DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 01/18/2024 for application number 18/415,906.
Claims 1-20 are presented for examination. Claims 1, 8, and 15 are independent claims.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Certified copy has been received for the foreign priority Application No. JP2023-012524 filed on 01/31/2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 1/18/2024, 9/12/2024, 6/17/2026 have been considered by the Examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7 are directed to a computer-readable recording medium, Claims 14-16 are directed to a method, and Claims 17-20 are directed to an apparatus and fall within one of the statutory categories (articles of manufacture, process, machine) and are eligible under Step 1.
Step 2A Prong 1
Independent Claims
Claims 1, 8, 15 recite:
extracting a plurality of representative points from a presence probability distribution of states of a target; identifying a first plurality of state transition paths between the plurality of representative points; and selecting a second plurality of state transition paths from the first plurality of state transition paths, based on a probability density of each path included in the first plurality of state transition paths - these limitations encompass mathematical concepts and mental processes. Identifying particular points on a probability distribution, defining paths between marked points, evaluating probability density associated with each path and choosing paths according to that evaluation are mathematical calculations as well as acts of observation, evaluation, and judgement that can be practically performed in a human mind or with pen and paper.
Accordingly, the claims recites an abstract idea that falls under “mathematical concepts” and “mental process” grouping.
Step 2A Prong 2
Independent Claims
Additional elements
Claim 1:
A non-transitory computer-readable recording medium storing therein a path selecting program that causes a computer to execute a process - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 8:
A path selecting method executed by a processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 15:
A path selecting apparatus comprising: a processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B
Independent Claims
Additional elements
Claim 1:
A non-transitory computer-readable recording medium storing therein a path selecting program that causes a computer to execute a process - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 8:
A path selecting method executed by a processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 15:
A path selecting apparatus comprising: a processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claim is patent ineligible.
Step 2A Prong 1
Dependent Claims
Claims 2, 9, and 16:
the extracting includes extracting, as the representative points, points of local maxima in a mixture Gaussian distribution corresponding to the presence probability distribution of the states of the target - these limitations encompass mathematical calculations and mental processes to locate local maxima.
Claims 3, 10, and 17:
the identifying includes identifying, as the first plurality of state transition paths, a fully connected graph including nodes each corresponding to the plurality of representative points, and an edge connecting each pair of the nodes - these limitations encompass mathematical relationships and mental processes to define a complete graph whose nodes are the representative points and whose edges join every pair of nodes.
Claims 4, 11, and 18:
the identifying includes identifying, as the first plurality of state transition paths, a graph corresponding to a minimum spanning tree, from a fully connected graph including nodes each corresponding to the plurality of representative points, and an edge connecting each pair of the nodes - these limitations encompass mathematical calculations and mental processes to compute minimum spanning tree from a complete graph.
Claims 5, 12, and 19:
the selecting includes selecting the second plurality of state transition paths based on a difference between maximum and minimum probability densities on each one of the first plurality of state transition paths - these limitations encompass mathematical calculations and mental processes to select paths according to the difference between the maximum and minimum probability densities.
Claims 6, 13, and 20:
the selecting includes selecting the second plurality of state transition paths based on a probability density of a first state, among states included in each path of the first plurality of state transition paths - these limitations encompass mathematical calculations and mental processes to select paths according to the probability density of a particular state on each path.
Claims 7 and 14:
the selecting includes selecting the second plurality of state transition paths based on a difference between a probability density of a first state and a probability density of a second state, among states included in each path of the first plurality of state transition paths - these limitations encompass mathematical calculations and mental processes to select paths according to the difference between the probability densities of two states on each path.
Thus, the claims recite the abstract idea.
Step 2A Prong 2
Dependent Claims
Additional elements
Claims 2-7:
The non-transitory computer-readable recording medium - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 9-14:
the processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B
Dependent Claims
Additional elements
Claims 2-7:
The non-transitory computer-readable recording medium - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 9-14:
the processor - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are patent ineligible.
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.
Claims 1-4, 8-11, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (US 2013/0060775 A1 hereinafter Qiu) in view of Chen et al. (“DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data” hereinafter Chen).
Regarding Claim 1, Qiu teaches a non-transitory computer-readable recording medium storing therein a path selecting program that causes a computer to execute a process ([0034] the functional aspects of the invention that are implemented on a computer, as will be understood from the teachings herein, may be implemented or accomplished using any appropriate implementation environment or programming language; [0134] the programs resident on a storage medium; [0081] method for discovering progression underlying multidimensional data such as flow cytometry data) comprising:
extracting a plurality of representative points from a presence probability distribution of states of a target ([0084] models a dataset as a point cloud in a high-dimensional space; each point in the cloud is one cell or one sample; parts of the point cloud that correspond to abundant cell or sample types have high density; performs density-dependent downsampling to remove the density variation in order to better identify rare cell or sample types; [0085] in order to perform density-dependent downsampling, the invention first estimates the local high dimensional density for each cell; the local density of a cell i (LDi) is defined as the number of cells within a user-defined n-dimensional neighborhood of this cell; the user or investigator chosen parameters target density (TD) and outlier density (OD), the invention downsamples each cell i with the following probability - thus, the cells form a multidimensional point cloud whose density over cell states is a presence probability distribution of the states of a target; [0088] after downsampling, agglomerative clustering process, each cell in the point cloud is treated as its own cluster; [0093]the minimum spanning tree (MST) is an undirected acyclic graph that connects all the cell clusters with minimum number of edges (lines connecting the different nodes representing clusters or center points of the clusters) and minimum total edge length; [0094] based on measurements of a set of features, a fully connected undirected graph is constructed, where each node represents one cell cluster; the length of the edge that connects nodes i and j is defined as the Euclidean distance between the average measurements of clusters i and j - thus, the density-downsampled cells are clustered and each cluster is represented by its average measurements (i.e., representative point));
identifying a first plurality of state transition paths between the plurality of representative points ([0094] based on measurements of a set of features, a fully connected undirected graph is constructed, where each node represents one cell cluster; the length of the edge that connects nodes i and j is defined as the Euclidean distance between the average measurements of clusters i and j; [0092] after the samples are grouped into clusters, a graph that connects the clusters is constructed to describe a hierarchy among the clusters, depicting the progression and differentiation underlying the data; this graph, or “progression tree”, is represented by the minimum spanning tree (MST) constructed based on a relevant set of features -thus, each edge of the fully connected graph links two cluster representatives and is a candidate transition in the progression; [0020] the invention provides a computational algorithm to discover the progression or hierarchy among thousands of samples (e.g., cells), based on multidimensional data - thus, identifying plurality of first transition paths); and
selecting a second plurality of state transition paths from the first plurality of state transition paths ([0094] then, the MST can be derived from the fully connected graph; [0093] the minimum spanning tree (MST) is an undirected acyclic graph that connects all the cell clusters with minimum number of edges (lines connecting the different nodes representing clusters or center points of the clusters) and minimum total edge length - thus, MST retains only a subset of the edges of the fully connected graph/ second plurality of paths).
However, Qiu does not expressly teach wherein selecting second plurality of paths based on a probability density of each path included in the first plurality of state transition paths.
In the same field of endeavor, Chen teaches wherein selecting second plurality of paths based on a probability density of each path included in the first plurality of state transition paths (pages 7 to 8, A4 - Construct the cell state-transition path; DensityPath uses the peak points of RCSs as the landmarks, calculates the shortest path (geodesics) of the peak points on the surface of the density landscape; given the coordinates of two points (RCSs) on the density surface, their shortest path (geodesics) is calculated by the Dijkstra’s algorithm on the king’s graph; the embedding cell state-transition path (cell developmental trajectory) is then constructed by finding the MST of the peak points of RCSs - thus, the shortest distance path/ geodesics computed between the RCS peak points (i.e., first plurality of paths) and a subset of those geodesics retained in the minimum spanning tree/ MST (second plurality of paths); page 7, A2 - DensityPath estimates the density function (landscape) of the reduced-dimension (low-dimensional) space of single cell expression profile using the standard nonparametric kernel density estimator (KDE); page 7, A4 - the density surface is represented by the z coordinates (density values) of points above a 2-d square grid; the distance between each pair of the points is set as (i) the reciprocal of the mean of the two points’ density values if they are connected on the king’s graph or (ii) +∞ otherwise; page 4 - DensityPath reconstructs the cell state-transition path by finding the MST of the peak points based on their calculated geodesic distances; the energy function can be defined as the negative logarithm of the density; the cell state-transition path based on the geodesic distance constructed by DensityPath exhibit physical interpretation as the shortest path, which is the minimum-transition-energy (least-cost) path. Thus, the density surface is the kernel density estimate of the cell states, which is probability density over cell states; the length of each candidate path is determined by the probability density of the states along that path and the MST selects paths based on the probability density of paths).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein selecting second plurality of paths based on a probability density of each path included in the first plurality of state transition paths, as suggested in Chen into Qiu. Doing so would be desirable because it would allow for accurately and efficiently visualize and reconstruct underlying cell developmental trajectories for large-scale data (Chen, page 2, Introduction).
As to dependent Claim 2, Qiu and Chen teach all the limitations of claim 1. Chen further teaches wherein the extracting includes extracting, as the representative points, points of local maxima in a mixture Gaussian distribution corresponding to the presence probability distribution of the states of the target (page 3 - DensityPath analyzes the heterogeneous multimodal behavior of the density landscape to identify the representative cell states (RCSs) and extract high density clusters; page 7, A2 - DensityPath estimates the density function (landscape) of the reduced-dimension (low-dimensional) space of single cell expression profile using the standard nonparametric kernel density estimator (KDE); the kernel function K is chosen as the Gaussian kernel; page 7, A4 - DensityPath uses the peak points of RCSs as the landmarks, calculates the shortest path (geodesics) of the peak points on the surface of the density landscape - thus, selecting the points of local maxima/ peak points on the density landscape/ Gaussian distribution of cell states on the density landscape).
As to dependent Claim 3, Qiu and Chen teach all the limitations of claim 1. Qiu further teaches wherein the identifying includes identifying, as the first plurality of state transition paths, a fully connected graph including nodes each corresponding to the plurality of representative points, and an edge connecting each pair of the nodes ([0094] based on measurements of a set of features, a fully connected undirected graph is constructed, where each node represents one cell cluster (i.e., fully connected graph with nodes corresponding to representative points and edge connecting the nodes); the length of the edge that connects nodes i and j is defined as the Euclidean distance between the average measurements of clusters i and j).
As to dependent Claim 4, Qiu and Chen teach all the limitations of claim 1. Qiu further teaches wherein the identifying includes identifying, as the first plurality of state transition paths, a graph corresponding to a minimum spanning tree, from a fully connected graph including nodes each corresponding to the plurality of representative points, and an edge connecting each pair of the nodes ([0094] based on measurements of a set of features, a fully connected undirected graph is constructed, where each node represents one cell cluster; then, the MST can be derived from the fully connected graph; [0093] the minimum spanning tree (MST) is an undirected acyclic graph that connects all the cell clusters with minimum number of edges (lines connecting the different nodes representing clusters or center points of the clusters) and minimum total edge length).
Claims 8-11 are method claims corresponding to the medium claims 1-4 respectively and therefore, rejected for the same reasons.
Claims 15-18 are apparatus claims corresponding to the medium claims 1-4 respectively and therefore, rejected for the same reasons. Qiu further teaches a path selecting apparatus comprising: a processor ([0127] an information appliance or digital device 700 that may be understood as a logical apparatus that can perform method steps as described herein; the logical apparatus containing CPU).
Claims 5-7, 12-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu in view of Chen, further in view of Bradski (US 2006/0136831 A1).
As to dependent Claim 5, Qiu and Chen teach all the limitations of claim 1. However, Qiu and Chen do not expressly teach wherein the selecting includes selecting the second plurality of state transition paths based on a difference between maximum and minimum probability densities on each one of the first plurality of state transition paths.
In the same field of endeavor, Bradski teaches wherein the selecting includes selecting the second plurality of state transition paths based on a difference between maximum and minimum probability densities on each one of the first plurality of state transition paths ([0013] as these windows move to find local hills, where two or more windows meet, such windows are merged; the merger of the windows can be used to generate a hierarchical tree of data clusters from the largest cluster, such as, the top cluster on down through each local hill and further on down to each local data point, such as, a bottom cluster; [0014] a type of mean shift algorithm, which is a robust (it ignores outliers) statistical method for finding the mode, such as, a top of a hill of a distribution of data; [0016] the windows may be defined as the Epanechnikov kernel, where the weight of data points falls off as the square; [0023] a test may be performed such as using the Epanechnikov kernel to check that between each potential cluster, there exists a predefined falling off, such as, a valley of data density; one may only count clusters when a substantial density valley (e.g., the density of data points falls off by a predetermined value) was found between the proposed cluster and all other clusters of that level; [0024] thus, significant data density “peaks” may be discovered. Thus, the data density between each potential cluster (i.e., density along the path connecting them) is measured with a density kernel/ Epanechnikov; each connection between clusters in the tree is analyzed, the falling off of density from the hill (maximum density point) to the valley (minimum density point) between two clusters is a difference between the maximum and minimum densities along the connection, and connections are accepted or rejected according to whether that difference meets a predetermined value).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the selecting includes selecting the second plurality of state transition paths based on a difference between maximum and minimum probability densities on each one of the first plurality of state transition paths , as suggested in Bradski into Qiu and Chen. Doing so would be desirable because it would provide a more efficient data clustering technique (Bradski [0004]-[0005]).
As to dependent Claim 6, Qiu and Chen teach all the limitations of claim 1. However, Qiu and Chen do not expressly teach wherein the selecting includes selecting the second plurality of state transition paths based on a probability density of a first state, among states included in each path of the first plurality of state transition paths.
In the same field of endeavor, Bradski teaches wherein the selecting includes selecting the second plurality of state transition paths based on a probability density of a first state, among states included in each path of the first plurality of state transition paths ([0013] as these windows move to find local hills, where two or more windows meet, such windows are merged; the merger of the windows can be used to generate a hierarchical tree of data clusters from the largest cluster, such as, the top cluster on down through each local hill and further on down to each local data point, such as, a bottom cluster; [0014] a type of mean shift algorithm, which is a robust (it ignores outliers) statistical method for finding the mode, such as, a top of a hill of a distribution of data; [0023] a test may be performed such as using the Epanechnikov kernel to check that between each potential cluster, there exists a predefined falling off, such as, a valley of data density; one may only count clusters when a substantial density valley (e.g., the density of data points falls off by a predetermined value) was found between the proposed cluster and all other clusters of that level (i.e., the test referenced to the density at the proposed cluster's hill, a first state at the end of the connection)).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the selecting includes selecting the second plurality of state transition paths based on a probability density of a first state, among states included in each path of the first plurality of state transition paths, as suggested in Bradski into Qiu and Chen. Doing so would be desirable because it would provide a more efficient data clustering technique (Bradski [0004]-[0005]).
As to dependent Claim 7, Qiu and Chen teach all the limitations of claim 1. However, Qiu and Chen do not expressly teach wherein the selecting includes selecting the second plurality of state transition paths based on a difference between a probability density of a first state and a probability density of a second state, among states included in each path of the first plurality of state transition paths.
In the same field of endeavor, Bradski teaches wherein the selecting includes selecting the second plurality of state transition paths based on a difference between a probability density of a first state and a probability density of a second state, among states included in each path of the first plurality of state transition paths ([0013] as these windows move to find local hills, where two or more windows meet, such windows are merged; the merger of the windows can be used to generate a hierarchical tree of data clusters from the largest cluster, such as, the top cluster on down through each local hill and further on down to each local data point, such as, a bottom cluster; [0014] a type of mean shift algorithm, which is a robust (it ignores outliers) statistical method for finding the mode, such as, a top of a hill of a distribution of data; [0016] the windows may be defined as the Epanechnikov kernel, where the weight of data points falls off as the square; [0023] a test may be performed such as using the Epanechnikov kernel to check that between each potential cluster, there exists a predefined falling off, such as, a valley of data density; one may only count clusters when a substantial density valley (e.g., the density of data points falls off by a predetermined value) was found between the proposed cluster and all other clusters of that level; [0024] thus, significant data density “peaks” may be discovered. Thus, the data density between each potential cluster (i.e., density along the path connecting them) is measured with a Epanechnikov kernel (probability density); each connection between clusters in the tree is analyzed, the falling off of density from the hill (first state) to the valley (second state) between two clusters is a difference between the maximum and minimum densities along the connection, and connections are accepted or rejected according to whether that difference meets a predetermined value).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the selecting includes selecting the second plurality of state transition paths based on a difference between a probability density of a first state and a probability density of a second state, among states included in each path of the first plurality of state transition paths, as suggested in Bradski into Qiu and Chen. Doing so would be desirable because it would provide a more efficient data clustering technique (Bradski [0004]-[0005]).
Claims 12-14 are method claims corresponding to the medium claims 5-7 respectively and therefore, rejected for the same reasons.
Claims 19-20 are apparatus claims corresponding to the medium claims 5-6 respectively and therefore, rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 CFR § 1.111(c) to consider these references fully when responding to this action.
Peter (US 2006/0047655 A1) teaches: At each grid point xp on the mesh, we will define a density of data p(xp) which is obtained by “weighting” the raw data values to grid points; If it is found that the gent should move up a grid point, the agent moves to xp+1 only if it is moving up in density; after Ns steps, the agents should find themselves at places of density maxima; all clusters can be determined in this fashion and the clusters can be ranked in “strength” (see [0012], [0027], [0033]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REJI KARTHOLY whose telephone number is (571)272-3432. The examiner can normally be reached on Monday - Thursday from 7:30 am to 3:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch, can be reached at telephone number 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REJI KARTHOLY/Primary Examiner, Art Unit 2143
