DETAILED ACTION
Notice of 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.
Status of the Claims
Claims 1-14 are pending.
Claims 1 is independent.
Claims 1-7, 10-11 and 13-14 are objected to.
Claims 1-14 are rejected.
Priority
This US Application 17/875,319 (07/27/2022) claims no benefit herein, as reflected in the filing receipt mailed on 08/10/22. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-14 is 07/27/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/27/2022 was considered.
Claim objections
Claims 1-7, 10-11 and 13-14 are objected to because of the following informalities related to format/grammar/punctuation. Appropriate correction is required.
Claim 1 presents inconsistent formatting.
As set forth in 37 CPR 1.75, each element or step of the claim should be separated by a line indentation (608.01(m) Form of Claims). Sub-steps/elements should be indented from their parent step/element. For example, the recited "wherein each of the one or more event populations …" (7th wherein clause) should be indented from its parent step "one or more event populations …" (6th claim element). In contrast, "a data structure" (5th claim element), which is the parent step of "wherein the area comprises" (5th wherein clause), does not present the same issue. This rule should be applied throughout the claims as needed.
The recited "events to the one of" is a continuation of step (1), and it is not indented, which is inconsistent with the remainder of the claim, such as the indentation of "event density" (1st wherein clause in step 1), where the continuing line of a step is indented.
The issue above repeats for steps 2-5 in claim 1.
In claim 1, the main elements should be, and are indeed separated by semi-colons, including a semi-colon before each additional main element. However, since the "wherein" clauses are subordinate to the main elements, then commas should separate the "wherein" clauses when there are three or more, as for the first sub-list of "wherein" clauses, rather than the present semi-colons between "wherein" clauses.
In claim 1, the recited "(2) …one or more of …" should read "(2) …one or more of: …" for proper punctuation preceding a list of elements. Claim 1 step (3) and claim 13 repeat the same issue.
claim 3 repeats the issue above for "the parameter value is one of."
claims 7, 11 and 14 repeat the issue above for "the group consisting of."
In claim 2, the recited " … ; and, wherein …" should read " … ; and wherein …" for proper punctuation. Claims 3, 5-6, 10, and 12 repeat the issue above.
Claim 4 recites an extra space between "closest population" and "is between".
Claim interpretation
112(f) interpretation of particular recitations
Recited instances of "computing system" (claims 1, 4 and 11)
The above recitations include means (or an equivalent, nonce term, here "system") and function and/or result (here "computing").
However, the recitation does not invoke 112/f because it is interpreted as well-known. MPEP 2181.I.A,3rd para. pertains with analogy to structures having "sufficiently definite meaning," such as "filters" and "brakes."
Recited instances of "user interface" (claim 1)
The above recitations include means (or an equivalent, nonce term, here "interface") and function and/or result (here "user" interpreted as the function of data transmission. However, the recitation does not invoke 112/f because it is interpreted as well-known. MPEP 2181.I.A,3rd para. pertains with analogy to structures having "sufficiently definite meaning," such as "filters" and "brakes."
Recited instances of "data structure" (claim 1)
The above recitations include means (or an equivalent, nonce term, here "structure") and function and/or result (here "data" interpreted as the function of data storage).
The above recitations are not sufficiently well-known and not accompanied by sufficient structure in the claims to prevent invoking. Therefore, each is interpreted as invoking.
Having invoked, each above recitation has been analyzed as clearly linking to sufficient structure in the specification, as supported at ([51]). Thus, the above recitations have been interpreted as properly invoking 112(f).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims
particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claim 1, each "wherein" clause subordinate to a main element, if recited as two or more successive "wherein" clauses, creates a grammatical sub-list of "wherein" clauses. Each sub-list requires a conjunction to define the relationship among the clauses in each sub-list. These conjunctions are lacking in two instances, so that the relationships are unclear, e.g. as to "or vs. "and," rendering the claim indefinite. For example, the three "wherein" clauses subordinate to the 1st element "a datastore." The relationship between these clauses is unclear as to "or" vs. "and." Similarly, a conjunction is missing between the two "wherein" clauses after "a data structure."
In claim 1, claimed elements lack clear structure. Claim 1 is interpreted as claiming a 101 machine or manufacture, here an "apparatus." As such, it is interpreted according to its recited structure. The required structure is unclear corresponding to the following claim elements comprised directly by the "apparatus:" "one or more marker pairs...," "a data structure...," "one or more event populations..." Relatedly, it is ambiguous whether the recited "marker pairs" and "event populations" read on data vs. real elements.
The same issues render claim 5 indefinite regarding the recited "the one or more intervening cluster units."
The same issues render claim 11 indefinite regarding the recited "B-cell population."
In claim 1, claimed process steps lack clear structure. Claim 1 is interpreted as claiming a 101 machine or manufacture, here an "apparatus." As such, it is interpreted according to its recited structure. In the "...event populations" element, in the 2nd wherein clause, it is unclear what is the structure corresponding to the recited "computing system is configured to..." This rejection might be overcome, for example, by amending to recite a storage element comprised by the "computer system," the storage element comprising instructions, the instructions "configured to..." However, while the specification discloses data storage (e.g. [42]), it is not clear that there is support for storage of software, instructions, etc. Similarly, in the following claims, it is unclear what is the structure corresponding to the recited process steps:
claim 2, "is assigned" and "the computing system sorts"
claim 4, "is selected" and "the computing system is configured to determine"
claim 5, "are selected"
claim 7, "are selected" (2nd "wherein" clause)
claim 10, "...is determined..." (two instances) and "...is selected" (two instances)
claim [please continue this list through the rest of the claims 11-14]
In claim 1, the relationship is unclear between the recited "ii) population immunophenotyping" and the "(3) determine an immunophenotype for each event population".
The same issue occurs with the recited "iii) light chain expression ratios" and the "light chain expression ratios" of claim 11, 3rd "wherein" clause.
The same issue occurs with the recited "iv) population size ratios" and the "each... comprises a population size ratio" of claim 12.
The same issue occurs with the recited "i) population classifications" and the "(4)... population classifications" of claim 13. Each of these instances might remedied, for example, by amending to add some form of "the" before the claim 13 recitation so as to clarify the relationship to the earlier instances as being the same.
The following recitations require but lack antecedent basis, rendering their claims indefinite:
a. claim 2, "the plurality of cluster units"
b. claims 7, 11, and 14, "the group consisting of"
In claim 4, the relationship between "the new population distance limit" and "the same population distance limit" is unclear. It is unclear what the "same" population limit refers to.
In claim 7, in the 2nd "wherein" clause, the relationship is unclear between the "iii) one or more randomly selected evaluated intervening cluster units" and the preceding "the one or more evaluated intervening cluster units are selected from..." (beginning of 2nd "wherein" clause), at least because the logic of the recitations appears circular regarding interpretation of "evaluated intervening cluster units." That is, it appears that "evaluated intervening cluster units are selected from randomly selected evaluated intervening cluster units, but the first group of units already appears to include the second. This rejection might be overcome, for example, by amending to clearly distinguish the instances of "evaluated intervening cluster units."
Claim 10 recites "a dim intensity, a moderate intensity, and a bright intensity" in which the recited are terms of relative or vague degree or form of association, with the required definition not recited in claims. MPEP 2173.05(b) pertains. Here, [73] does not disclose a definition for "a dim intensity, a moderate intensity, and a bright intensity" because it discloses "may be", which it is only exemplary. Although claims are interpreted in light of the specification, examples from the specification are not imported into the claims as limitations absent a clearly limiting definition in the specification. (MPEP 2145.VI pertains.)
In claim 13, the recited "i) population classifications" and "iii) light chain expression ratios" should read "i) the population classifications" and "iii) the light chain expression ratios" as these terms have been previously instantiated.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed 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.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
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 (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Claims 1-14 are directed to a 101 machine or manufacture, here an "apparatus," comprising at least one non-transitory element such as "a computing system."
[Step 1: claims 1-14: Yes]
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 (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
Mathematical concepts recited in instant claims 1-4 and 10, include the terms:
• "determine the event population for each of the cluster units" (claim 1);
• "determine the event population for each of the cluster units" (claim 1);
• "determine an immunophenotype for each event population" (claim 1);
• "sorts the plurality of the cluster units according to the cluster event" (claim 2);
• "parameter values/parameter intensities" (claims 1-3 and 10); and
• "determine the event population based on the intervening density" (claim 4).
The above recitations are interpreted as mathematical concepts. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one having ordinary skill in the art. In this instant disclosure, [82-83] describes the claimed clustering analysis for the determination of population clusters based on algorithmic steps that involve percentage calculations (i.e. sorting the cluster events); which indicates the use of math. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• the identified math recitations above;
• "assign each of the clustered events to one of the cluster units" (claim 1);
• "determine one or more population classifications" (claim 1); and
• "generate a diagnostic report" (claim 1).
Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of evaluate data to assign clustered events and determine a classification for a population, writing a report with pen and paper.
Dependent claims 2-14 recite further details about the clustering analysis of "one or more event populations". These dependent claims do not recite any additional non-abstract elements; all reciting further aspects of the information being analyzed or the manner in which that analysis is performed. Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The instant claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)).
[Step 2A Prong One: claims 1-14: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1-2, 4 and 11 recite additional elements that are not abstract ideas:
• "a datastore that stores one or more cytometry datasets" (claim 1);
• "computing system" (claims 1-2, 4, and 11);
• "user interface" (claim 1); and
• "data structure" (claim 1).
The recited limitations in 1-2, 4 and 11 are interpreted to require the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
The claims state nothing more than a generic computer which performs the functions of data gathering (i.e. "data structure" to be informed to the judicial exception); and, therefore the claims do not integrate that abstract idea into a practical application.
Claims directed to "user interface" and "datastore" read on receiving or transmitting data, which constitutes just necessary data gathering and outputting and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination it is not yet the case that any of the Step 2A, Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the first consideration at MPEP 2106.04(d)(1), the record, including for example the specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field. The claims do not yet clearly result in such an improvement (e.g. specification: [69]).
[Step 2A Prong Two: claims 1-14: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 examination 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).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a 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), Versa ta 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)(Il)(i)).
In conclusion, no unconventional additional elements have been identified in the claims under step 2B.
[Step 2B: claims 1-14: No]
Conclusion: Instant claims are directed to non-statutory subject matter
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h). For these reasons, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the abstract idea, so instant claims 1-14 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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)(l) the claimed invention was patented, described in a printed publication, or in public use, on sale,
or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lucchesi ("Computational analysis of multiparametric flow cytometric data to dissect B cell subsets in vaccine studies" Cytometry Part A 97(3):259-267 (2020)), as cited on the attached Form PTO-892.
Lucchesi discloses a computational analysis of multiparametric flow cytometric data performed employing clustering approach to identify group B cells population into different subsets on the basis of their marker expression (pg. 265 col. 1 para. 3). Bullet points indicate the teachings of the instant features over the prior art. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 discloses:
a datastore that stores one or more cytometry datasets, each cytometry dataset comprising a series of events about the fluid sample; wherein each event comprises a plurality of parameter values; wherein each of the plurality of the parameter values is associated with one parameter from a plurality of parameters; wherein the plurality of parameters comprises scatter parameters and antigen parameters;
• Lucchesi teaches the analysis of data acquired on LSR Fortessa X20 flow cytometer - BD Biosciences (i.e. apparatus) (pg. 261 col. 1 para. 1); wherein data is stored in memory (i.e. coupled datastore) (pg. 114 para. 1 cytometer manual); wherein CD38 cells was identified by the marker pair - bright (CD38high) and intermediate (CD38dim) (pg. 263 col. 1 para. 1); wherein parameter selection was performed for each marker (i.e. each marker pair comprising a first parameter and a second parameter; wherein the first parameter and the second parameter are selected from the plurality of parameters) (pg. 261 col. 1 para. 2).
a computing system coupled to the datastore, wherein the computing system operates upon the one or more cytometry datasets; one or more marker pairs, each marker pair comprising a first parameter and a second parameter; wherein the first parameter and the second parameter are selected from the plurality of parameters;
• Lucchesi teaches the computational analysis (i.e. computing system) of multiparametric (i.e. plurality of parameters) flow cytometric data performed employing clustering approach (i.e. one or more clustered events from the series of events) to identify group B cells population into different subsets on the basis of their marker expression (pg. 265 col. 1 para. 3); wherein fluid sample controls are found in the control panel (pg. 18 para. 3 cytometer manual) as evidenced by the LSR Fortessa X20 flow cytometer manual; wherein parameters comprised scatter parameters (pg. 261 col. 1 para. 1); wherein dimensionality reduction parameter were applied for antigen expression analysis (i.e. antigen parameters) (pg. 264 Fig. 3).
a user interface coupled with the computing system; a data structure representing a bivariate coordinate system having an x-axis, a y-axis, and an area; wherein the area comprises units, each of the units having an x-unit range and a y-unit range; wherein the units comprise cluster units and empty units; and, one or more event populations selected from a group consisting of an initial population and one or more subpopulations; wherein each of the one or more event populations comprise one or more clustered events from the series of events;
• Lucchesi teaches the computational analysis of flow cytometric data performed employing clustering approach (i.e. one or more clustered events from the series of events) to identify group B cells population into different subsets (i.e. an initial population and one or more subpopulations) (pg. 265 col. 1 para. 3) with data acquired on LSR Fortessa X20 flow cytometer - BD Biosciences (pg. 261 col. 1 para. 1); wherein workstation allows for user interface (pg. 17 para. 1) as evidenced by the LSR Fortessa X20 flow cytometer manual; wherein Euclidean distance was used in clustering data structure (pg. 261 col. 1 para. 3) and “manual gating” based on the measurement of two parameters was visualized on bidimensional plots (i.e. a data structure representing a bivariate coordinate) (pg. 259 para. 2) depicting a system having an x-axis, a y-axis, and an area; wherein the area comprises units, each of the units having an x-unit range and a y-unit range; wherein the units comprise cluster units and empty units (pg. 262 Fig. 1).
wherein for each marker pair the computing system is configured to:
(1) for each of the one or more event populations, assign each of the clustered events to one of the cluster units based on the parameter value for the first parameter of each clustered event, the parameter value for the second parameter of each clustered event, the x-unit range of each cluster unit, and the y-unit range of each cluster unit; wherein each cluster unit comprises at least one clustered event and a cluster event density representative of the number of clustered events in the cluster unit;
(2) determine the event population for each of the cluster units from the one or more event populations based on one or more of i) the cluster event density, ii) a closest cluster population, iii) a distance to closest population, and iv) one or more intervening density differences;
(3) determine an immunophenotype for each event population based on one or more of i) an antigen median fluorescence intensity for each antigen parameter represented in the event population, ii) an antigen parameter expression, iii) an antigen parameter intensity, and iv) a light chain expression;
(4) determine one or more population classifications based on the immunophenotype for each event population; and,
(5) generate a diagnostic report based on the one or more population classifications.
• Lucchesi teaches cluster analysis based on the measurement of two parameters visualized on bidimensional plots with the first parameter of each clustered event on the x-axis and a second parameter on the y-axis and a cluster event density (i.e. step 2) representative of the number of clustered events in the cluster unit (i.e. step 1) (pg. 262 Fig. 1); wherein cell populations were visualized in a dimensionally reduced space grouped by their immunophenotype similarities (i.e. immunophenotype for each event population – step 3) wherein parameters applied allowed for antigen expression analysis (i.e. an antigen parameter intensity) with relative antigen expression visualized by the color tone - from blue to red (i.e. classifications based on the immunophenotype for each event population - step 4) and surface markers distribution reported via dimensionally reduced space plots with clusters sorted by density of events (i.e. step 5) (pg. 264 Fig. 3).
Claim 2 discloses:
wherein each event represents a cell in the fluid sample; wherein each of the one or more event populations comprises a population size greater than a minimum analyzable population size; wherein the bivariate coordinate system comprises X-count units along the x-axis and Y-count units along the y-axis; wherein each clustered event is assigned to the one cluster unit if the parameter values of the clustered event are within the x-unit range and y-unit range of the cluster unit; and, wherein the computing system sorts the plurality of the cluster units according to the cluster event density of each cluster unit.
• Lucchesi teaches clustering analysis in which metaclusters that were significant in >50% of times were shown as significant (i.e. population size greater than a minimum analyzable population size) (pg. 261 col. 2 para. 3 and pg. 263 Fig. 2). The remaining described recitations are taught in claim 1 above.
Claim 3 discloses:
wherein each antigen parameter is associated with a parameter cutoff value; wherein each of the one or more event populations comprises a quantity of marker pair positive events; wherein the quantity of marker pair positive events is greater than a minimum event count; wherein each of the marker pair positive events comprises the parameter value from the plurality of parameter values greater than the parameter cutoff value; and, wherein the parameter value is one of i) the first marker pair parameter value and ii) the second marker pair parameter value.
• Lucchesi teaches automatic parameters selection for each marker (pg. 261 col. 1 para. 2); wherein thresholds (i.e. parameter cutoffs) to bisect positive and negative cells for each marker expression were automatically set (i.e. each antigen parameter is associated with a parameter cutoff value) (pg. 261 col. 1 para. 3); wherein a heatmap reporting the percentage of positive cells for each marker within the cluster (pg. 261 col. 2 para. 1) visualized as color-scale from blue (0% of positive cells) to red (100%) (i.e. quantity of positive events above zero – minimum event count) with positive and negative cells defined using the threshold estimated with FlowDensity package (pg. 263 col. 1 para. 1); wherein CD38 cells was identified by the marker pair - bright (CD38high) and intermediate (CD38dim) (pg. 263 col. 1 para. 1); wherein parameter selection was performed for each marker (i.e. wherein the parameter value is one of i) the first marker pair parameter value and ii) the second marker pair parameter value) (pg. 261 col. 1 para. 2).
Claim 4 discloses:
wherein the distance to closest population represents the distance that is shortest between the cluster unit and a closest assigned cluster unit from the plurality of cluster units; wherein the closest assigned cluster unit comprises the closest cluster population; wherein the closest cluster population is selected from the one or more event populations; wherein the event population is the same as the closest cluster population if the distance to closest population is less than a same population distance limit; wherein the event population is a new subpopulation if the distance to closest population is greater than a new population distance limit; and, wherein the computing system is configured to determine the event population based on the intervening density differences between the cluster unit and one or more intervening cluster units if the distance to closest population is between the new population distance limit and the same population distance limit.
• Lucchesi teaches a clustering method via a distant matrix representation for all possible clusters where the value zero indicates the maximum correspondence (i.e. distance limit being the shortest between the cluster unit and a closest assigned cluster unit) with all elements being in the intersection between the clusters (i.e. event population is the same as the closest cluster population/intervening cluster units and closest assigned cluster unit from the plurality of cluster units) while the higher the value the more different were the two clusters (i.e. new subpopulation if the distance to closest population is greater than a new population distance limit) (pg. 261 col. 2 para. 3); wherein the distance matrix reported one clustering by row and the other one by columns, calculating each element in the matrix by summing the cells in both clusters subtracted by twice the cells detected in the intersection of the two clusters (i.e. determine the event population based on the intervening density differences between the cluster unit and one or more intervening cluster units if the distance to closest population is between the new population distance limit and the same population distance limit) (pg. 261 col. 2 para. 3).
Claim 5 discloses:
wherein the one or more intervening density differences are density differences between the cluster unit and each of one or more evaluated intervening cluster units; and, wherein the one or more evaluated intervening cluster units are selected from among the one or more intervening cluster units.
• Lucchesi teaches a distance matrix report where one clustering by row and the other one by columns, calculating each element in the matrix by summing the cells in both clusters subtracted by twice the cells detected in the intersection of the two clusters (i.e. wherein the one or more intervening density differences are density differences between the cluster unit and each of one or more evaluated intervening cluster units) (pg. 261 col. 2 para. 3); wherein the value zero indicates the maximum correspondence with all elements being in the intersection between the clusters (i.e. wherein the one or more evaluated intervening cluster units are selected from among the one or more intervening cluster units) (pg. 261 col. 2 para. 3).
Claim 6 discloses:
wherein each of the intervening density differences is associated with one of the evaluated intervening cluster units and with an associated intervening density difference cutoff; wherein the event population is the closest cluster population if each of the intervening density differences is less than or equal to its associated intervening density difference cutoff; and, wherein the event population is the new subpopulation if at least one of the intervening density differences is greater than its associated intervening density difference cutoff.
• Lucchesi teaches a clustering method via a distant matrix representation as a heatmap for all possible clusters, where the value zero indicates the maximum correspondence (i.e. associated intervening density difference cutoff) with all elements being in the intersection between the clusters (i.e. wherein the event population is the closest cluster population if each of the intervening density differences is less than or equal to its associated intervening density difference cutoff) while the higher the value the more different were the two clusters (pg. 261 col. 2 para. 3); wherein the distance matrix reported one clustering by row and the other one by columns, calculating each element in the matrix by summing the cells in both clusters subtracted by twice the cells detected in the intersection of the two clusters (i.e. intervening density differences is associated with one of the evaluated intervening cluster units) (pg. 261 col. 2 para. 3).
Claim 7 discloses:
wherein the x-unit range of each intervening cluster unit is between the x-unit range of the cluster unit and the x-unit range of the closest assigned cluster unit; wherein the y-unit range of each intervening cluster unit is between the y-unit range of the cluster unit and the y-unit range of the closest assigned cluster unit; and,
wherein the one or more evaluated intervening cluster units are selected from the group consisting of i) all intervening cluster units, ii) intervening cluster units that are crossed by a straight line connecting the cluster unit and the closest assigned cluster unit, and iii) one or more randomly selected evaluated intervening cluster units from among the one or more intervening cluster units.
• Lucchesi teaches the described recitation in claim 6 above, as a heatmap is a two-dimensional visualization defined by an X-axis, a Y-axis, and a color scale (i.e. all intervening cluster units).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 8-9 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lucchesi as applied to claim 1 in the 102 rejection above further in view of O'Neill ("Flow cytometry bioinformatics" PLoS computational biology 9(12):e1003365 (2013)), as cited on the attached Form PTO-892.
Claim 8 discloses:
wherein the area is a square and comprises an equal number of units along the x-axis, and along the y-axis.
• O'Neil teaches computational methods for analysis of flow cytometry data, identifying cell populations within it, matching those cell populations across samples (pg. 1 col. 1 para. 1); wherein a system having an x-axis, a y-axis, and an area of 800 units square for the identification of cell populations (pg. 5 Fig. 3).
Claim 9 discloses:
wherein the area comprises 400 units.
• O'Neil teaches he described recitation in claim 8 above.
Rationale for combining (MPEP §2142-2143)
Regarding claims 8-9, 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 Lucchesi in view of O'Neil because all references disclose methods for analyzing flow cytometry data. The motivation would have been to incorporate the ability to quantify a wide range of metabolites (pg. 1 col. 2 paras 1-2 O'Neil).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Lucchesi to the methods by O'Neil because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for analyzing flow cytometry data.
B. Claims 10-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lucchesi as applied to claim 1 in the 102 rejection above further in view of D'Arena ("Quantitative flow cytometry for the differential diagnosis of leukemic B‐cell chronic lymphoproliferative disorders" American journal of hematology 64(4):275-281 (2000)), as cited on the attached Form PTO-892.
Claim 10 discloses:
wherein each of a plurality of antigen expressions is determined based on an antigen MFI value for each of the antigen parameters in the event population; wherein each of the plurality of antigen expressions is selected from a positive antigen expression, a negative antigen expression, and an equivocal antigen expression; wherein each of a plurality of antigen parameter intensities is determined based on the antigen MFI values for an antigen parameter having the positive antigen expression; and, wherein each of the plurality of antigen parameter intensities is selected from a dim intensity, a moderate intensity, and a bright intensity.
• D'Arena teaches the analysis of flow cytometry data involving the calculation of the number of antigen molecules per cell, expressed as antibody binding capacity (ABC) (pg. 275 para. 1) and the correlation between ABC values for each sample and surface membrane immunoglobulin mean fluorescence intensity (MFI) (pg. 276 col. 2 para. 2); wherein all samples expressed CD19 and CD20 antigens with a cut-off $30% of positive cells (i.e. positive antigen expression) (pg. 277 col. 2 para. 1); wherein overlays of fluorescence histograms related to CD20 expression patterns were reported in a range from lowest intermediate to highest expression levels (i.e. antigen parameter intensities is selected from a dim intensity, a moderate intensity, and a bright intensity) (pg. 278 Fig. 3).
Claim 11 discloses:
wherein the event populations further comprise a B-cell population; wherein the B-cell population comprises positive antigen expressions for B-cell parameters; wherein the B-cell parameters are selected from the group consisting of CD19, CD20, CD22, CD79a, CD79b, and combinations thereof; and, wherein the computing system is configured to determine one or more light chain expression ratios for each of the B-cell populations based on a lambda parameter value and a kappa parameter value.
• D'Arena teaches the characterization of CD19, CD20, CD22, CD23, CD79b, and CD5 monoclonal antibodies (pg. 275 para. 1); and the evaluation of the ratio between the mean fluorescence channel of kappa and lambda histogram and the mean fluorescence channel of isotypic control (i.e. determine one or more light chain expression ratios for each of the B-cell populations based on a lambda parameter value and a kappa parameter value) (pg. 277 col. 1 lines 1-2); wherein all samples expressed CD19 and CD20 antigens with a cut-off $30% of positive cells (i.e. positive antigen expression) (pg. 277 col. 2 para. 1).
Claim 12 discloses:
wherein each of the one or more event populations comprises a population size ratio; and, wherein each population size ratio is representative of a ratio of each event population size to the number of events in the series of events.
• D'Arena teaches that a total of ten thousands events were stored as data files from quantification of total population (pg. 276 col. 2 para. 1); wherein the percentage of positive cases for CD5, CD23 and CD79b expression in B-cell are reported (i.e. population size ratio is representative of a ratio of each event population size to the number of events in the series of events) (pg. 276 Fig. 1).
Claim 13 discloses:
wherein the diagnostic report is generated based on one or more of i) population classifications, ii) population immunophenotyping, iii) light chain expression ratios, and iv) population size ratios.
• D'Arena teaches the described recitation in claim 12 above (i.e. population size ratios).
Claim 14 discloses:
wherein the B-cell parameters are further selected from the group consisting of CD24, CD27, PAX5, OCT2, BOB1, immunoglobulin, and combinations thereof.
• D'Arena teaches the described recitation in claim 10 above (i.e. immunoglobulin).
Rationale for combining (MPEP §2142-2143)
Regarding claims 10-14, 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 Lucchesi in view of D'Arena because all references disclose methods for analyzing flow cytometry data. The motivation would have been to measure the absolute numbers of fluorophore molecules per cell in addition to the traditional analysis of the frequency of cells expressing specific antigens (pg. 278 col. 2 para. 2 D'Arena) and achieve refinement for a correct diagnosis (pg. 280 col. 1 para. 5 D'Arena).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Lucchesi to the methods by D'Arena because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for analyzing flow cytometry data.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686