, 23, 27, 31, 36, 39-126 have been canceled by preliminary amendment.
This application is a DIVISIONAL of 15/605,874, which claims priority to three US provisional applications. The pending claims correspond to Group II of the original restriction in the ‘874 parent. The examiner has reviewed the prosecution history.
This application has published as US PG-Pub 2023/0065324 A1.
The effective filing date for the pending claims appears to be that of the third provisional: 62/401,720, filed 9/29/2016 as it is the first instance reciting “determining a number of noise molecular labels with distinct sequences associated with the target in the sequencing data” and “adjusted according to the number of noise molecular labels …” as required by the pending claims. The earliest provisional 62/342,137 addresses noisy targets or genes, and removing those targets from analysis, but not “noise molecular labels” and making adjustments to the estimate “according to the number of noise molecular labels”. The ‘137 addresses true and false labels, which does not appear to be the same as the currently claimed invention. The second provisional 62/381,945 does not clearly address “noise molecular labels”.
Replacement drawings filed 11/8/2022 have been entered and are suitable for Examination.
The amendment to the specification filed 11/08/2022 has been entered.
The IDS filed 11/8/2022 has been entered and considered.
Applicant is reminded that it is desirable to avoid the submission of long lists of documents if it can be avoided. As set forth in MPEP 2004, applicant is directed to eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant's attention and/or are known to be of most significance. See Penn Yah Boats, Inc. v. Sea Lark Boats, Inc., 359 F.Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aft'd, 479 F.2d 1338, 178 USPQ 577 (5th Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d1823 (Fed. Cir. 1995). Applicant has cited more than 600 references, many of which are clearly irrelevant to the claimed invention including office actions, office actions from non-US countries, and third party protests to applications not directly related to the claimed invention. Applicant is cautioned against burying material references and the appearance of inequitable conduct in this application.
The Sequence listing and associated documents have been entered. Applicant is strongly requested to review the Specification and Drawings, to ensure the appropriate SEQ ID NO is associated with each eligible recitation therein.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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.
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 15-18, 21-22, 24-26, 28-32, 34-35, 37-38 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: Processes, and a computer system.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
15. (Original) A method for determining the numbers of targets, comprising:
(Preamble, describing a method, and the goal of the method.)
(a) stochastically barcoding a plurality of targets using a plurality of stochastic barcodes to create a plurality of stochastically barcoded targets, wherein each of the plurality of stochastic barcodes comprises a molecular label;
(EIA- an element of data gathering, performing a routine laboratory process; MPEP 2106.05(g).)
(b) obtaining sequencing data of the stochastically barcoded targets; and
(EIA- an element of data gathering, performing routine sequencing of the targets and obtaining the resulting sequence read data. MPEP 2106.05(g).)
(c) for one or more of the plurality of targets:
(i) counting the number of molecular labels with distinct sequences associated with the target in the sequencing data;
(ii) determining a number of noise molecular labels with distinct sequences associated with the target in the sequencing data; and
(iii) estimating the number of the target, wherein the number of the target estimated correlates with the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i) adjusted according to the number of noise molecular labels determined in (ii).
(Mathematic Concepts: steps of counting, and calculating estimates are mathematic calculations. MPEP 2106.04(a)(2) section I.)
16. (Original) The method of claim 15, further comprising determining a sequencing status of the target in the sequencing data.
(Mental Process: a step of observing the sequence information, and making a judgement as to sequencing status. MPEP 2106.04(a)(2)(III).)
17. (Original) The method of claim 16, wherein the sequencing status of the target in the sequencing data is saturated sequencing, under sequencing, or over sequencing.
(Mental process: a step of observing the target data, and making a judgement as to whether the data falls within the three categories. (MPEP 2106.04(a)(2)(III).)
18. (Original) The method of claim 17, wherein the saturated sequencing status is determined by the target having a number of molecular labels with distinct sequences greater than a predetermined saturation threshold.
(Mathematic concept: the concept of one number being greater than, or less than threshold value.)
21. (Currently Amended) The method of claim 17, wherein the sequencing status of the target in the sequencing data is the saturated sequencing status, and the number of noise molecular labels determined in (ii) is given a value of zero.
(Mathematic concept: providing the values required for calculating the estimation of the target.)
22. (Currently Amended) The method of claim 17, wherein the under sequencing status is determined by the target having a depth smaller less than a predetermined under sequencing threshold, wherein the depth of the target comprises an average, a minimum, or a maximum depth of the molecular labels with distinct sequences associated with the target in the sequencing data.
(Mathematic concept: the concept of one value being less than another, as well as calculating averages, etc.)
24. (Currently Amended) The method of claim 22, wherein the under sequencing threshold is independent of the number of molecular labels with distinct sequences.
(Mathematic concept: indicating data values not to be included in calculating the threshold.)
25. (Currently Amended) The method of claim 17, wherein the sequencing status of the target in the sequencing data is the under sequencing status, and the number of noise molecular labels determined in (ii) is given a value of zero.
(Mathematic concept: providing the values required for calculating the estimation of the target.)
26. (Currently Amended) The method of claim 17, wherein the over sequencing status is determined by the target having a depth greater than a predetermined over sequencing threshold, wherein the depth of the target comprises an average, a minimum, or a maximum depth of the molecular labels with distinct sequences associated with the target in the sequencing data.
(Mathematic concept: the concept of one value being greater than another, as well as calculating averages, etc.)
28. (Currently Amended) The method of claim 26, further comprising, when the sequencing status of the target in the sequencing data is the over sequencing status: subsampling the number of molecular labels with distinct sequences associated with the target in the sequencing data to about the predetermined over sequencing threshold.
(Mathematic concept of sampling numbers from a set.)
29. (Currently Amended) The method of claim 17, wherein determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data comprises: when a negative binomial distribution fitting condition is satisfied, (iv) fitting a signal negative binomial distribution to the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i), wherein the signal negative binomial distribution corresponds to a number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i) being signal molecular labels; (v) fitting a noise negative binomial distribution to the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i), wherein the noise negative binomial distribution corresponds to a number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i) being noise molecular labels; and (vi) determining the number of noise molecular labels using the signal negative binomial distribution fitted in (v) and the noise negative binomial distribution fitted in (vi).
(Mathematic concept of calculating negative binomial distributions, noise distributions, signal distributions, et al.)
30. (Original) The method of claim 29, wherein the negative binomial distribution fitting condition comprises: the sequencing status of the target in the sequencing data is not the under sequencing status or the over sequencing status.
(Mathematic concept, excluding data from the calculations.)
31. (Currently Amended) The method of claim 29, wherein determining the number of noise molecular labels using the signal negative binomial distribution fitted in (v) and the noise negative binomial distribution fitted in (vi) comprises: for each of the distinct sequences associated with the target in the sequencing data: determining a signal probability of the distinct sequence to be in the signal negative binomial distribution; determining a noise probability of the distinct sequence to be in the noise negative binomial distribution; and determining the distinct sequence to be a noise molecular label [[if ]]when the signal probability is smaller than the noise probability.
(Mathematic concept of calculating probability values, and comparisons of values.)
32. (Currently Amended) The method of claim 17, wherein determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data comprises: adding pseudopoints to the number of molecular labels with distinct sequences associated with the target in the sequencing data prior to determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data in (ii)[[, if]] when the sequencing status of the target in the sequencing data is not the under sequencing status or the over sequencing status and the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i) is less than a pseudopoints threshold.
(Mathematic concept of adding false data under certain conditions, which require comparison of data values.)
34. (Currently Amended) The method of claim 17, wherein determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data comprises: removing non-unique molecular labels when determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data in (ii) [[, if]] when the sequencing status of the target in the sequencing data is not the under sequencing status or the over sequencing status and the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (i) is not less than a pseudopoints threshold.
(Mathematic concepts of comparing data to thresholds, and deleting unneeded data under certain conditions.)
35. (Currently Amended) The method of claim 34, wherein removing the non-unique molecular labels comprises removing the non-unique molecular labels when determining the number of noise molecular labels with distinct sequences associated with the target in the sequencing data in (ii)[[ if]] when the number of molecular labels with distinct sequences associated with the target in the sequencing data is greater than a predetermined recycled molecular label threshold.
(Mathematic concepts of comparing data to thresholds, and deleting unneeded data under certain conditions.)
37. (Currently Amended) The method of claim 34, wherein removing the non-unique molecular labels comprises: determining a theoretical number of non-unique molecular labels for the number of molecular labels with distinct sequences associated with the target in the sequencing data; and removing a molecular label with an occurrence greater than the nth most abundant molecular label of the molecular labels with distinct sequences associated with the target in the sequencing data, wherein n is the theoretical number of non-unique molecular labels.
(Mathematic concepts of comparing data to thresholds, and deleting unneeded data under certain conditions.)
38. (Currently Amended) A computer system for determining the number of targets comprising: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor cause the processor to perform the method of claim 15.
(EIA: routine computer system, comprising routine processors and memory. MPEP 2106.05(b). The remainder of the analysis is the same as for claim 15 above.)
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 15, 38 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: labeling sequencing targets for analysis. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 38 recites the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 16-18, 21-22, 24-26, 28-32, 34-35, 37 each recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 15, 38: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
With respect to the step of stochastically barcoding targets, the specification notes the PreciseTM assay can be used to stochastically barcode targets in a sample [0142-0143]. The PreciseTM assay provides stochastic barcodes which also comprise molecular labels, as required. [0143]. With respect to the step of obtaining sequencing data of the stochastically barcoded targets, the PreciseTM assay provides direction for the labeled targets to be amplified and sequenced [0143-0144]. “After labeling, stochastically barcoded cDNA molecules from microwells of a microwell plate can be pooled into a single tube for PCR amplification and sequencing.” [0268, 0277] state Next Generation Sequencing can be employed.
Additionally, stochastic barcoding and sequencing meeting the limitations of the claims are disclosed by: US 2015/0299784; WO2015/031691; Fu et al. (2011) PNAS USA, 108:22 9026-9031. [specification, 0179]
Peng et al. (2015) (PTO-1449) provides stochastic barcoding of targets, and obtaining sequencing data.
Fodor (2011) (PTO-1449) provides stochastic barcoding of targets and obtaining sequencing data.
Shiroguchi (2012) (PTO-1449) provides stochastic barcoding of targets and obtaining sequencing data.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the barcoding of targets for analysis. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, 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 (as discussed in Alice Corp.,).
With respect to claim(s) 38: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Each of Peng, Fodor, Fu and Shiroguchi disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 16-18, 21-22, 24-26, 28-32, 34-35, 37 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
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.
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 15-18, 21-22, 24-26, 28-32, 34-35, 37-38 are rejected under 35 U.S.C. 112, first paragraph, because the specification, while being enabling for methods of determining the number of targets when the targets are a type of nucleic acid, does not reasonably provide enablement for methods of determining the number of targets when the target is any other polymer or any other type of target. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make or use the invention commensurate in scope with these claims.
In In re Wands (8 USPQ2d 1400 (CAFC 1988)) the CAFC considered the issue of enablement in molecular biology. The CAFC summarized eight factors to be considered in a determination of "undue experimentation". These factors include: (a) the quantity of experimentation necessary; (b) the amount of direction or guidance presented; (c) the presence or absence of working examples; (d) the nature of the invention; (e) the state of the prior art; (f) the relative skill of those in the art; (g) the predictability of the art; and (h) the breadth of the claims.
In considering the factors for the instant claims:
a) In order to practice the claimed invention one of skill in the art must be able to determine the number of any target, by the labeling, sequencing, and analysis steps of the claims. For the reasons discussed below, there would be an unpredictable amount of experimentation required to practice the claimed invention.
b) The specification provides guidance for determining the number of nucleic acid targets where the targets are mRNA in Fig 2, and the summary in paragraphs [0140-0150] on pages 33-36. Targets are defined as being types of nucleic acids (RNA, DNA, mRNA, MicroRNA, tRNA et al.) Proteins and lipids are also allegedly contemplated. [0174]. The specification provides detailed guidance on what types of nucleic acids are to be employed [0158-0165]. Stochastic barcoding is defined as “the random labeling of nucleic acids” [0173, 0232-0233]. Stochastic barcodes are defined as “a polynucleotide sequence that can be used for stochastic barcoding” [0171, 0178-181]. They can comprise a molecular label or tag [0171, Fig 1, 0179]. The “molecular label” is defined as a nucleic acid sequence that provides identifying information for the specific type of target nucleic acid species hybridized to the stochastic barcode [0192]. The “labels” to be used are nucleic acid “molecular labels” [0156, Fig 1, 0179, 0192-0194]. The only type of sequencing disclosed are methods of sequencing nucleic acids, such as next-generation sequencing (NGS) [0274, 0403-0410]. “Collapsing” the sequence data as referred to in the claims is disclosed as combining counts of certain molecularly labeled targets if the molecular labels differ by at least one nucleotide base [0276, 0280-0283]. The technique of “directional adjacency” to be used in clustering molecular labels and correcting errors is disclosed as “recursive substitution error correction (RSEC)” [0138]. An example of the distance measure used in determining whether certain molecular labels should be collapsed with the original, includes a Hamming distance [0319]. Estimating the number of labeled targets is then defined in paragraph [0325].
c) The specification provides working examples of determining the number of targets using the method of claim 1 to label nucleic acids in examples 1-20. No working examples are provided for performing the method of determining the number of targets, wherein the targets are any other type of entity, such as polymers, polypeptides, or lipids.
d) The invention is drawn to methods of determining the number of targets where the targets comprise any entity, including polymers, polypeptides, lipids, etc.
e), g) The state of the art of attempting to count the number of specifically labeled targets in a sample can be represented by Peng et al (2015) or Fodor et al. (2011). Each of these references make clear that the type of stochastic barcoding employed in the claims, which include a molecular label, are to be employed to determine nucleic acid targets. The structure of the barcode, the steps of barcoding, the steps of sequencing etc are all directed to nucleic acid examples, and not any other type of polymer. There is no direction as to how to utilize stochastic barcoding on proteins, lipids, polymers or any other entity from a sample.
f) The skill of those in the art of molecular biology and bioinformatics is high.
h) The claims are broad because they are drawn to determining the number of targets, wherein the target is unlimited in structure.
The skilled practitioner would first turn to the instant specification for guidance to practice methods of determining the number of targets other than nucleic acid targets. However, the instant specification does not provide specific guidance to practice these embodiments. As such, the skilled practitioner would turn to the prior art for such guidance, however, the prior art shows that stochastic barcoding is designed for use with nucleic acids, and provides no direction for use with any other type of target. Finally, said practitioner would turn to trial-and-error experimentation to determine how to apply stochastic barcoding to any other type of target. Such represents undue experimentation.
Claims 15-18, 21-22, 24-26, 28-32, 34-35, 37-38 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.
In claim 15, the metes and bounds of the term “noise molecular label” in steps c)ii) and c)iii) are unclear, with respect to how a noise molecular label is to be identified, or distinguished from the generic “molecular label” of step c)i). It is unclear what differs between the two categories, and the specification does not clearly set forth a definition of the term. It would appear any sequencing error within the barcode, or sequence difference within the barcode would meet this limitation.
Claim 15 further fails to particularly point out and distinctly claim how the estimate of the target is made, based on the information collected in steps a-c)ii). The limitation states that the estimate is adjusted, but not how the estimate is made, nor does it point out the particular adjustments to be made to the initial estimate.
The metes and bounds of claims 16-18 are unclear with respect to the term “sequencing status”, sequence saturation, under sequencing, and over sequencing. It is unclear how this status is to be identified, or calculated. This term “sequencing status” appears to differ from the term of the prior art “sequencing depth” which refers to the number of times a particular RNA or DNA has produced a sequence read. It is unclear if this refers to an experimental parameter, such as an interrupted experiment, or an exhaustion of a reagent, or whether it refers to “complete” sequence reads as opposed to “partial” sequence reads, or some other sequence-related status.
The term “saturated sequencing” “under sequencing” and “over sequencing” in claims 16-18, 21, 22, 24, 25, 26, 28, 30, 32, 34 are relative terms which renders the claim indefinite. The terms “saturated, under and over” are 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. It is entirely unclear when a sequencing reaction is deemed to be under-sequenced, when a sequencing reaction is deemed to be over-sequenced, or when a sequencing reaction is identified as saturated.
Claim 38, directed to a computer system, depends from claim 15. Claim 15 is a method claim. It is unclear which of the steps of claim 15 are intended to be performed using the system, including the laboratory steps of barcoding and sequencing. It is recommended that claim 38 be amended to recite all the limitations from claim 15 into a properly independent claim, indicating how the computer system carries out the relevant limitations.
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.
Claim(s) s 15-17, 38 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Shiroguchi et al (2012).
Shiroguchi, K. et al. (2012) Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-nucleotide barcodes. PNAS, Vol 109, No 4, p1347-1352 and some supplemental information, PTO-1449.
With respect to claim 15, Shiroguchi intends to determine a number of labeled targets through stochastic barcode labeling (abstract, p1347 column 1, last 3 lines to column 2 end of the paragraph, and Fig 1).
With respect to step a) Shiroguchi discloses stochastically barcoding a plurality of targets in a sample, using a plurality of stochastic barcodes, each barcode having a molecular label (page 1348, second column).
With respect to step b) the labeled targets are amplified then sequenced (Materials and methods, page 1).
With respect to step c)(i) Shiroguchi counts the number of labels with distinct sequences associated with the target (p1348, second column).
With respect to step c)(ii) labeled targets that had at most two mismatches from an original barcode meet the BRI of the term “noise molecular label”.
With respect to step c)iii) the counts from those mismatches meeting the BRI of “noise molecular labels” were used to adjust the count of the original code (Materials and Methods, page 1, Results) meeting step c) (iii).
This number was determined to be an estimate of the number of labeled targets (Materials and methods, page 1, Results).
With respect to claim 16-17, Shiroguchi determines sequencing depth /sequencing status. (Materials and methods, page 1, Sequencing sample preparation and sequencing, Results, Efficacy of digital counting strategy, and Tenfold down-sampling of spike-in reads). Depending on the sequencing depth, the sequence count data can be adjusted (Materials and methods, page 1, Results, tenfold down-sampling of spike-in reads).
With respect to claim 38, Shiroguchi provides computer implemented methods using computer systems.
Claim(s) s 15 and 38 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Fodor et al (2011).
Fodor, S.P.A. et al. (6/30/2011) Digital counting of individual molecules by Stochastic attachment of diverse labels. US 2011/0160078 A1.
With respect to claim 15, step a), Fodor stochastically barcodes a plurality of nucleic acid targets, and each barcoded target also comprises a molecular label, as set forth at [0013, Fig 2, Fig 8, Fig 10, Fig 18, 0083-0084, 0096-0100, 0103-0109, 0121, etc.].
With respect to claim 15, step b), Fodor sequences the barcoded, labeled targets, to obtain sequencing data, as set forth at [0081, 0102, 0111, 0159, 0167, etc.].
With respect to claim 15, step c)i), Fodor counts the number of molecular labels, with distinct or unique sequences as set forth at [0011-0012, 0097, 0103, 0106, etc.].
“The labeled features indicate the presence of a specific target-label-tag combination and each corresponds to a count.” [0106]
With respect to claim 15, step c)ii), Fodor identifies labels which may be noisy, including errors originating in sampling, amplification or sequencing, which may lead to errors in estimation of the target count, for example at [0120, 0188, 0246].
With respect to claim 15 step c)iii) Fodor estimates the number of targets, correlated with the number of labels, adjusted for noise, or errors, as set forth at [0109, 0152, 0195-0198, Table 4, 0204-0205, 0235, etc.].
“Undercounting can also be adjusted for by estimating the number of copies that are likely to be multiply labeled and adjusting the final count upwards to take those into account. For example, if there is a likelihood that 5 of 1000 copies will be labeled with the same label tag then the final number should be adjusted up by 0.5%” [0152]
As such, claim 15 is anticipated.
Fodor’s methods are all computer-implemented using computer systems comprising processors, memory and instructions, meeting claim 38.
Claim(s) s 15-18, 32 and 38 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Fan et al (2015).
Fan, C. et al. (3/5/2015) Massively parallel single cell analysis. WO2015/031691 A1.
With respect to step 15, a), Fan discloses stochastic barcoding target polynucleotides using stochastic barcodes, which may comprise a molecular label, for example at [00112-00115, Fig 56, 00116-00121, 0130-0131, 00237-00245, Fig 1, Fig 3, 00247-00250, etc.]
“Generally, the methods, kits, and compositions comprise (a) stochastically labeling molecules in two or more samples with molecular barcodes to produce labeled molecules; and (b) detecting the labeled molecules. The molecular barcodes may comprise one or more target specific regions, label regions, sample index regions, universal PCR regions, adaptors, linkers, or a combination thereof. The labeled molecules may comprise a) a molecule region; b) a sample index region; and c) a label region.” [0112]
With respect to claim 15, step b), Fan provides sequencing the labeled targets, and obtaining sequencing data, at [0003, 0010, 0086, Fig 55-59, 00119 (HiSeq, MiSeq), 00195, 0340 etc.]
With respect to claim 15, step c)i), Fan counts the number of molecular labels with distinct sequences, at [0010, 00114-00117, 00297, 00325, 00394, 00404, 00512, 00609, 00722, etc.]
“The sequencing data may be used to count the number of target nucleic acid molecules in a cell. For example, a plurality of copies of a target nucleic acid in a cell may bind to a different oligonucleotide on the solid support. When the plurality of target nucleic acids are amplified and sequenced, they may comprise different molecular labels. The number of molecular labels for a same target nucleic acid may be indicative of the number of copies of the target nucleic acid in the cell. Determining the copy number of a target nucleic acid may be useful for removing amplification bias when determining the concentration of a target nucleic acid in a cell.” [00394]
With respect to claim 15, step c)ii), Fan addresses noisy labels, or labels with errors arising from the sampling, amplification, or sequencing processes, for example at [00225, 00770, 00804, etc.]
“decoding/demultiplexing of sample barcodes, cell barcodes, and molecular barcodes; and automated clustering of molecular labels to compensate for amplification or sequencing errors; wherein the sequence data is generated by performing multiplexed, single cell stochastic labeling and molecular indexing assays.” [00804]
With respect to claim 15, step c)iii) Fan estimates the number of target molecules, after compensating for over or underrepresentation of labels, for example at [00131].
“Accordingly, in some instances, the probability of any of the molecules in a sample finding any of the tags and labels is assumed to be equal, an assumption that may be used in mathematical calculations to estimate the number of molecules in the sample. In some circumstances the probability of attaching may be manipulated by, for example electing tags and labels with different properties that would increase or decrease the binding efficiency of that molecular barcodes, sample tags, and/or molecular identifier labels with an individual molecule. The tags and labels may also be varied in numbers to alter the probability that a particular molecular barcodes, sample tags, and/or molecular identifier labels will find a binding partner during the stochastic labeling. For example, one label is overrepresented in a pool of labels, thereby increasing the chances that the overrepresented label finds at least one binding partner.” [00131].
As such, claim 15 is anticipated.
With respect to claims 16-18, sequencing status, including saturated sequencing status is provided at [00125, 00357, 00583, 00599, 00609]
“Sequencing of the amplicons revealed the cell label, the molecular label, and the gene identity (FIG. 55). Computational analysis grouped the reads based on the cell label, and collapsed the reads with the same molecular label and gene sequence into a single entry to suppress any amplification bias. The use of molecular label enabled us to measure the absolute number of molecules per gene per cell, and therefore allowed the direct comparison of cellular expression level across biological samples that may have undergone different depths of sequencing.”
With respect to claim 32, Fan adds a pseudopoint or pseudocount at 00609
The methods of Fan are all computer-implemented, using computer systems comprising processors, memory and instructions, thus meeting claim 38.
Claim(s) s 15-18, 21-22, 24-25, 29-31, 38 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Robles et al (2012).
Robles, J. A. et al. (2012) Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics, 13:484, 14 pages and some supplemental material.
Robles is directed to “quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.” (Abstract).
With respect to claim 15, Robles seeks to label RNA targets, using stochastic barcoding wherein the barcodes comprise labels, as set forth in the discussion of RNA-seq methodology. Robles notes that aspects of sequencing depth, or amount of sequencing data generated can dramatically affect the counting of the RNA targets. (p2). Technical replication, experimental design, and barcoding design also affect the final estimate of RNA targets. (p3).
With respect to claim 15 step a) and “stochastically barcoding a plurality of targets…”
Robles discloses this at page 3, col 1. “Multiplexing uses indexing tags, “barcodes” or short (≤ 20 bp) stretches of sequence that are ligated to the start of sample sequence fragments during the library preparation step.”
With respect to claim 15, step b) “obtaining sequencing data…
Robles provides sequencing data of the barcoded targets: P3 Col 1: “Barcodes are distinct between sample libraries and allow pooling for sequencing followed by allocation of reads back to individual samples after sequencing by analysis of the sequenced barcode.”
With respect to claim 15 step c)i), and “counting the number of molecular labels…”
Robles counts the number of sequence reads for each of the molecular labels, as well as the total number of molecular labels.
With respect to claim 15, step c)ii) and “counting the number of noise molecular labels…”
Robles notes that various technical errors can lead to noisy labels that do not perfectly match the original molecular label. P3, col 1-2; Methods, p10. “The model is a hierarchical model which takes into account sources of variability in the molar concentration of each transcript isoform in the prepared cDNA library due to i) library preparation steps and, in the case of biological replicates, ii) biological variation. This variation is compounded by an additional Poisson shot-noise arising from the sequencing step.” See also methods p12.
With respect to claim 15, step c)iii), and “estimating the number of the target…” by adjusting based on the noise labels,
Robles adjusts the estimate of any particular target amount, based on aspects of sequencing depth, noise labels and replication variation. Methods, p10. The estimates for each target are generated and validated utilizing multiple software packages, such as edgeR, DESeq, and NBPSeq.
With respect to claim 38, the methods of Robles are all computer implemented.
With respect to claims 16-18, Robles discusses the aspects of how sequencing depth affects the ultimate estimate of target concentration or amount, throughout. See p2,
Sequencing depth: “Sequencing depth is usually referenced to be the expected mean coverage at all loci over the target sequence(s), in the case of RNA-seq experiments assuming all transcripts having similar levels of expression… Pragmatically, RNA-seq sequencing depth is typically chosen based on an estimation of total transcriptome length (bases) and the expected dynamic range of transcript abundances. Given the dynamic nature of the transcriptome, the suitability of these estimates could vary substantially across organisms, tissues, time points and biological contexts.”
P3, Experimental Design “With the dramatic increases in sequencing yields being achieved with current chemistries and new platforms, multiplexing is becoming the method of choice to increase sample throughput. These designs have direct impacts on sequencing depth generated that need to be considered in the power of the experimental design. Also, when multiplex strategies are used, biologists need to be mindful of potential systematic variations between sequencing lanes. These variations can be addressed through randomisation or blocking designs to distribute samples across lanes, see [30] for a discussion of barcoding bias in multiplex sequencing, and [31] for an alternative to barcoding.”
P3, Approach: “To quantify the effects of different sequencing depths and replication choices we compared a range of realistic experimental designs for their ability to robustly detect DE. Using simulated data with known DE transcripts allowed us to estimate the false positive rate (FPR) and true positive rate (TPR) of DE calls. The changes of these rates were used to compare the detection power yielded by each choice of number of biological replicates and sequencing depth. In the Methods section, we outline the definitions used for FPR and TPR as well as explaining the method used for the construction of the synthetic data; which includes induced differential expression, simulates the variations that biological replicates introduce and simulates loss of sequencing depth.”
P6, “Detection of DE as a function of sequencing depth Figure 3 represents the combined results of decreasing sequencing depth for all values of n. It can be seen that as sequencing depth decreases the TPR generated by DESeq decreases monotonically across all n while the FPR remains below 0.1% (the corresponding results obtained using edgeR are shown in Additional file 1: Figure S2). Table2showstheFPRforallbiologicalreplicatesnanda subset of sequencing depths: 25%, 50%, 75% and 100%, the FPR remains below 0.1% at all sequencing depths. Table 3 shows the TPR reported by DESeq for the same subset of sequencing depths, here the TPR increases strongly as sequencing depth increases for any number of biological replicates n”
p12, Multiplexing experimental designs.
With respect to claims 21-22, and 24-25 Robles analyzes undersequencing and saturated (100%) sequencing and counting the molecular labels at p6, and Fig 3.
“Figure 3 represents the combined results of decreasing sequencing depth for all values of n. It can be seen that as sequencing depth decreases the TPR generated by DESeq decreases monotonically across all n while the FPR remains below 0.1% (the corresponding results obtained using edgeR are shown in Additional file 1: Figure S2).
Table 2 shows the FPR for all biological replicates n and a subset of sequencing depths: 25%, 50%, 75% and 100%, the FPR remains below 0.1% at all sequencing depths. Table 3 shows the TPR reported by DESeq for the same subset of sequencing depths, here the TPR increases strongly as sequencing depth increases for any number of biological replicates n.”
With respect to claims 29-31, Robles applies the negative binomial distribution fitting to the sequencing data collected from the multiplexed barcoded simulated samples. Methods, p10-12.
“Our synthetic data is based on a negative binomial (NB) model of read counts assumed by [39] and used in edgeR [32], DESeq [25] and NBPSeq [33]. The model is a hierarchical model which takes into account sources of variability in the molar concentration of each transcript isoform in the prepared cDNA library due to i) library preparation steps and, in the case of biological replicates, ii) biological variation. This variation is compounded by an additional Poisson shot-noise arising from the sequencing step. Assuming the molar concentration in the prepared cDNA library to have a Gamma distribution, one arrives at a NB distribution for the number of counts K mapped onto a particular transcript of interest in a given lane of the sequencer: (Eq 1) The mean μ is proportional to the concentration of the transcript of interest in the original biological sample, up to a normalisation factor specific to the lane of the sequencer. A suitable model for this normalisation factor is the Robinson-Oshlack TMM factor [32]. The quantity φ is called the dispersion parameter [39], and is specific to the transcript isoform and the library preparation. A more detailed account of the model is given in the Additional file 3.
R packages for DE in RNA-Seq: All three packages considered are based on a NB model, and differ principally in the way the dispersion parameter is estimated. Unless otherwise stated, tests of these packages used herein use default settings. Typical coding sequences are given in the Additional file 3.”
With respect to claim 38, all of the methods of Robles are performed on computer systems, comprising processors, memory and instructions.
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.
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(s) 32, 34-35, 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robles (2012) as applied to claims 15-18, 21-22, 24-25, 29-31, 38 above, in view of Erhard (2015).
Robles, J. A. et al. (2012) Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics, 13:484, 14 pages and some supplemental material.
Erhard, F. et al. (7/8/2015) Count ratio model reveal bias affecting NGS fold changes. Nucleic Acids Research, Vol 43, no 20, e136, 14 pages.
Claims 32, 34-35 and 37 add pseudocounts or pseudopoints to certain aspects of the data, and delete non-unique labels under certain conditions.
Robles describes the claimed methods, wherein targets are stochastically barcoded, where the barcodes comprise molecular labels, the labeled targets are sequenced, the molecular labels are counted, noise labels are identified, and used to estimate the number of original targets. Robles discloses using negative binomial fitting under various conditions. Robles does not address pseudocounts or pseudopoints in the analysis of the molecular labels.
Erhard is directed to analysis of bias inherent in stochastic barcoding processes, and high throughput sequencing processes.
“Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data.” (abstract)
Erhard discusses known sources of bias in the introduction:
“…single-sample or per-experiment measurements are hampered by known biases introduced dur ing sample preparation, e.g. by polymerase chain reaction (PCR) amplification (13,14) or adapter ligation (15,16). As a consequence, some sequences from the same entity are observed orders of magnitude more often than others (1,17). Several computational ad-hoc attempts have been made to correct for such bias (18–20), but cannot remove it completely.” P1,
“Bias can be handled experimentally by labeling RNA fragments using random barcodes before PCR (14). Thus, additional experimental steps are necessary, that so far have only been applied in a few published studies (7,14). To handle such bias in available data sets, we introduce a novel method to estimate fold changes. We show that fold change estimates are significantly improved by our method using data from the MAQC project (26,27) and that about 20% of genes that are called differentially expressed are affected in a standard RNA-seq setting. Finally, we show that the method can also be applied to the estimation of normalization constants and show that it outperforms the widely used median based normalization.” P2
Erhard analyzes sequence read data from barcoded E. coli gene expression experiments as set forth at p2, Datasets.
“For both digital and conventional counts, normalization constants were computed such that the median log fold change of genes with more than 50 counts in both replicates was 0. For all genes, the Maximum-A-Posteriori (MAP) estimate with no pseudo-counts and its 99% symmetric credible interval (see below) were computed. As the true fold change of all genes should correspond to the normalization constant, genes where the normalization constant was outside of the computed credible interval were marked in Figures 2A and 4B... To test local deviations from the gene fold change (Figures 2E and 4E), the Positive predictive distribution of our Bayesian model was used as follows: we computed the cumulative distribution function for local read counts of the first replicate using the Beta-Binomial distribution parameterized with the sum of the local read counts and the total counts from each replicate.” P2
Erhard further discusses read counting, and pseudo-counts at p5, “count ratio model”.
“We define local read counts as the number of reads that have been aligned to a certain genomic position. Importantly, genomic position does not only refer to the start position of the alignment, but also includes all potential splice junctions and the alignment end (which is important when reads have different length due to trimming). A local count ratio is the ratio of two local read counts from two conditions or samples or aggregated numbers from sets of replicates or sets of samples/conditions.
Our model is based on the following considerations: given two lists of local read counts we want to determine the true mixing ratio that has led to these counts. If we assume that all n reads belonging to a pair of local read counts were pooled, the local read count from the first condition is binomially distributed with parameters n and p, where p is related to the true log fold change between the two conditions. The lists of local read counts represent repeated and independent measurements of this binomial distribution with the same parameter p. Thus, these lists of local read counts can be used to estimate p, and, by transformation, the true log fold change. In fact, the Maximum Likelihood Estimate (MLE) of this model is mathematically equivalent to the obvious and widely used log fold change, which is the total number of reads in condition 1 divided by the total number of reads in condition 2 (see Methods section for further details).
Furthermore, the Bayesian MAP estimate extends the MLE and introduces the parameters of its prior distribution as pseudocounts that are added to both total numbers of reads. Of note, pseudocounts are widely in use as well to avoid division by zero.
Thus, the basic statistics from the count ratio model are already widely in use. However, it brings three additional benefits for NGS data analysis. First, it introduces a theoretical justification for ad-hoc pseudocounts. Second, we can analytically compute the full posterior distribution or credible intervals for the true log fold change in addition to the above introduced point estimates. And third, our model indicates that the total number of reads can be decomposed into many local read counts, which allows to handle bias in a straight-forward way (see below)” p5
The stochastic barcoding process, using pseudocounts is discussed beginning at p5:
“Usually, it is not possible to distinguish whether high copy numbers of observed sequences are the product of PCR amplification or indeed correspond to multiple copies of the same RNA fragment in the sample before amplification. By using random barcodes in the sequencing adapters, it is possible to make this distinction.”
“The estimated mRNA log fold change should be within the bounds of the credible intervals along the whole mRNA (as for fumA; see Figure 2D). If not, deviations cannot be explained by sampling noise introduced by sequencing, but must be due to technical (see below) or biological reasons, e.g. differential splicing.” P6
“Often, prior knowledge is available for differential expression of genes. For instance, the experimenter could be 99% sure that the fold change of a certain gene is 2 with a tolerance of 0.5 fold. Or, when the two conditions under investigation are quite different, we would like to tolerate high fold changes in general, and only small changes, when conditions are highly similar. Our model allows to incorporate such prior information by transforming it into corresponding pseudocounts α and β (see Methods section for details).
There are a few remarks here. First, even if no pseudocounts are used, i.e. α= β= 1, a specific log2 fold change distribution is imposed on the fold change estimator (see Figure 3A and B). Specifically, deviations of at most 10 are tolerated with a certainty of about 90%. This does not mean that larger deviations are not allowed: the more data are used for the inference, the less influence has the prior distribution. However, this plays an important role especially for entities with few reads. Second, it is possible to intention ally bias fold changes toward specific values known a-priori by using asymmetric pseudocounts and our framework provides the theoretical background for specifying the intended value as well as its tolerance. For instance, if the log2 fold change is supposed to lie between 0 and 2 with 50% certainty, = 3.26 and = 1.63 must be used. And, finally, and maybe smaller than 1 (but strictly greater than 0), corresponding to a wider prior log2 fold change distribution than when no pseudocounts are used (see Figure 3 A and B).”
Erhard further discusses the presence of noise, and sampling / subsampling errors on the resulting data. Artificial increase of a read count, based on counting labels or barcodes, is common when PCR is used to amplify the genetic material. These artificially inflated read counts must be addressed by subsampling, and further downsampling techniques as set forth at p7. Downsampling of the analyzed data by Erhard led to data agreeing more closely to the standard qPCR results.
“Consistent with the results from the replicate comparison above, all downsampling methods improve deviations from the qPCR reference significantly (see Figure 5). Importantly, in all cases, there are also many genes where the deviation increases slightly upon downsampling. This can be a consequence of the fact that the qPCR fold changes are in fact not a gold standard and suffer from inaccuracies as well (31). Importantly, the accuracy gain for SEQC data set are less pronounced indicating that sequencing quality may have improved in recent years. However, it is unclear, whether this is a consequence of the sequencing mode (single-end versus paired-end), the sequencing device or differences in sample preparation. Nevertheless, even for the recent data set, downsampling still leads to significantly more accurate fold changes, indicating that there is still room for improvement by computational analysis methods.” P9
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
The Robles reference does not teach adding pseudopoints or pseudocounts to read count data in the data analysis process. However, the use of pseudocounts in analysis of genetic data were well known in the prior art, to avoid the common problem of dividing by zero. Erhard utilizes pseudocounts in the analysis of barcoded target sequencing data, to avoid that common problem, and indicates it is a well-used technique in the statistical analysis of such data. The rejected claims have the risk that certain barcodes or labels are reset to zero during the processing, due to technical bias removal. One of skill would have looked to the prior art of bioinformatics and statistics to see how to remedy such situations. One of ordinary skill in the art would have been motivated to add pseudopoints under certain situations, so that the related data would not have been lost from the analysis. One of skill would have had a reasonable expectation of success in applying pseudopoints or pseudocounts to the data, as it is a routine technique, often applied in similar situations as illustrated by Erhard. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 15, 38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 11,608,497. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the patent apply the same techniques to identify noisy labels, where the label can be a cell label, in the analysis of types of cells in a sample. The instant claims provide the same steps, to be used in any stochastic barcoding environment and are thus generic to the patent.
Claims 15, 38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,525,157. Although the claims at issue are not identical, they are not patentably distinct from each other because both methods stochastically label targets, where the stochastic barcode can comprise a molecular label, and both methods account for the presence of noisy labels in the estimation of the count of the original target.
Claims 15, 38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,331,351. Although the claims at issue are not identical, they are not patentably distinct from each other because both methods stochastically label targets, where the stochastic barcode can comprise a molecular label, and both methods account for the presence of noisy labels in the estimation of the count of the original target.
Conclusion
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
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