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 .
Status of the Claims
Claims 1-4, 6-11 are currently pending and under exam herein.
Claims 1-4, 6-11 are rejected.
Priority
The application is a national stage application from PCT/CN2020/072044 filed on 1/14/2020. Therefore, the effective filing date of the instant application is 1/14/2020.
Drawings
The Drawings filed on 7/14/2022 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/14/2022 and 7/15/2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Response to Arguments Claim Objections
Claim 2 was modified to include a period. Therefore, the objection was dropped.
Response to Arguments 112(b)
Claim 1 and Claim 2 were amended and the relative term “around” was removed. Therefore, the rejection is withdrawn.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-11 are directed to a method for predicting spatial relations between cells based on single-cell transcriptome sequencing data.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
acquiring a probability matrix P of a cell-cell interaction intensity matrix A based on single-cell transcriptome sequencing data (Mathematical Concepts)
reconstructing a one/two/three-dimensional spatial structure of cell interactions according to the acquired probability matrix P of the cell-cell interaction intensity matrix A (Mathematical Concepts)
obtaining an intercellular action network from the reconstructed three-dimensional spatial structure by setting an intracellular threshold distance from around one cell (Mathematical Concepts)
wherein the cell-cell interaction intensity matrix A is obtained according to a public receptor-ligand database based on the single-cell transcriptome sequencing data; every element in the cell-cell interaction intensity matrix A is divided by Zp, a sum of all elements in the cell-cell interaction intensity matrix A, to obtain the probability matrix P of the cell-cell interaction intensity matrix A,
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I is a total number of cells; K is a total number of ligand-receptor pairs; wL k ,R k represents a chemical binding constant of ligand-receptor pair k; ei L k is an expression level of ligand k in cell i; ei R k is an expression level of receptor k in cell i; ej L k is an expression level of ligand k in cell j; ej R k is an expression level of receptor k in cell j. (Mathematical Concepts)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a model for reconstructing a three-dimensional spatial structure of cell interactions is as follows: minimizing an objective function
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such that:
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wherein; pij is an interaction intensity between cell i and cell j in the probability matrix P of the cell-cell interaction intensity matrix A; qij is a probability of cell j being within the intracellular distance from the cell i; dij is a Euclidean distance between cell i and cell j in a three-dimensional space; yi m is a coordinate of cell i on axis m; and yj m is a coordinate of cell j on axis m. (Mathematical Concepts)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the objective function
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is minimized, cell coordinates are updated using gradient descent, and a gradient direction is calculated for each cell at the present coordinates:
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wherein, C represents the objective function, yi is a present coordinate of cell i on one axis, and yj is a present coordinate of cell j on the same axis; with the gradient direction as a coordinate updating direction, the cell coordinates are updated with a fixed step size, and a plurality of iterations are performed. (Mathematical Concepts)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein when a distance between cell i and cell j is smaller than a minimum distance r between two cells in the three-dimensional space, when pij−qij>0, let pij−qij=s, wherein s is a negative number not smaller than -1. (Mathematical Concepts)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the elements in the probability matrix P of the cell-cell interaction intensity matrix A are:
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(Mathematical Concepts)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein each element in the cell-cell interaction intensity matrix A is an interaction intensity between corresponding cell C1 and cell C2; a relation for the interaction intensity is
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or
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or
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wherein, AC1,C2 represents the cell-cell interaction intensity between cell C1 and cell C2; wA,B represents a weight for an interaction between ligand A and receptor B; AC1 and AC2 represent expression levels of ligand A in cell C1 and cell C2, respectively; BC1 and BC2 represent expression levels of receptor B in cell C1 and cell C2, respectively; K represents a total number of ligand-receptor pairs. (Mathematical Concepts)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the intercellular distance threshold where each cell interacts on average with h cells is determined using the following method: for each cell, the distance to the cell closest to it in the hth order is calculated, and the median distance value for all cells is calculated and set as the intercellular distance threshold. (Mathematical Concepts)
Dependent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the probability matrix P of the cell-cell interaction intensity matrix A obtained is discretized before reconstructing the three-dimensional spatial structure of cell interactions. (Mathematical Concepts)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the expression levels of ligands and receptors are measured using TPM, FPKM, CPM, Counts, TP10K or log 2(TPM+1). (Mathematical Concepts)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the expression levels of ligands and receptors are measured using TPM, FPKM, CPM, Counts, TP10K or log 2(TPM+1). (Mathematical Concepts)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-11 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
There are no additional elements in claims 1-11.
Claims 1-11 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-11 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-11 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1-11 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
Claims 1-11 do not recite any elements in addition to the judicial exception. Therefore, when taken alone, all additional elements in claims 1-11 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the lack of additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-11 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments 101
Applicant claims that the invention solves a specific technical problem and applicant even admits that “provides a method for accurately predicting the three-dimensional spatial relationships between cells using only single-cell transcriptome sequencing data without the need for experimental imaging” This is simply a prediction which any human can look at data and reconstruct it using either a mental process or mathematical relations described in the application. Applicant does not use this prediction for anything that could help integrate it into a practical application.
Examiner states that applicant is simply organizing a mathematical relationship. The MPEP states “iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.” Or “iii. using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential)“ MPEP 2106.
The MPEP also states “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Mental processes performed by humans with the assistance of physical aids such as pens or paper are explained further below with respect to point B. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C. The MPEP also states a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014) were deemed as mental processes. The MPEP also states that using a computer can still be used to perform a mental process.
The MPEP also states that examples that the courts have indicated may not be sufficient to show an improvement to technology include ii. Using well-known standard laboratory techniques to detect enzyme levels in a bodily sample such as blood or plasma, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1355, 1362, 123 USPQ2d 1081, 1082-83, 1088 (Fed. Cir. 2017); ii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Each step applicant did was well known math and in the field. Applicant even admits techniques like TMM are commonly used in singe cell RNA seq.
Examiner also asserts that under the broadest reasonable interpretation there are no additional elements that can be used to integrate the abstract idea into a practical application. Additionally, there is nothing in the claim that prevents a dedicated person skilled in the art of doing the complete process by reading data and performing calculations with a computer. Examiner also asserts that mental processes are repeatable as a person is capable of completing the same mental process in repetition. Additionally, the calculations do not take significant time when using a modern computer as a tool so the number of cells or difficulty of calculations is a moot point when it is possible and doable by a human. Additionally the claims do not require a number of cells so under the BRI it can be a very limited number making calculations by hand as feasible.
Applicant states that “technical effects are specific, tangible and verifiable” and “produce specific, tangible technical effects” however nothing is done with the claimed improvement. Applicant is simply implementing an abstract idea. In order to integrate the claimed invention into a practical application Examiner recommends to further add information on integrating it into “provide technical support for the fields of biomedical research, tumor diagnosis, immunotherapy” as that could potentially provide a practical application as currently applicant is just predicting a relationship (using a mathematical concept and or mental process) and does nothing with that relationship in order to integrate it into a practical application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6-7, 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (Kumar et al. Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics. Cell Reports 2018, 25 (6), 1458-1468.e4.) in view of Maaten et al. (Maaten, et al. Visualizing Data Using T-SNE. Journal of Machine Learning Research 2008, 9 (86), 2579–2605.) and further in view of Ramilowski et al. (Ramilowski et al. A draft network of ligand–receptor-mediated multicellular signalling in human, Nature Communications volume 6, Article number: 7866 (2015)). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of claims 1, Kumar et al. teaches creating matrices of interaction scores between all cel type pairs using sc-RNA data. (pgs. 1459-1465 A method for predicting spatial relations between cells based on single-cell transcriptome sequencing data, comprising: acquiring a probability matrix P of a cell-cell interaction intensity matrix A based on single-cell transcriptome sequencing data (Claim 1)) Kumar et al. also teaches a method to characterize cell-cell interactions involving Tregs in human metastatic melanoma averaged across 19 tumor samples. A score is used which is equivalent of a threshold to determine interactions. (Figure 4, pg 1464, obtaining an intercellular action network from the reconstructed three-dimensional spatial structure by setting an intracellular threshold distance from around one cell. Claim 1) Kumar et al. also teaches a database generated through scRNA-seq (pg. 1459, Results, 1st paragraph) wherein the cell-cell interaction intensity matrix A is obtained according to a public receptor-ligand database based on the single-cell transcriptome sequencing data (Claim 1))
With respect to the limitations of claims 10 and 11, Kumar et al. teaches the use of TPM values (pg. e2, last paragraph, wherein the expression levels of ligands and receptors are measured using TPM, FPKM, CPM, Counts, TP10K or log 2(TPM+1) (Claim 10), wherein the expression levels of ligands and receptors are measured using TPM, FPKM, CPM, Counts, TP10K or log 2(TPM+1) (Claim 11))
Kumar et al. does not explicitly teach
reconstructing a one/two/three-dimensional spatial structure of cell interactions according to the acquired probability matrix P of the cell-cell interaction intensity matrix A (Claim 1)
wherein a model for reconstructing a three- dimensional spatial structure of cell interactions is as follows: minimizing an objective function
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wherein, I is a total number of cells; pij is an interaction intensity between cell i and cell j in the probability matrix P of the cell-cell interaction intensity matrix A; qij is a probability of cell j being within the intracellular distance from cell i; dij is a Euclidean distance between cell i and cell j in a three-dimensional space; yi m is a coordinate of cell i on axis m; yj m is a coordinate of cell j on axis m; (Claim 2)
wherein the objective function
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is minimized, cell coordinates are updated using gradient descent, and a gradient direction is calculated for each cell at the present coordinates:
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wherein, C represents the objective function, yi is a present coordinate of cell i on one axis, and yj is a present coordinate of cell j on the same axis; with the gradient direction as a coordinate updating direction, the cell coordinates are updated with a fixed step size, and a plurality of iterations are performed (Claim 3)
wherein when a distance between cell i and cell j is smaller than a minimum distance r between two cells in the three-dimensional space, when pij−qij>0, let pij−qij=s, wherein s is a negative number not smaller than -1 (Claim 4)
every element in the cell-cell interaction intensity matrix A is divided by Zp, a sum of all elements in the cell-cell interaction intensity matrix A, to obtain the probability matrix P of the cell-cell interaction intensity matrix A,
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I is a total number of cells; K is a total number of ligand-receptor pairs; wL k ,R k represents a chemical binding constant of ligand-receptor pair k; ei L k is an expression level of ligand k in cell i; ei R k is an expression level of receptor k in cell i; ej L k is an expression level of ligand k in cell j; ej R k is an expression level of receptor k in cell j. (Claim 1)
wherein the elements in the probability matrix P of the cell-cell interaction intensity matrix A are:
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(Claim 6)
wherein each element in the cell-cell interaction intensity matrix A is an interaction intensity between corresponding cell C1 and cell C2; a relation for the interaction intensity is
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or
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or
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wherein, AC1,C2 represents the cell-cell interaction intensity between cell C1 and cell C2; wA,B represents a weight for an interaction between ligand A and receptor B; AC1 and AC2 represent expression levels of ligand A in cell C1 and cell C2, respectively; BC1 and BC2 represent expression levels of receptor B in cell C1 and cell C2, respectively; K represents a total number of ligand-receptor pairs (Claim 7)
With respect to the limitations of claim 1, Maaten et al. teaches dimensionality reduction methods convert the high-dimensional data set X = {x1,x2,...,xn} into two or three-dimensional data Y = {y1,y2,...,yn} that can be displayed in a scatterplot. In the paper, we refer to the low-dimensional data representation Y as a map, and to the low-dimensional representations yi of individual datapoints as map points. The aim of dimensionality reduction is to preserve as much of the significant structure of the high-dimensional data as possible in the low-dimensional map. This can easily be applied to the cell interaction matrix taught by Kumar et al. (pg. 2580 paragraph 2, reconstructing a one/two/three-dimensional spatial structure of cell interactions according to the acquired probability matrix P of the cell-cell interaction intensity matrix A (Claim 1)) With respect to the limitations of claim 2, Maaten et al. also teaches t-SNE which minimizes the cost function
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(pg. 2583, paragraph 5) which is equivalent to the formula in the instant application. Maaten et al. also employ a Student t-distribution with one degree of freedom (which is the sameas a Cauchy distribution) as the heavy-tailed distribution in the low-dimensional map. Using this distribution, the joint probabilities qij are defined as
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(pg. 2585 paragraph 4, equation 4) which is equivalent to how qij is defined but dij2 and Zq are calculated in the function. The difference being Euclidian distance being 3d in the instant application while being general in the prior art, which is a known and obvious adjustment. Maaten et al. also farther define terms
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(pg 2601, paragraph 1) (wherein a model for reconstructing a three- dimensional spatial structure of cell interactions is as follows: minimizing an objective function
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wherein, pij is an interaction intensity between cell i and cell j in the probability matrix P of the cell-cell interaction intensity matrix A; qij is a probability of cell j being within the intracellular distance cell i; dij is a Euclidean distance between cell i and cell j in a three-dimensional space; yi m is a coordinate of cell i on axis m; yj m is a coordinate of cell j on axis m; (Claim 2)) With respect to the limitations of claim 3, Maaten et al. also teaches minimization of a cost function using gradient descent
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(pg. 2586, Equation 5, paragraph 2, wherein the objective function
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is minimized, cell coordinates are updated using gradient descent, and a gradient direction is calculated for each cell at the present coordinates:
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wherein, C represents the objective function, yi is a present coordinate of cell i on one axis, and yj is a present coordinate of cell j on the same axis; with the gradient direction as a coordinate updating direction, the cell coordinates are updated with a fixed step size, and a plurality of iterations are performed (Claim 3)) With respect to the limitations of claim 4, Maaten et al. also teaches the crowding problem which is when the volume of a sphere centered on datapoint i scales as rm, where r is the radius and m the dimensionality of the sphere. So if the datapoints are approximately uniformly distributed in the region around i on the ten-dimensional manifold, and we try to model the distances from i to the other datapoints in the two-dimensional map, we get the following “crowding problem”: the area of the two-dimensional map that is available to accommodate moderately distant datapoints will not be nearly large enough compared with the area available to accommodate nearby datapoints. Hence, if we want to model the small distances accurately in the map, most of the points that are at a moderate distance from datapoint i will have to be placed much too far away in the two-dimensional map (pg. 2584 final paragraph – pg. 2585 first paragraph) and the first trick, which we call “early compression”, is to force the map points to stay close together at the start of the optimization. When the distances between map points are small, it is easy for clusters to move through one another so it is much easier to explore the space of possible global organizations of the data. Early compression is implemented by adding an additional L2-penalty to the cost function that is proportional to the sum of squared distances of the map points from the origin (pg. 2584 final paragraph – pg. 2585 first paragraph) this is a well known technique of clamping or bounding gradient values to prevent numerical instability (wherein when a distance between cell i and cell j is smaller than a minimum distance r between two cells in the three-dimensional space, when pij−qij>0, let pij−qij=s, wherein s is a negative number not smaller than -1 (Claim 4)).
With respect to the limitations of claim 1, Ramilowski et al. teaches the results of a search for the CSF1–CSF1R ligand–receptor pair, filtered for the top cell-to-cell paths (ranked by the product of CSF1 and CSF1R expression). In this network, stimulated mast cells express the highest levels of CSF1. This teaches the product of cell-cell interactions (pg. 6, Figure 4 caption). Ramilowski et al. also teaches interaction matrix (pg. 4, figure 2) (every element in the cell-cell interaction intensity matrix A is divided by Zp, a sum of all elements in the cell-cell interaction intensity matrix A, to obtain the probability matrix P of the cell-cell interaction intensity matrix A,
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I is a total number of cells; K is a total number of ligand-receptor pairs; wL k ,R k represents a chemical binding constant of ligand-receptor pair k; ei L k is an expression level of ligand k in cell i; ei R k is an expression level of receptor k in cell i; ej L k is an expression level of ligand k in cell j; ej R k is an expression level of receptor k in cell j. (Claim 1))
With respect to the limitations of claim 6, A person having ordinary skill in the art would know to normalize the probability matrix by dividing by Zp
(wherein the elements in the probability matrix P of the cell-cell interaction intensity matrix A are:
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(Claim 6))
With respect to the limitations of claim 7, Ramilowski et al. also teaches the results of a search for the CSF1–CSF1R ligand–receptor pair, filtered for the top cell-to-cell paths (ranked by the product of CSF1 and CSF1R expression). In this network, stimulated mast cells express the highest levels of CSF1. This teaches the product of cell-cell interactions (pg. 6, Figure 4 caption). Ramilowski et al. also teaches interaction matrix (pg. 4, figure 2, wherein each element in the cell-cell interaction intensity matrix A is an interaction intensity between corresponding cell C1 and cell C2; a relation for the interaction intensity is
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or
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wherein, AC1,C2 represents the cell-cell interaction intensity between cell C1 and cell C2; wA,B represents a weight for an interaction between ligand A and receptor B; AC1 and AC2 represent expression levels of ligand A in cell C1 and cell C2, respectively; BC1 and BC2 represent expression levels of receptor B in cell C1 and cell C2, respectively; K represents a total number of ligand-receptor pairs (Claim 7))
It would be obvious to a person of ordinary skill in the art to use the method of Kumar et al. with the t-SNE method taught by Maaten et al. because Kumar et al. uses the t-SNE (Figure 2). Maaten et al. is included to explain the mathematics behind the t-SNE method used. A person of ordinary skill in the art would also be motivated to combine it with Ramilowski et al. because all methods deal with sc-RNA seq data. Additionally, Ramilowski et al. directly deals with reconstructing cell to cell interactions so a person of ordinary skill would look at how others have done it in the past. A person of ordinary skill in the art would understand how to adapt the mathematical concepts and methods taught by Ramilowski et al. There is a reasonable expectation of success because Kumar et al. has used t-SNE to visualize sc-RNA data. There is no change in the method used therefore a person of ordinary skill in the art would have a reasonable expectation of success. Maaten et al. just gives a deeper explanation of the math and how to use it in 2d or 3d. Ramilowski et al. just adds additional well known mathematics which can easily be combined. There is a reasonable expectation of success because all methods work separately and combining them does not change the function of any method just the operation being performed.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. in view of Maaten et al. in view of Ramilowski et al. as applied to claims 1-4, 6-7, 10-11 above, and further in view of Rostom et al. (Rostom et al. Computational Approaches for Interpreting ScRNA-Seq Data. FEBS Letters 2017, 591 (15), 2213–2225.) The limitations of claims 1-4, 6-7, 10-11 have been taught by Kumar et al. in view of Maaten et al. in view of Ramilowski et al. above.
Kumar et al. in view of Maaten et al. in view of Ramilowski et al. do not explicitly teach
wherein the intercellular distance threshold where each cell interacts on average with h cells is determined using the following method: for each cell, the distance to the cell closest to it in the hth order is calculated, and the median distance value for all cells is calculated and set as the intercellular distance threshold (Claim 8)
With respect to the limitations of claim 8, Rostom et al. suggests using k-nn to analyze RNA-seq data. It would be obvious to modify this with a median value. (pg. 2219, Table 2, wherein the intercellular distance threshold where each cell interacts on average with h cells is determined using the following method: for each cell, the distance to the cell closest to it in the hth order is calculated, and the median distance value for all cells is calculated and set as the intercellular distance threshold (Claim 8))
It would be obvious for a person of ordinary skill in the art to combine the method for predicting cell spatial relation based on single-cell transcriptome sequencing data taught by Kumar et al. in view of Maaten et al. in view of Ramilowski et al. with Rostom et al. because it suggests methods to deal with sc-RNA seq data. Therefore, a person of ordinary skill in the art would know to add the method Rostom et al. There is a reasonable expectation of success because all methods work separately and combining them does not change the function of any method just the operation being performed.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. in view of Maaten et al. in view of Ramilowski et al. as applied to claims 1-4, 6-7, 10-11 above, and further in view of Gallo et al. (Cristian A. Gallo, Discretization of gene expression data revised, Briefings in Bioinformatics, Volume 17, Issue 5, September 2016, Pages 758–770) The limitations of claims 1-4, 6-7, 10-11 have been taught by Kumar et al. in view of Maaten et al. in view of Ramilowski et al. above.
Kumar et al. in view of Maaten et al. in view of Ramilowski et al. do not explicitly teach
With respect to the limitations of claim 9, Gallo et al. teaches data discretization is a technique used in computer science and statistics, frequently applied as a preprocessing step in the analysis of biological data (pg. 758, col. 1, paragraph 2, wherein the probability matrix P of the cell-cell interaction intensity matrix A obtained is discretized before reconstructing the three-dimensional spatial structure of cell interactions (Claim 9))
It would be obvious for a person of ordinary skill in the art to combine the method for predicting cell spatial relation based on single-cell transcriptome sequencing data taught by Kumar et al. in view of Maaten et al. in view of Ramilowski et al. with Gallo et al. because it suggests using their method of data discretization on RNA-seq data (pg. 758, col. 1, paragraph 2). There is a reasonable expectation of success because all methods work separately and combining them does not change the function of any method just the operation being performed.
Response to Arguments 103
Applicant states “Kumar only teaches preliminary analysis of intercellular interactions, and is not relate to the reconstruction of cellular spatial structures” Examiner asserts that when using t-SNE it does reconstruct a spatial relationship. The points on a t-SNE plot describe a spatial relationship. Applicant states "The t-SNE algorithm of Maaten is a general dimensionality reduction algorithm,
which is different from the three-dimensional spatial reconstruction method of the
claimed invention; it cannot be combined with Kumar and, even if combined,
cannot arrive at the claimed invention” Kumar et al. uses the t-SNE (Figure 2) showing it is easily combined. Maaten et al. teaches dimensionality reduction methods convert the high-dimensional data set X = {x1,x2,...,xn} into two or three-dimensional data (pg. 2580 paragraph 2) Maaten et al. teaches dimensionality reduction methods convert the high-dimensional data set X = {x1,x2,...,xn} into two or three-dimensional data. (pg. 2580 paragraph 2). This is a 3-dimensional spatial reconstruction. Examiner also rejected claim 5 in the previous office action but applicant did not respond.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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