DETAILED ACTION
Applicant’s response, filed Dec 24 2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Claim Status
Claims 1-22 and 24-38 are pending.
Claim 23 is canceled.
Claims 1-22 and 24-38 are rejected.
Priority
Applicant's claim for the benefit of a prior-filed application, PCT/JP2020/021017, filed 27 May 2020, is acknowledged.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to App. No. JP2019-099716, filed 28 May 2019. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Accordingly, each of claims 1-22 and 24-38 are afforded the effective filing date of 28 May 2019.
Information Disclosure Statement
The information disclosure statement (IDS) filed on Dec 22 2025 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Claim Interpretation
The interpretation of “a sorting apparatus” in claims 1 and 14-15 under 35 USC 112(f) is withdrawn in view of the deletion of those limitations from the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 20-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The rejection is newly stated and is necessitated by claim amendment.
Claim 20 recites “wherein controlling the sorting apparatus based, at least in part, on the instructions further comprises…”. It is unclear whether the wherein clause is intended to require performing the controlling within the metes and bounds of the claimed invention, or if it is only further limiting the instructions which are transmitted in claim 15 such that performing the controlling is not required within the metes and bounds of the invention. As set forth in MPEP 2111.04.I, “wherein” clauses raise the question as to the limiting effect of the language in a claim. As the claims do not recite an active performance of the controlling, the metes and bounds of the claims are unclear. For compact examination, it is assumed that the controlling is not required to be performed. The rejection may be overcome by clarifying what steps are required to be performed. Claim 21 is similarly rejected, and is rejected based on its dependency from claim 20.
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 1-12, 14-22, 24-29, and 34-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. The rejection is newly stated and is necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to systems, methods, and non-transitory computer-readable storage media, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claims 1 and 14: applying a data compression process to data indicating light emitted from biological particles;
outputting, based on a result of the data compression process, one or more groups of the biological particles;
using at least some of the data corresponding to the one or more groups of the biological particles in training at least one machine learning model.
Independent claims 15 and 24: generate instructions for controlling a sorting apparatus to sort the one or more of the one or more biological particles, wherein the at least one statistical machine learning model was trained using training data corresponding to one or more groups of biological particles determined based on a compressed format of the training data.
Dependent claims 4 and 29: selecting a first group from among the one or more groups of the biological particles, and
wherein using at least some of the data further comprises using data corresponding to the first group.
Dependent claim 6: specifying a range for at least one group of the one or more groups of the biological particles, and
wherein using at least some of the data further comprises using data corresponding to the range for the at least one group.
Dependent claim 6: an output of the at least one machine learning model obtained by the applying the at least one trained machine learning model identifies at least some of the data indicating light emitted from biological particles as being within a range.
Dependent claims 22 and 28: applying a data compression process to the data indicating light received by the photodetector array;
outputting, based on a result of the data compression process, the one or more groups of the biological particles; and
using at least some of the data corresponding to the one or more groups of the biological particles as the training data to train the at least one machine learning model
Dependent claims 2-3, 7-8, 11, 18-19, and 38 recite further steps that limit the judicial exceptions in independent claims 1, 13-15, and 30 and, as such, also are directed to those abstract ideas. For example, claims 2-3, and 18-19 further limit applying the data compression process in claims 1 and the compressed format in claim 15; claims 7-8 further limit the data used in claim 1; claim 11 further limit the at least one machine learning model claim 1; and claim 38 further limits training the at least one machine learning model.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually train a statistical model to output which specifies an indication to sort biological particles. Without further detail as to the methodology involved in “applying”, “outputting”, “training”, “generate”, “selecting”, and “specifying”, under the BRI, one may simply, for example, use pen and paper to apply a data compression process to data, record an output of groups to sort based on the data compression process, and train a statistical model to output which specifies an indication to sort biological particles. Those steps directed to “applying a dimension compression process” and “training at least one machine learning model” require mathematical techniques as the only supported embodiments, as is disclosed in the specification at [0051-0054; 0057; 0061-0064; 0073-0074], which describes data compression as either a non-linear or linear process that performs dimensional compression, clustering, grouping, or a machine learning using various algorithms and training a statistical model as training a learning algorithm, such as random forests, support vector machine, or deep learning.
Therefore, claims 1-12, 14-22, 24-29, and 34-38 recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1 and 14: applying the at least one trained machine learning model to subsequent data indicating light emitted from subsequent biological particles by inputting the subsequent data to the at least one trained machine learning model; and
transmitting one or more instructions to a sorting apparatus to sort the subsequent biological particles based on the result of applying the at least one trained machine learning model.
Dependent claim 4: receiving an input.
Dependent claim 6: receiving, from a user interface, input.
Dependent claim 36: presenting compression processed data to a user by mapping the compression processed data to a three-dimensional or smaller area on a user interface.
Dependent claim 37: present at least some of the data used in training the at least one statistical model and one or more groups of the biological particles on a user interface.
Independent claims 15 and 30: obtaining data indicating the light received by the photodetector array;
using the data and at least one machine learning model…; and
transmitting the instructions to the sorting apparatus.
Dependent claims 5, 9-10, 16-17, 20, 25-27, and 34-35 recite steps that further limit the recited additional elements in the claims. For example, claim 5 further limits receiving the input in claim 4; claims 9-10 and 26-27 further limit the biological particles from which the light is emitted in claims 1 and 24; claim 16 further limits the sorting apparatus of claim 15; claims 17 and 25 further limits the data obtained in claims 15 and 24; claim 20 further limits controlling the sorting apparatus in claim 15, which is interpreted as further limiting the instructions for control (see the above 35 USC 112(b) rejection); and claims 34-35 further limit the data of claim 1.
The claims also include non-abstract computing and data gathering elements. For example, independent claim 1 includes an information processing system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions; independent claim 14 includes at least one non-transitory computer-readable storage medium storing processor-executable instructions; independent claim 15 includes a sorting system comprising: a photodetector array configured to receive light emitted from one or more biological particles; at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions; and claim 30 includes at least one non-transitory computer-readable storage medium storing processor-executable instructions.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receiving” data and those non-abstract data gathering elements which perform the data gathering functions as in the independent claims, and to data outputting, such as “transmitting” or “outputting” instructions as in the independent claims and “presenting” data as in claims 36-37, perform functions of collecting and outputting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract computing do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Further, the limitations reciting “applying the at least one trained machine learning model…” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the limitations merely serve to link the judicial exception of generating instructions to the technological environment of the trained machine learning model.
The specification does not provide a clear explanation for how the additional elements in the claims provide improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving, storing, and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
With respect to the instant claims and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018), MPEP 2106.06(A)). Additionally, a photodetector array configured to receive light emitted from one or more biological particles in claim 15 and a cell sorting apparatus in claims 15 and 30 are considered to be a well-understood, routine, and conventional system component used in cell sorting, as demonstrated by Radisic et al. (International Journal of Nanomedicine, 2006, 1(1):3-14; cited on the Jun 13 2024 IDS) at least at p .5, col. 2, par. 3 through p. 7, col. 1, par. 1 and Figure 2. Further, the specification also notes that cell sorters and photodetector arrays are commercially available or widely used at [0024-0025]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, instant claims 1-12, 14-22, 24-29, and 34-38 are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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.
A. Claims 1-14, 34, and 36-38 are rejected under 35 U.S.C. 103 as being unpatentable over Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS) in view of Aghaeepour et al. (Bioinformatics, 2018, 34(23):4131-4133; and Supplemental Methods; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
The prior art to Becht discloses Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity (abstract). Becht, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows.
Claim 1 discloses an information processing system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform the method. Claim 13 discloses an information processing method. Claim 14 discloses at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform the method. Becht teaches that Hypergate is an algorithm which is implemented computationally and available on CRAN (abstract), which is considered to read on the instant system and non-transitory computer-readable storage medium. Becht teaches flow cytometers in general (p. 301, col. 1; abstract), but does not explicitly teach an information processing system comprising a sorting apparatus as instantly claimed.
The method steps of claims 1 and 13-14 comprise:
applying a data compression process to data indicating light emitted from biological particles;
Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification at least at [0003-0006].
Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by the instant specification at least at [0062-0063].
outputting, based on a result of the data compression process, one or more groups of the biological particles; and
Becht teaches identifying clusters of interest defined by t-SNE (i.e., one or more groups of the biological particles) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5).
using at least some of the data corresponding to the one or more groups of the biological particles in training at least one machine learning model;
Becht teaches that the inputs of Hypergate are the dataset, which is an expression matrix of events and parameters (i.e., optical data), and a cluster of interest which has been identified from clustered data (i.e., information regarding sorting of the biological particles specified based on the compressed data) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5). Becht teaches training Hypergate to obtain a gating strategy that identifies a malignant population of cells in a sample and applying these gating strategies to follow-up samples (p. 304, col. 1, par. 4 through col. 2, par. 1; p. 307, col. 1 through p. 308, col. 1, par. 3; Fig. 6). As Becht teaches that Hypergate optimizes sorting strategies (p. 307, col. 1, par. 4) and general methods for cell sorting (p. 307, col. 2, par. 2), it is considered that Becht fairly teaches an output of the at least one statistical model specifies an indication to sort one or more of the biological particles as instantly claimed.
Although Becht teaches training Hypergate, Becht does not explicitly teach that Hypergate is a machine learning model. See below for teachings by Aghaeepour regarding a machine learning model.
applying the at least one trained machine model to subsequent data indicating light emitted from subsequent biological particles by inputting the subsequent data to the at least one trained machine model; and transmitting one or more instructions to a sorting apparatus that cause the sorting apparatus to sort the subsequent biological particles based on the result of applying the at least one trained machine model (claim 1); controlling a/the sorting apparatus to sort the subsequent biological particles based on the result of applying the at least one trained machine model (claim 13); or transmitting one or more instructions to a sorting apparatus to sort the subsequent biological particles based on the result of applying the at least one trained machine learning model (claim 14).
As Becht teaches that Hypergate optimizes sorting strategies (p. 307, col. 1, par. 4) and general methods for cell sorting (p. 307, col. 2, par. 2), it is considered that Becht fairly teaches a sorting system as instantly claimed.
Becht does not teach applying the trained machine learning model to subsequent optical data indicative of light emitted from subsequent biological particles by inputting the subsequent optical data to the trained learning model and sorting the subsequent biological particles based on result of applying the trained learning model.
However, the prior art to Aghaeepour discloses GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time (abstract). Aghaeepour teaches that GateFinder workflow begins with the researcher selecting a target cell population of interest such as a manually gated sub-population or the output of a clustering algorithm (i.e. groups of biological particles output from a data compression process), then GateFinder takes a randomly selected sub-sample of the complete high-dimensional dataset and projects it in all possible two-dimensional scatter plots, eliminates outliers, constructs gates around the target cells on the scatter plots, find the gate that achieves the best enrichment of the target cells, and repeats the steps until all markers have been exhausted (p. 4132, col. 1, par. 2). Aghaeepour teaches that GateFinder uses a supervised feature selection algorithm (p. 4132, col. 1, par. 2), which reads on a machine learning model as instantly claimed. Aghaeepour teaches that one of the practical uses for the concise visual description of a target population includes designing fluorescence activated cell sorting strategies for physical isolation of cells (p. 4131, col. 2, par. 1), which is considered to read on applying trained GateFinder on subsequent optical data to sort the subsequent biological particles, as instantly claimed.
Regarding claims 1 and 13-14, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Becht and Aghaeepour because both references disclose methods for learning models for gating or classifying populations of cells in flow cytometry data. The motivation to use the supervised feature selection algorithm of Aghaeepour in the method of Becht would have been to limit the search to the most relevant markers, as taught by Aghaeepour (p. 4132, col. 1, par. 2).
Regarding claim 2, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 2 further adds that applying the data compression process to the data indicating light emitted from biological particles further comprises performing clustering of the data to classify one or more of the biological particles into a plurality of groups. As Becht teaches that t-SNE defines clusters of cells (p. 303, col. 2, par. 5), it is considered that Becht fairly teaches this limitation.
Regarding claim 3, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 3 further adds that applying the data compression process to the data indicating light emitted from biological particles further comprises reducing a number of dimensions of the data. As Becht teaches performing t-SNE defines as described above (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), it is considered that Becht fairly teaches this limitation. Such an interpretation is supported by the instant specification at least at [0062-0063].
Regarding claims 4-5, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 4 further adds receiving an input selecting a first group from among the one or more groups of the biological particles, and wherein using at least some of the data further comprises using data corresponding to the first group. Claim 5 further adds receiving user input from a user interface indicating selection of the first group. Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE (p. 303, col. 2, par. 5), which reads on user input selecting a first group of the biological particles and using that data to train the statistical method.
Regarding claim 6, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 6 further adds receiving, from a user interface, input specifying a range for at least one group of the one or more groups of the biological particles, and wherein using at least some of the data further comprises using data corresponding to the range for the at least one group. Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE and using the parameters chosen by the authors (p. 303, col. 2, par. 5), which reads on input specifying a range for at least one group of biological particles and using that data to train the statistical method. As Becht teaches that Hypergate is an algorithm which is implemented computationally and available on CRAN (abstract), it is considered that they would have to input the parameters through a user interface as instantly claimed.
Regarding claim 7, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 7 further adds that the data indicating light emitted from biological particles includes information received by a flow cytometer. Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3).
Regarding claim 8, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 8 further adds that the data indicating light emitted from biological particles includes information identifying a spectrum of light for each of one or more biological particles. Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a spectrum of light) (p. 304, col. 1, par. 2).
Regarding claim 9, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 9 further adds that the biological particles include at least one biological particle chosen from a cell, a microorganism, a virus, a fungus, an organelle, and a biological polymer. Becht teaches that Hypergate provides gating strategies for cell populations (abstract).
Regarding claim 10, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 10 further adds that one or more of the biological particles is labeled with a fluorescent dye. Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a fluorescent dye) (p. 304, col. 1, par. 2).
Regarding claim 11, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 11 further adds that the at least one machine model comprises a classifier chosen from a random forest classifier and a support vector machine classifier. Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1). Additionally, Aghaeepour Supplemental Methods teaches using Random Forests as a feature selection algorithm.
Regarding claim 12, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 12 further adds that an output of the at least one machine model obtained by the applying the at least one trained machine model identifies at least some of the data indicating light emitted from biological particles as being within a range. Becht teaches that Hypergate provides gating strategies for cell populations (abstract), where gating is the process of setting thresholds on measured parameters (i.e., particles within a range), to filter out unwanted cells until only a population of interest is left (p. 301, col. 1). Becht does not teach applying the at least one trained machine model.
However, Aghaeepour teaches using GateFinder (p. 4132, col. 1, par. 2), which is considered to read on a machine learning model as described above.
Regarding claim 34, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 34 further adds that the data indicating light emitted from biological particles is information obtained by performing fluorescent separation on the light emitted from the biological particles to obtain a level of expression of fluorescent dye of each color of a plurality of colors. Becht teaches that the flow cytometry datasets have multiple markers (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2). Becht teaches visualizing the cells by plotting two markers against one another (Fig. 2C; Fig. 6B-D), which is considered to read on fluorescent separation on the light emitted from the biological particles to obtain a level of expression of fluorescent dye of each color of a plurality of colors as instantly claimed.
Regarding claim 36, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 36 further adds that the processor- executable instructions, when executed by the at least one hardware processor, further cause the at least one processor to perform presenting compression processed data to a user by mapping the compression processed data to a three-dimensional or smaller area on a user interface. Becht teaches mapping two of the t-SNE parameters (i.e., three-dimensional or smaller area) to identify clusters of interest (Fig. 1-2 and 4). The figures printed in the publication of Becht are considered to read on a user interface as instantly claimed.
Regarding claim 37, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 37 further adds that the processor-executable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to present the at least some of the data used in training the at least one machine model and one or more groups of the biological particles on a user interface. Becht teaches displaying the clustering from Hypergate, t-SNE and Phenograph on the same graph (p. 305, col. 2, par. 1; Fig. 4B).
Regarding claim 38, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 38 further adds determining whether a rate of correct answers of the at least one machine learning model exceeds a threshold value. Becht teaches using the trained models to predict the class the test set of data to compute the corresponding F1 scores and accuracies (p. 304, col. 1, par. 1).
B. Claims 15-22 and 24-33 are rejected under 35 U.S.C. 103 as being unpatentable over Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS) as evidenced by Radisic et al. (International Journal of Nanomedicine, 2006, 1(1):3-14; cited on the Jun 13 2024 IDS) in view of Aghaeepour et al. (Bioinformatics, 2018, 34(23):4131-4133; and Supplemental Methods; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
The prior art to Becht discloses Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity (abstract). Becht, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows.
Claim 15 discloses a sorting system comprising: a photodetector array configured to receive light emitted from one or more biological particles; at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform the method. Claim 24 discloses an information processing method. Claim 30 discloses at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor in communication with a sorting apparatus, cause the at least one hardware processor to perform the method. Becht teaches that Hypergate is an algorithm which is implemented computationally and available on CRAN (abstract), which is considered to read on the instant system and non-transitory computer-readable storage medium. Becht teaches the use of flow cytometry to collect fluorescent data from single cells (abstract; p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-4). It is considered that a light source to irradiate laser light to the biological particles and a photodetector configured to obtain the light emitted from the biological particles are inherent features of a flow cytometer which analyzes fluorescently labeled cells, as evidenced by Radisic (p .5, col. 2, par. 3 through p. 7, col. 1, par. 1).
The method steps of claims 15, 24, and 30 comprise:
obtaining data indicating light emitted from biological particles and received by a photodetector array;
Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on obtaining optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification at least at [0003-0006]. It is considered that a light source to irradiate laser light to the biological particles and a photodetector configured to obtain the light emitted from the biological particles are inherent features of a flow cytometer which analyzes fluorescently labeled cells, as evidenced by Radisic (p .5, col. 2, par. 3 through p. 7, col. 1, par. 1).
using the data and at least one machine learning model to generate instructions for controlling a sorting apparatus to sort one or more of the one or more biological particles, wherein the at least one machine learning model was trained using training data corresponding to one or more groups of biological particles determined based on a compressed format of the training data; and
Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a compression format of the training data as instantly claimed. Such an interpretation is supported by the instant specification at least at [0062-0063]. Becht teaches that the inputs of Hypergate for training are the dataset, which is an expression matrix of events and parameters (i.e., optical data), and a cluster of interest which has been identified from clustered data produced by t-SNE (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5; p. 304, col. 1, par. 1-4). Becht teaches training Hypergate to obtain a gating strategy that identifies a malignant population of cells in a sample and applying these gating strategies to follow-up samples (p. 304, col. 1, par. 4 through col. 2, par. 1; p. 307, col. 1 through p. 308, col. 1, par. 3; Fig. 6). As Becht teaches that Hypergate optimizes sorting strategies (p. 307, col. 1, par. 4) and general methods for cell sorting (p. 307, col. 2, par. 2), it is considered that Becht fairly teaches generating an output specifying an indication to sort one or more of the biological particles as instantly claimed.
Although Becht teaches training Hypergate, Becht does not explicitly teach that Hypergate is a machine learning model. See below for teachings by Aghaeepour regarding a machine learning model.
transmitting the instructions to the sorting apparatus (claims 15 and 24); controlling the sorting apparatus based, at least in part, on the instructions, to sort the one or more biological particles (claim 30).
Becht does not teach this limitation.
However, the prior art to Aghaeepour discloses GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time (abstract). Aghaeepour teaches that GateFinder workflow begins with the researcher selecting a target cell population of interest such as a manually gated sub-population or the output of a clustering algorithm (i.e. groups of biological particles output from a data compression process), then GateFinder takes a randomly selected sub-sample of the complete high-dimensional dataset and projects it in all possible two-dimensional scatter plots, eliminates outliers, constructs gates around the target cells on the scatter plots, find the gate that achieves the best enrichment of the target cells, and repeats the steps until all markers have been exhausted (p. 4132, col. 1, par. 2). Aghaeepour teaches that GateFinder uses a supervised feature selection algorithm (p. 4132, col. 1, par. 2), which reads on a machine learning model as instantly claimed. Aghaeepour teaches that one of the practical uses for the concise visual description of a target population includes designing fluorescence activated cell sorting strategies for physical isolation of cells (p. 4131, col. 2, par. 1), which is considered to read on applying trained GateFinder on subsequent optical data to sort the subsequent biological particles, as instantly claimed.
Regarding claims 15, 24, and 30, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Becht as evidenced by Radisic with Aghaeepour because both references disclose methods for learning models for gating or classifying populations of cells in flow cytometry data. The motivation to use the supervised feature selection algorithm of Aghaeepour in the method of Becht would have been to limit the search to the most relevant markers, as taught by Aghaeepour (p. 4132, col. 1, par. 2).
Regarding claim 16, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 as described above. Claim 16 further adds that the sorting apparatus is a flow cytometer configured to perform sorting of the one or more of the one or more biological particles based, at least in part, on the instructions. Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), but does not teach a sorting apparatus as instantly claimed.
However, Aghaeepour teaches that one of the practical uses for the concise visual description of a target population includes designing fluorescence activated cell sorting strategies for physical isolation of cells (p. 4131, col. 2, par. 1), which is considered to read on a sorting apparatus as instantly claimed.
Regarding claims 17 and 25, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 and the information processing method of claim 24 as described above. Claims 17 and 25 further add that the data indicating the light received by the photodetector array includes information identifying a spectrum of light for each of one or more biological particles. Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a spectrum of light) (p. 304, col. 1, par. 2).
Regarding claims 18 and 32, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 and the at least one non-transitory computer-readable storage medium of claim 30 as described above. Claims 18 and 32 further add that the compressed format of the training data comprises a plurality of groups of the biological particles generated by performing a clustering process on the training data. As Becht teaches that t-SNE defines clusters of cells (p. 303, col. 2, par. 5), it is considered that Becht fairly teaches this limitation.
Regarding claims 19 and 33, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 and the at least one non-transitory computer-readable storage medium of claim 30 as described above. Claims 19 and 33 further add that the compressed format of the training data comprises data having fewer dimensions than the training data. As Becht teaches performing t-SNE defines as described above (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), it is considered that Becht fairly teaches this limitation. Such an interpretation is supported by the instant specification at least at [0062-0063].
Regarding claims 20-21, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 as described above. Claim 20 further adds that controlling the sorting apparatus based, at least in part, on the instructions further comprises separating a first biological particle into a first group of biological particles. Claim 21 further adds that controlling the sorting apparatus based, at least in part, on the instructions further comprises separating a second biological particle into a second group of biological particles. Becht does not teach these limitations.
However, Aghaeepour teaches that one of the practical uses for the concise visual description of a target population includes designing fluorescence activated cell sorting strategies for physical isolation of cells (p. 4131, col. 2, par. 1), which is considered to read on the instant claim.
Regarding claims 22 and 28, Becht as evidenced by Radisic and in view of Aghaeepour teaches the sorting system of claim 15 and the information processing method of claim 24 as described above. Claims 22 and 28 further add applying a data compression process to the data; outputting, based on a result of the data compression process, the one or more groups of the biological particles; and using at least some of the data corresponding to the one or more groups of the biological particles as the training data to train the at least one machine learning model. Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification at least at [0003-0006]. Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by the instant specification at least at [0062-0063]. Becht teaches identifying (i.e., outputting) clusters of interest defined by t-SNE (i.e., one or more groups of the biological particles) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5). Becht teaches that the inputs of Hypergate are the dataset, which is an expression matrix of events and parameters (i.e., optical data), and a cluster of interest which has been manually identified from clustered data (i.e., information regarding sorting of the biological particles specified based on the compressed data) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5).
Regarding claim 26, Becht as evidenced by Radisic and in view of Aghaeepour teaches the information processing method of claim 24 as described above. Claim 26 further adds that the biological particles include at least one biological particle chosen from a cell, a microorganism, a virus, a fungus, an organelle, and a biological polymer. Becht teaches that Hypergate provides gating strategies for cell populations (abstract).
Regarding claim 27, Becht as evidenced by Radisic and in view of Aghaeepour teaches the information processing method of claim 24 as described above. Claim 27 further adds that one or more of the biological particles is labeled with a fluorescent dye. Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a fluorescent dye) (p. 304, col. 1, par. 2).
Regarding claim 29, Becht as evidenced by Radisic and in view of Aghaeepour teaches the information processing method of claims 24 and 28 as described above. Claim 29 further adds receiving an input selecting a first group from among the one or more groups of the biological particles, and wherein using at least some of the data further comprises using data corresponding to the first group. Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE (p. 303, col. 2, par. 5), which reads on user input selecting a first group of the biological particles and using that data to train the statistical method.
Regarding claim 31, Becht as evidenced by Radisic and in view of Aghaeepour teaches the at least one non-transitory computer-readable storage medium of claim 30 as described above. Claim 31 further adds that the at least one statistical model comprises a classifier chosen from a random forest classifier and a support vector machine classifier. Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1).
C. Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Becht in view of Aghaeepour, as applied to claims 1 and 34 above, and in further view of Novo et al. (Cytometry Part A 2013, 83(5):508-520; previously cited). Any newly recited portions are necessitated by claim amendment.
Regarding claim 35, Becht in view of Aghaeepour teaches the information processing system of claim 1 as described above. Claim 35 further adds that the fluorescence separation is performed by weighted least squares method, which Becht does not teach.
However, Novo discloses a study of methods to unmix (i.e., separate) fluorescence data from flow cytometry (abstract). Novo teaches a weighted least-squares solution for unmixing (abstract; p. 509, col. 2, par. 2; p. 512, col. 2, par. 2 through p. 513, col. 1).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Becht in view of Aghaeepour with the method of Novo because each reference discloses methods for analyzing fluorescence flow cytometry data from cells. The motivation would have been to use flow cytometer with a multispectral detector array and to use a technique to unmix the resulting fluorescence signals using a method which avoids negative values in biomarker detection, as taught by Novo (abstract).
Response to Applicant Arguments
With respect to Applicant’s arguments under 35 USC 103, the arguments have been fully considered but are moot in view of the new grounds of rejection set forth above as necessitated by claim amendment herein.
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.
A. Instant claims 1-22 and 24-38 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 and 5-11 of U.S. Patent No. 11,137,338 (reference patent) in view of Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS). Any newly recited portions are necessitated by claim amendment.
Regarding instant claims 1, 13-15, 24, and 30, reference claims 1, 7-9, and 10-11 disclose the limitations of instant claims 1 and 19-20 except for applying a data compression process to data indicating light emitted from biological particles.
However, the prior art to Becht discloses Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity (abstract). Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification as published at least at [0003-0006]. Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by instant claim 2 and the instant specification as published at least at [0051; 0061-0063], which provides examples of data compression processing including dimensional compression, clustering and grouping, including the use of algorithms including t-SNE.
Regarding instant claim 2-3, 18-19, and 32-33, the reference patent does not disclose the limitations of instant claim 2-3, 18-19, and 32-33.
However, Becht teaches performing t-SNE on datasets for input to Hypergate (p. 303, col. 2, par. 3 and 5), which is considered to read on clustering and reducing a number of dimensions of the data.
Regarding instant claims 4-5 and 29, the reference patent does not disclose the limitations of instant claims 4-5 and 29.
However, Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE (p. 303, col. 2, par. 5), which reads on user input selecting a first group of the biological particles and using that data to train the statistical method.
Regarding instant claims 6 and 12, reference claim 3 discloses the limitation of claims 6 and 12.
Regarding instant claims 7 and 16, reference claim 7 discloses the limitation of claims 7 and 16, as evidenced by the reference disclosure at (col. 6, lines 33-37), which discloses that the particle sorting system includes a flow cytometer.
Regarding instant claims 8, 17, and 25, reference claims 1, 5, 7, and 10-11 discloses the limitation of claims 8, 17, and 25.
Regarding instant claims 9 and 26, reference claim 6 discloses the limitation of claims 9 and 26.
Regarding instant claims 10 and 27, reference claim 6 discloses the limitation of claims 10 and 27.
Regarding instant claims 11 and 31, the reference patent does not disclose the limitations of instant claims 11 and 31.
However, Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1).
Regarding instant claims 16 and 20-21, reference claims 8-9 discloses the limitation of the claims. It is considered that the sorting instructions recited in claim 7 and the sorting mechanism of claims 8-9 inherently discloses sorting a first biological particle into a first group of biological particles and a second biological particle into a second group of biological particles as instantly claimed.
Regarding instant claims 22 and 28, the reference patent does not disclose the limitations of instant claims 22 and 28.
However, Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification at least at [0003-0006]. Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by the instant specification at least at [0062-0063]. Becht teaches identifying (i.e., outputting) clusters of interest defined by t-SNE (i.e., one or more groups of the biological particles) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5). Becht teaches that the inputs of Hypergate are the dataset, which is an expression matrix of events and parameters (i.e., optical data), and a cluster of interest which has been manually identified from clustered data (i.e., information regarding sorting of the biological particles specified based on the compressed data) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5).
Regarding instant claims 34-35, reference claim 2 discloses the limitation of claims 34-35.
Regarding instant claims 36-37, the reference patent does not disclose the limitations of instant claims 36-37.
However, Becht teaches mapping two of the t-SNE parameters (i.e., three-dimensional or smaller area) to identify clusters of interest (Fig. 1-2 and 4). The figures printed in the publication of Becht are considered to read on a user interface as instantly claimed. Becht teaches displaying the clustering from Hypergate, t-SNE and Phenograph on the same graph (p. 305, col. 2, par. 1; Fig. 4B).
Regarding instant claim 38, reference claim 3 discloses the limitation of claim 38 by reciting “designate a margin of the process target range”. The reference specification discloses “When the margin is set on the process target range according to this embodiment, it is possible to control false positive situations by relaxing or tightening discrimination between cells” at col. 12, lines 57-67, which reads on determining whether a rate of correct answers of the at least one statistical model exceeds a threshold.
Regarding instant claims 1-22 and 24-38, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference patent and Becht because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to perform dimensionality reduction techniques on the optical data would have been to analyze high-dimensional data in order to gate cells, where classical gating techniques are not appropriate, as taught by Becht (p. 302, col. 1, par. 2-3).
B. Instant claims 1-22 and 24-38 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 9-11, 13, 15, 19, and 22-24 of copending Application No. 17/468,368 (reference application; now allowed as US 12,287,275) in view of Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS). Any newly recited portions are necessitated by claim amendment.
Regarding instant claims 1, 13-15, 24, and 30, reference claims 1, 10, 15, 19, and 22-24 disclose the limitations of instant claims 1 and 19-20 except for applying a data compression process to data indicating light emitted from biological particles.
However, the prior art to Becht discloses Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity (abstract). Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification as published at least at [0003-0006]. Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by instant claim 2 and the instant specification as published at least at [0051; 0061-0063], which provides examples of data compression processing including dimensional compression, clustering and grouping, including the use of algorithms including t-SNE.
Regarding instant claim 2-3, 18-19, and 32-33, the reference patent does not disclose the limitations of instant claim 2-3, 18-19, and 32-33.
However, Becht teaches performing t-SNE on datasets for input to Hypergate (p. 303, col. 2, par. 3 and 5), which is considered to read on clustering and reducing a number of dimensions of the data.
Regarding instant claims 4-5 and 29, reference claim 4 teaches the limitations of claims 4-5 and 29.
Regarding instant claims 6 and 12, reference claim 11 discloses the limitation of claims 6 and 12.
Regarding instant claim 7, the reference patent does not disclose the limitations of instant claim 7.
However, Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3).
Regarding instant claims 8, 17, and 25, reference claims 1, 13, 15, and 22-24 discloses the limitation of claims 8, 17, and 25.
Regarding instant claims 9 and 26, reference claim 10 discloses the limitation of claims 9 and 26.
Regarding instant claims 10 and 27, reference claims 1 and 10 discloses the limitation of claims 10 and 27. As reference claim 1 recites “obtaining fluorophore information by performing a calculation based on detection data and reference spectra of plural types of fluorophores; outputting the fluorophore information to receive information on a process target; generating teaching data by associating with the detection data one or more groups of particles, based on the fluorophore information”, and reference claim 10 recites “wherein the particles are cells”, it is considered that the particles, or cells, would have had to be labelled with a fluorescent dye to provide fluorophore information.
Regarding instant claims 11 and 31, the reference patent does not disclose the limitations of instant claims 11 and 31.
However, Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1).
Regarding instant claims 16 and 20-21, reference claims 1, 9, 15, 19, and 22-24 discloses the limitation of the claims except for the flow cytometer.
However, Becht teaches flow-cytometry datasets (p. 302, col. 1, par. 4), which is considered to have been inherently generated by a flow cytometer.
Regarding instant claims 22 and 28, the reference patent does not disclose the limitations of instant claims 22 and 28.
However, Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3), which is considered to read on optical data indicative of light emitted from biological particles as instantly claimed. Such an interpretation is supported by the instant specification at least at [0003-0006]. Becht teaches processing the data with dimensionality reduction techniques, including t-SNE, for visualization (p. 302, col. 1, par. 2; p. 303, col. 2, par. 3), which is considered to read on a data compression process as instantly claimed. Such an interpretation is supported by the instant specification at least at [0062-0063]. Becht teaches identifying (i.e., outputting) clusters of interest defined by t-SNE (i.e., one or more groups of the biological particles) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5). Becht teaches that the inputs of Hypergate are the dataset, which is an expression matrix of events and parameters (i.e., optical data), and a cluster of interest which has been manually identified from clustered data (i.e., information regarding sorting of the biological particles specified based on the compressed data) (Fig. 1C; p. 302, col. 2, par. 3-4; p. 303, col. 2, par. 2-5).
Regarding instant claims 34-35, reference claim 3 discloses the limitation of claims 34-35.
Regarding instant claims 36-37, the reference patent does not disclose the limitations of instant claims 36-37.
However, Becht teaches mapping two of the t-SNE parameters (i.e., three-dimensional or smaller area) to identify clusters of interest (Fig. 1-2 and 4). The figures printed in the publication of Becht are considered to read on a user interface as instantly claimed. Becht teaches displaying the clustering from Hypergate, t-SNE and Phenograph on the same graph (p. 305, col. 2, par. 1; Fig. 4B).
Regarding instant claim 38, reference claim 11 discloses the limitation of claim 38 by reciting “designate a margin of the process target range”. The reference specification as published discloses “When the margin is set on the process target range according to this embodiment, it is possible to control false positive situations by relaxing or tightening discrimination between cells” at [0136], which reads on determining whether a rate of correct answers of the at least one statistical model exceeds a threshold.
Regarding instant claims 1-22 and 24-38, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference patent and Becht because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to perform dimensionality reduction techniques on the optical data would have been to analyze high-dimensional data in order to gate cells, where classical gating techniques are not appropriate, as taught by Becht (p. 302, col. 1, par. 2-3).
C. Instant claims 1, 3-5, 8-10, 13-15, 17, 19, 22, 24-30, 33-36, and 38 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 10, 16, and 18-20 of copending Application No. 18/428,160 (reference application). Instant claims 2, 6-7, 11-12, 18, 31-32, and 37 are unpatentable over the reference application, as applied to claims 1, 15, 24, and 30, and in further view of Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS). Instant claims 16 and 20-21 are unpatentable over the reference application, as applied to claims 1 and 4, and in further view of Lai et al. (US 2020/0105376; priority to Oct 1 2018; cited on the Sep 13 2024 IDS). Any newly recited portions are necessitated by claim amendment.
Regarding instant claims 1, 13-15, 24, and 30, reference claims 1, 12, and 19-20 disclose the limitations of instant claims 1, 13-15, 24, and 30.
Regarding instant claim 2, 18, and 32, the reference application does not disclose the limitations of instant claim 2, 18, and 32.
However, Becht teaches performing t-SNE on datasets for input to Hypergate (p. 303, col. 2, par. 3 and 5), which is considered to read on clustering as instantly claimed.
Regarding instant claim 3, 19, and 33, reference claims 2-3 disclose the limitations of instant claim 3, 19, and 33.
Regarding instant claims 4-5 and 29, reference claims 1 and 19-20 disclose the limitations of claims 4-5 and 29.
Regarding instant claims 6 and 12, the reference application does not disclose the limitations 6 and 12.
However, Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE and using the parameters chosen by the authors (p. 303, col. 2, par. 5), which reads on input specifying a range for at least one group of biological particles and using that data to train the statistical method. As Becht teaches that Hypergate is an algorithm which is implemented computationally and available on CRAN (abstract), it is considered that they would have to input the parameters through a user interface as instantly claimed.
Regarding instant claim 7, the reference application does not disclose the limitations of instant claim 7.
However, Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3).
Regarding instant claims 8, 17, and 25, reference claim 18 discloses the limitation of claims 8, 17, and 25.
Regarding instant claims 9 and 26, reference claim 16 discloses the limitation of claims 9 and 26.
Regarding instant claims 10 and 27, reference claim 4 discloses the limitation of claims 10 and 27.
Regarding instant claims 11 and 31, the reference application does not disclose the limitations of instant claims 11 and 31.
However, Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1).
Regarding instant claims 16 and 20-21, reference claims 1 and 19-20 discloses the limitation the claims except for the flow cytometer.
However, the prior art to Lai discloses methods and systems for a deep-learning platform for sorting cell populations (abstract). Lai teaches selecting a machine learning model which was trained based on similar phenotype information as indicated in the flow cytometry data, and applying the machine learning model to the flow cytometry data (abstract; claims 1, 8, and 15; [0042-0043]. Lai teaches that the trained machine learning models may analyze the input information and generate output classification information [0060]. Lai teaches instructing a flow cytometer to sort cell populations according to the assigned classifications (claim 19; [0043]).
Regarding instant claims 22 and 28, reference claims 1 and 19-20 disclose the limitations of instant claims 22 and 28.
Regarding instant claims 34-35, reference claims 4-5 discloses the limitation of claims 34-35.
Regarding instant claim 36, reference claim 14 discloses the limitation of claim 36.
Regarding instant claims 37, the reference application does not disclose the limitations of instant claims 37.
However, Becht teaches displaying the clustering from Hypergate, t-SNE and Phenograph on the same graph (p. 305, col. 2, par. 1; Fig. 4B). The figures printed in the publication of Becht are considered to read on a user interface as instantly claimed.
Regarding instant claim 38, reference claim 10 discloses the limitation of claim 38.
Regarding instant claims 2, 6-7, 11-12, 16, 31-32, and 37, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application and Becht because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to perform dimensionality reduction techniques on the optical data would have been to analyze high-dimensional data in order to gate cells, where classical gating techniques are not appropriate, as taught by Becht (p. 302, col. 1, par. 2-3).
Regarding instant claims 16 and 20-21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with Lai because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to apply the trained machine learning model taught by the reference application to subsequent data would have been to select a particular machine learning model which is optimized, or otherwise advantageous, for the input information, while applying automatic gating tools to avoid user introduced variability, as taught by Lai [0005; 0043].
This is a provisional nonstatutory double patenting rejection.
D. Instant 1, 3-5, 13-15, 19, 22, 24, 28-30, and 33 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 9-11, and 12-15 of copending Application No. 18/570,354 (reference application). Instant claims 2, 6-12, 17-18, 25-27, 31-32, and 36-38 are unpatentable over the reference application, as applied to claims 1, 15, 24, and 30, and in further view of Becht et al. (Bioinformatics, published 21 Jun 2018, 35(2):301-308; cited on the Jun 13 2024 IDS). Instant claims 16 and 20-21 are unpatentable over the reference application, as applied to claims 1 and 15, and in further view of Lai et al. (US 2020/0105376; priority to Oct 1 2018; cited on the Sep 13 2024 IDS). Instant claims 34-35 are unpatentable over the reference application, as applied to claim 1, and in further view of Novo et al. (Cytometry Part A 2013, 83(5):508-520; ). Any newly recited portions are necessitated by claim amendment.
Regarding instant claims 1, 13-15, 24, and 30, reference claims 1, 10, and 14-15 disclose the limitations of instant claims 1, 13-15, 24, and 30.
Regarding instant claim 2, 18, and 32, the reference application does not disclose the limitations of instant claim 2, 18, and 32.
However, Becht teaches performing t-SNE on datasets for input to Hypergate (p. 303, col. 2, par. 3 and 5), which is considered to read on clustering as instantly claimed.
Regarding instant claim 3, 19, and 33, reference claims 1-4 and 14-15 disclose the limitations of instant claim 3, 19, and 33.
Regarding instant claims 4-5 and 29, reference claim 11 discloses the limitations of claims 4-5 and 29.
Regarding instant claims 6 and 12, the reference application does not disclose the limitations 6 and 12.
However, Becht teaches applying Hypergate on the cluster of macrophages defined by t-SNE and using the parameters chosen by the authors (p. 303, col. 2, par. 5), which reads on input specifying a range for at least one group of biological particles and using that data to train the statistical method. As Becht teaches that Hypergate is an algorithm which is implemented computationally and available on CRAN (abstract), it is considered that they would have to input the parameters through a user interface as instantly claimed.
Regarding instant claim 7, the reference application does not disclose the limitations of instant claim 7.
However, Becht teaches the analysis of high-dimensional flow cytometry datasets generated from biological samples labeled with fluorochrome-conjugated antibodies (p. 302, col. 1, par. 4; p. 304, col. 1, par. 2-3).
Regarding instant claims 8, 17, and 25, the reference application does not disclose the limitations of claims 8, 17, and 25.
However, Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a spectrum of light) (p. 304, col. 1, par. 2).
Regarding instant claims 9 and 26, the reference application does not disclose the limitations of claims 9 and 26.
However, Becht teaches that Hypergate provides gating strategies for cell populations (abstract).
Regarding instant claims 10 and 27, the reference application does not disclose the limitations of claims 10 and 27.
However, Becht teaches analyzing data from cells labelled with fluorochrome-conjugated anti-bodies (i.e., a fluorescent dye) (p. 304, col. 1, par. 2).
Regarding instant claims 11 and 31, the reference application does not disclose the limitations of instant claims 11 and 31.
However, Becht teaches training multiple classifiers on training data, including support vector machines and a random forest (p. 304, col. 1, par. 1).
Regarding instant claims 16 and 20-21, reference claims 1, 10-11, and 19-20 discloses the limitation of the claims except for the flow cytometer.
However, the prior art to Lai discloses methods and systems for a deep-learning platform for sorting cell populations (abstract). Lai teaches selecting a machine learning model which was trained based on similar phenotype information as indicated in the flow cytometry data, and applying the machine learning model to the flow cytometry data (abstract; claims 1, 8, and 15; [0042-0043]. Lai teaches that the trained machine learning models may analyze the input information and generate output classification information [0060]. Lai teaches instructing a flow cytometer to sort cell populations according to the assigned classifications (claim 19; [0043]).
Regarding instant claims 22 and 28, reference claims 1 and 14-15 disclose the limitations of instant claims 22 and 28.
Regarding instant claims 34-35, the reference application does not disclose the limitations of claims 34-35.
However, Novo discloses a study of methods to unmix fluorescence data from flow cytometry (abstract). Novo teaches a weighted least-squares solution for unmixing (abstract; p. 509, col. 2, par. 2; p.512, col. 2, par. 2 through p. 513, col. 1).
Regarding instant claims 36-37, the reference application does not disclose the limitations of instant claims 36-37.
However, Becht teaches mapping two of the t-SNE parameters (i.e., three-dimensional or smaller area) to identify clusters of interest (Fig. 1-2 and 4). The figures printed in the publication of Becht are considered to read on a user interface as instantly claimed. Becht teaches displaying the clustering from Hypergate, t-SNE and Phenograph on the same graph (p. 305, col. 2, par. 1; Fig. 4B).
Regarding instant claim 38, the reference application does not disclose the limitations of instant claim 38.
However, Becht teaches using the trained models to predict the class the test set of data to compute the corresponding F1 scores and accuracies (p. 304, col. 1, par. 1).
Regarding instant claims 2, 6-12, 17-18, 25-27, 31-32, and 36-38, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application and Becht because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to perform dimensionality reduction techniques on the optical data would have been to analyze high-dimensional data in order to gate cells, where classical gating techniques are not appropriate, as taught by Becht (p. 302, col. 1, par. 2-3).
Regarding instant claims 16 and 20-21, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application with Lai because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation to apply the trained machine learning model taught by the reference application to subsequent data would have been to select a particular machine learning model which is optimized, or otherwise advantageous, for the input information, while applying automatic gating tools to avoid user introduced variability, as taught by Lai [0005; 0043].
Regarding instant claims 34-35, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the reference application and Novo because each reference discloses methods for analyzing fluorescence data from flow cytometry for cell sorting applications. The motivation would have been to use a technique to unmix the flow cytometer fluorescence signals using a method which avoids negative values in biomarker detection, as taught by Novo (abstract).
This is a provisional nonstatutory double patenting rejection.
Response to Applicant Arguments
At p. 15-17, Applicant submits that the double patenting rejections are improper because the rejections do not perform a proper analysis which relies on obviousness of the examined claim over the reference claim, but rather the secondary references form the base from which the obviousness analysis proceeds.
It is respectfully submitted that this is not persuasive. As set forth in the above rejections, the instant claims are rejected based on the disclosure of the reference claims and in view of the secondary references, as submitted by Applicant and outlined in MPEP 804(II)(B) as being proper. As Applicant has not pointed out exactly where or how the rejections are considered to be improper, it is not clear what parts of the rejection Applicant considers as improper.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.N.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685