DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner’s Note
Claim 15 is patent eligible applicant excludes “signal” from the definition of computer readable media. ( See spec [0042] FIG. 1 is a block diagram showing some of the components typically incorporated in at least some of the computer systems and other devices on which the facility operates. In various embodiments, these computer systems and other devices 100 can include server computer systems, cloud computing platforms or virtual machines in other configurations, …… ASIC; a computer memory 102—such as RAM, SDRAM, ROM, PROM, etc.—for storing programs and data while they are being used, including the facility and associated data, an operating system including a kernel, and device drivers; a persistent storage device 103, such as a hard drive or flash drive for persistently storing programs and data; a computer-readable media drive 104, such as a floppy, CD-ROM, or DVD drive, for reading programs and data stored on a computer-readable medium; and a network connection 105 for connecting the computer system to other computer systems to send and/or receive data, such as via the Internet …. None of the components shown in FIG. 1 and discussed above constitutes a data signal per se”)
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 5-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by MOON et al. , "Visualizing structure and transitions in high- dimensional biological data", NATURE BIOTECHNOLOGY, NATURE PUBLISHING GROUP US, NEW YORK, vol. 37, no. 12, 1 December 2019 (2019-12-01), pages 1482-1492, “Moon”).
Regarding claim 1, Moon teaches, A method in a computing system performed with respect to a sample of cells, (see abstract disclosing visualization method) the method comprising:
accessing analysis results indicating a detected expression level for each of a number of cellular constituents of each cell of the sample; (see fig 2, table 1, page 1487 left col, page 1488 left col, Methods section, disclosing accessing of a variety of data types)
accessing a cell type for each cell of the sample, where the cell type is among a plurality of cell types that is attributed to the cell based on each cell's cellular constituent expression levels; (see page 1488 left col "data exploration with PHATE')
for each of a plurality of pairs of cell types among the plurality of cell types, accessing a level of similarity between the cell types of each pair; (see "methods"//local affinities and the diffusion operator section disclosing transformation of global distances to local similarities using a Gaussian kernel quantifying the similarity between the two points (pairs of cell types) based on their Euclidean distance )
for each of the plurality of cell types, establishing a first representation of each cell type based on the accessed levels of similarity between each cell type and each of the other cell types of the plurality; { see "methods"//local affinities and the diffusion operator section disclosing that embedding local affinities directly results in a loss of global structure thus the proposed method constructs a diffusion geometry based on local similarities between data points (=first representation based on level of similarity))
accessing an emphasis weight specifying a degree to which cell type is to be emphasized relative to cell constituent expression levels in determining visualization coordinates for each cell of the sample; (“see "methods"// the á-decaying kernel and adaptive bandwidth section disclosing parameters for tuning the results (choice of kernel K (equation 3) and bandwidth E in the Propagating affinities via diffusion section disclosing raising the diffusion operator to its /-th power.”)
generating a cell matrix comprising a grid of values in which each row represents one of the cells of the sample, in which a first group of the columns correspond to constituent expression levels detected for the cell, and a second group of the columns correspond to the first representation established for the cell's cell type, the values in the first group of the columns being weighted against the values in the second group of the columns in accordance with the accessed emphasis weight; (see "methods"// the á-decaying kernel and adaptive bandwidth section. The emphasis weight in the context of the claim corresponds to the bandwidth chosen in D1: "if the bandwidth is too small, then single-step transitions in the random walk using P₂ are largely confined to the nearest neighbors of each data point. In biological data, trajectories between major cell types may be relatively sparsely sampled. Thus if the bandwidth is too small, then the neighbors of points in the sparsely sampled regions may be excluded entirely and the trajectory structure in the probability matrix Pₑ, will not be encoded. Conversely if the bandwidth is too large, then the resulting probability matrix P₂ loses local information [...] which may result in an inability to resolve different trajectories.". The cell matrix is the P matrix. ) and
performing dimensionality reduction on the rows of the generated cell matrix to
obtain visualization coordinates for each cell of the sample. (see "methods"// embedding the potential distances in low dimensions section disclosing step (4) "capturing the data in low dimensions using MDS for visualization", thereby disclosing performance of a dimensionality reduction to obtain visualization coordinates for the low-dimensional plot.
Regarding claim 2, Moon teaches, constructing a visualization image containing, for each cell of the sample, a visual indication of the cell that appears in a spatial location specified by the visualization coordinates obtained for the cell. (see "methods: embedding the potential distances in low dimensions section disclosing step (4) "capturing the data in low dimensions using MDS for visualization", thereby disclosing performance of a dimensionality reduction to obtain visualization coordinates for the low-dimensional plot.)
Regarding claim 3, Moon teaches, causing the constructed visualization image to be presented. see "methods: embedding the potential distances in low dimensions section disclosing step (4) "capturing the data in low dimensions using MDS for visualization", thereby disclosing the constructed visualization image to be presented)
Regarding claim 5, Moon teaches, invoking an automatic analysis against the constructed visualization image. (See Page 1488, method: PHATE analysis of human ES cell differentiation data.)
Regarding claim 6, Moon teaches, wherein, in the constructed visualization image, each visual indication of a cell is shown in a color corresponding to the cell's cell type. ( See Fig. 1)
Regarding claim 7, Moon teaches, wherein, in the constructed visualization image, each visual indication of a cell is shown in a color corresponding to the expression level indicated for a distinguished cellular constituent in the cell. ( See Fig.1 Phate 1 display for a distinguished cellular constituent in the cell)
Regarding claim 8, Moon teaches, receiving input specifying the distinguished cellular constituent. ("Methods" invoking "EMD score analysis").
Regarding claim 9, Moon teaches, for each of the plurality of cell types, establishing a first representation of the cell type comprises:
for each of the plurality of pairs of cell types, accessing a distance in an adjacency graph among the plurality of cell types reflecting a hierarchy established for the plurality of cell types; ("Methods"// Distance preservation and Local affinities and the diffusion operator sections.)
for each of the cell types of the plurality of cell types, constructing a second representation of the cell type by concatenating values that are based on the distances accessed for the cell type with respect to all of the cell types of the plurality of cell types; ("Methods"// Distance preservation and Local affinities and the diffusion operator sections.)
performing embeddings into an embedding space of the constructed second representations of the cell types of the plurality of cell types to obtain the first representations of the cell types of the plurality of cell types.("Methods"// Distance preservation and Local affinities and the diffusion operator sections.)
Regarding claim 10, Moon teaches, wherein embedding is performed using a process selected from among:
t-distributed Stochastic Neighbor Embedding (t-SNE); Multi-dimensional scaling (MDS); Force-Directed Placement; Kamada algorithm for drawing general undirected graphs; and Kobourov Spring Embedders and Force-Directed Graph Drawing Algorithms. (Moon Page 1483 discloses MDS for the embeddings in low-dimensional space, “ 3. Embed potential distance information into low dimensions for visualization (Fig. 2e–f). The information in the potential dis tances are then squeezed into low dimensions for visualization via metric MDS,”)..
Regarding claim 11, Moon teaches, receiving input specifying the hierarchy established for the plurality of cell types; constructing the adjacency graph in accordance with the hierarchy, in which each of the plurality of cell types is represented by a node, and nodes are connected directly or indirectly by edges; ( Page 1483 left column, “For more details on the scalability of PHATE see the Methods, Supplementary Table 2 and Supplementary Fig. 6, which shows the fast runtime of PHATE on datasets of different sizes, including a dataset of 1.3 mil lion cells (2.5 h) and a network of 1.8 million nodes (12 min)” and
for each of the plurality of pairs of cell types, determining the accessed distance by counting the minimum number of edges between the pair of nodes representing the pair of cell types. (figures 3, 6 and "Methods"// the alpha-decaying kernel and adaptive bandwidth section, in particular the passages pertaining to adaptive bandwidth.”)
Regarding claim 12, Moon teaches, for each of the cell types, determining a frequency of the cell type within the sample; and
determining the values that are based on the distances accessed for the cell type with respect to all of the cell types of the plurality of cell types by weighting the accessed distances in accordance with the determined frequencies. (“figures 3, 6 and "Methods": the alpha-decaying kernel and adaptive bandwidth section, in particular the passages pertaining to adaptive bandwidth.)
Regarding claim 13, Moon teaches, receiving input specifying the accessed emphasis weight. (figures 3, 6 and "Methods"// the alpha-decaying kernel and adaptive bandwidth section, in particular the passages pertaining to adaptive bandwidth.)
Regarding claim 14, Moon teaches, wherein the dimensionality reduction is performed using a process selected from among: t-distributed Stochastic Neighbor Embedding (t-SNE); and Uniform Manifold Approximation and Projection (UMAP). (Page 1487: “Table 3. We found that PHATE had the highest DEMaP score in 22 of 24 comparisons and was the top-performing method overall. Uniform manifold approximation and projection (UMAP) was the second best performing method overall but had the highest DEMaP score in only two of the comparisons, one of which is equal with PHATE. We ran further tests on cluster data using the adjusted Rand index26 and found that on average PHATE preserves local cluster structure as well as, or better than, t-SNE, UMAP and PCA (Supplementary Fig. 9)”)
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.
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Moon in view of Moriya et al, ( US patent publication: 20100303330, “Moriya”)
Regarding claim 4, Moon doesn’t expressly teach, causing the constructed visualization image to be persistently stored.
Moriya teaches, a constructed visualization image to be persistently stored ( [0049] The system includes a radiographic image display apparatus 10 according to the presently disclosed subject matter, a photographing apparatus 40 installed at a medical facility, and the like, a console 42 which is used to perform an operation, and the like, of the photographing apparatus 40, an image database (image DB) 44 which stores a medical radiographic image, and the like, photographed by using the photographing apparatus 40, and a pathology database (pathology DB) 46 which manages information on a pathological examination, obtained as a result of the pathological examination of a tissue, cells, and the like, collected from a position of the pathological examination.”)
Moon and Moriya are analogous as they are from the field of medical image processing
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of the claimed invention to have modified Moon to have the constructed visualization image to be persistently stored as taught by Moriya.
The motivation to include the modification is to retrieve previously generated image when necessary.
Claim(s) 15-20 is rejected under 35 U.S.C. 103 as being unpatentable over Moon in view of Goldberg et al, ( US patent publication: 20240170096, “Goldberg”)
Claim 15 recites one or more instances of computer-readable media collectively having contents configured to cause a computing system to perform a method, the method of claim 1 which is taught by Moon as shown above.
Goldberg teaches computer implementation of methods of dimensionality reduction of cells. ([0425, “To perform any of the functionality described herein, the processor 1510 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 1520), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 1510.”).
Goldberg and Moon are from the field of dimensionality reduction of cells.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of the claimed invention to have modified to have included one or more instances of computer-readable media collectively having contents configured to cause a computing system to perform the method of claim 1 (Moon’s teaching) as taught by Goldberg’s computer implementation of reduction of dimensions of cells.
The motivation for the modification is to achieve a different implementation of Moon’s method.
Claim 16 recites One or more memories collectively storing a cell matrix data structure, the data structure comprising a subset of data structure elements of method claim 1 where the method is taught by Moon as shown in the rejection of claim 1.
Goldberg teaches computer implementation of methods of dimensionality reduction of cells where the method steps are program instruction stored in a memory . ([0425, “To perform any of the functionality described herein, the processor 1510 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 1520), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 1510.”).
Goldberg and Moon are from the field of dimensionality reduction of cells.
Therefore it would have been obvious for an ordinary skilled person in the art before the effective filing date of the claimed invention to have modified to have the method elements in a data structure as taught by Goldberg’s computer implementation where method elements are captured in memory to execute the corresponding methods..
The motivation for the modification is to achieve a different implementation of Moon’s method.
Regarding claim 17, Moon as modified by Goldberg teaches, wherein each first entry of the plurality of first entries further comprises: coordinates determined for the visual indication of the cell to which the first entry corresponds based on the first and second groups of values of the first entry. (Moon, “see "methods"// the á-decaying kernel and adaptive bandwidth section disclosing parameters for tuning the results (choice of kernel K (equation 3) and bandwidth E in the Propagating affinities via diffusion section disclosing raising the diffusion operator to its /-th power.”)
Regarding claim 18, Moon as modified by Goldberg teaches, the data structure further comprising: data representing a visualization image for the cell sample, comprising, for each of the plurality of cells of the cell sample, a visual indication of the cell placed at a spatial location determined based on the contents of the first entry that corresponds to the cell, the visual indication being colored in accordance with the cell type determined for the cell. (. Moon, see "methods: embedding the potential distances in low dimensions section disclosing step (4) "capturing the data in low dimensions using MDS for visualization", thereby disclosing performance of a dimensionality reduction to obtain visualization coordinates for the low-dimensional plot.)
Regarding claim 19, Moon as modified by Goldberg teaches, data representing a visualization image for the cell sample, comprising, for each of the plurality of cells of the cell sample, a visual indication of the cell placed at a spatial location determined based on the contents of the first entry that corresponds to the cell, the visual indication being colored in accordance with an expression level detected in the cell of a distinguished cellular constituent. ( Moon, See Fig.1 Phate 1 display for a distinguished cellular constituent in the cell)
Regarding claim 20, Moon as modified by Goldberg teaches, the data structure further comprising:
a plurality of second entries, each second entry corresponding to one of a plurality of cell types determined for the cells of the plurality of cells of the sample, each second entry comprising: the representation of the cell type assigned such that the distance between a pair of cell types is representative of a level of dissimilarity between the cell types of the pair (Moon,. see "methods"// the á-decaying kernel and adaptive bandwidth section. The emphasis weight in the context of the claim corresponds to the bandwidth chosen in D1: "if the bandwidth is too small, then single-step transitions in the random walk using P₂ are largely confined to the nearest neighbors of each data point. In biological data, trajectories between major cell types may be relatively sparsely sampled. Thus if the bandwidth is too small, then the neighbors of points in the sparsely sampled regions may be excluded entirely and the trajectory structure in the probability matrix Pₑ, will not be encoded. Conversely if the bandwidth is too large, then the resulting probability matrix P₂ loses local information [...] which may result in an inability to resolve different trajectories.". The cell matrix is the P matrix. )
Conclusion
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/TAPAS MAZUMDER/ Primary Examiner, Art Unit 2615
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