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 .
Response to Amendment
The amendment filed 01/06/2026 was entered. By the amendment, Applicant canceling claims 2-3, 9, 11, 17, 23-25, and 28 without prejudice or disclaimer. Claims 1, 10, 12-13, 18-22, 26-27, and 29-31 have been amended. New claims 32 and 33 have been added. As a result, claims 1, 4-8, 10, 12-16, 18-22, 26-27, and 29-33 are pending for examination with claim 1 being an independent claim. Support for the amendments can be found at least in FIG. 7, FIG. 9, and pages 2-3 and 18-19 of the application as originally filed. No new matter has been added.
Response to Arguments
Applicant’s arguments with respect to claim(s) the independent claim has been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1, 6, 8, 10, 32 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1) in view of Khater (US Pub. No. 2020/0300763 A1).
With regards to claim 1, Rothenberg discloses a method of investigating a plurality of cells {[0015-0017]}, comprising detecting one or more species of proteins on each of the plurality of cells {[0017]}; obtaining respective spatial coordinates of the detected proteins within the plurality of cells {[0017], [0057]}; detecting boundaries of the plurality of cells {[0017], [0057]}; and constructing a data vector based on the obtained spatial coordinates and the detected boundaries {[0039-0040]}.
Rothenberg fails to expressly disclose “each of” the detected proteins…along with “partitioning the spatial coordinates of each of the detected proteins into a plurality of clusters at a respective plurality of length scales; and determining a set of properties for the plurality of clusters across each of the length scales; wherein, at each length scale, each cluster comprises the spatial coordinates of the detected proteins within an area corresponding to the length scale; wherein the data vector comprises the set of properties determined for the clusters across each of the length scales; and wherein the method further comprises classifying the cells based on a comparison of the data vector with reference data comprising one or more reference feature vectors obtained for reference cells of one or more defined types.
Khater relates to a super-resolution microscopy, the method comprising mapping three-dimensional location of each single emission event (blink) in a plurality of single emission events (blinks) from a series of optical images of a sample [0001], [0013] [0015]. Additionally, Khater teaches removing non-biological networks from the point cloud, wherein optionally non-biological networks are distinguished from biological networks in a point cloud by performing a multi-scale (varying proximity thresholds) network analysis of the point cloud and determining the network degree distribution for each proximity threshold [0012] and further, a cluster characterization determining geometrical, topological, and/or physical properties, such as the shape, size, distribution of the single emission events (blinks), and/or hollowness of the individual cluster [0014].
Khater teaches network analysis [0094] – [0099], segmentation, grouping and matching [0103].
In FIG. 1, Khater expressly teaches that the methods and systems for super-resolution microscopy map single emission events (blinks) from fluorophores in three-dimensions creates a point cloud. The resulting point clouds are then refined or filtered. Clusters are identified in the refined or filtered point cloud and optionally characterized. In some embodiments, characterization of the clusters includes machine-learning based classification of clusters [0009] – [0014], [0048].
In view of the utility, for robust extraction, classifier, reference, localization or the like as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to also include the teachings such as that taught by Khater.
With regards to claim 6, Rothenberg discloses constructing a feature vector by performing a dimension reduction analysis on the constructed data vector, wherein a first dimension of the feature vector is larger than two and smaller than a second dimension of the data vector [0040] [0053].
With regards to claim 8, Rothenberg discloses the step of detecting the one or more species of proteins on each of the plurality of cells and obtaining respective spatial coordinates comprises carrying out single molecule localization microscopy [0015] – [0019], [0039].
With regards to claim 10, Rothenberg discloses raw images are then subjected to computational methods comprising the following steps: (i) detection of molecules using an optimized method that reliably can detect molecular features [0015] – [0019], [0039]. [0040], [0082], [0109], [0074].; (ii) building a physical model of the interactions and determining the model parameters using computer simulations and randomly generated images with the same number and density of the proteins of the real image [0015] – [0019], [0039]. [0040], [0082], [0109], [0074].; (iii) applying univariate and multivariate statistical methods to further reduce these model parameters in dimensionality and to obtain a set of biomarkers that can subsequently be combined into multivariate predictive/classifier models, with model error estimated using cross-validation [0015] – [0019], [0039]. [0040], [0082], [0109], [0074]. Any known statistical methods may be used to generate reference values for use in diagnostics, or to generate a standard library for comparison such that disease progression may be tracked [0015] – [0019], [0039]. [0040], [0082], [0109], [0074].
Khater also teaches learned groups and centroid feature vectors for comparison (Figure 6) [0025], [0094], [0103].
In view of the utility, to analyze overlapping clusters and organization at the cell boundaries of single molecule localization microscopy to define molecular architecture, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to also include the teachings such as that taught by Khater.
With regards to claim 32, Rothenberg discloses the data vector includes a measure of cell-cell interactions [0027] – [0033]; [0050] - [0054].
With regards to claim 33, Rothenberg discloses the set of properties for the clusters includes one or more of: a number of clusters; an average area of clusters; a cluster density; a cluster shape; a number of localizations per cluster [0027] – [0033]; [0050] - [0054], [0057].
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1) and Khater (US Pub. No. 2020/0300763 A1) in view of Sammak et al. US Pub. No. 2004/0041347 A1).
With regards to claim 4, Rothenberg discloses the claimed invention according to claim 4, but fails to expressly disclose obtaining the boundaries comprising obtaining an optical image of the plurality of cells; performing a segmentation algorithm on the optical image of the plurality of cells; and extending a border obtained by the segmentation algorithm by a predetermined distance.
Sammak provides automated systems, methods, screens, and software for the analysis of cell spreading, providing cells containing fluorescent reporter molecules in an array of locations, contacting the cells with a test stimulus, acquiring images from the cells, and automatically calculating one or more morphological features that provide a measure of cell spreading (Abstract). Sammak teaches a scanning cell array operation [0097] – [0105]. Sammak teaches using markers, generating masks/segments/patterns and using operations to create regions along segmented elements [0065] – [0069], [0103] - [0105].
In view of the utility, by applying a number of analytical methods simultaneously to measure features at multiple wavelengths and improve the users screening, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to also include the teachings such as that taught by Sammak.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1), Khater (US Pub. No. 2020/0300763 A1) and Sammak et al. US Pub. No. 2004/0041347 A1) in view of Levet et al. (A tessellation-based colocalization analysis approach for single-molecule localization microscopy. Nat Commun. 2019 May 30;10(1):2379. doi: 10.1038/s41467-019-10007-4. PMID: 31147535; PMCID: PMC6542817).
With regards to claim 5, Rothenberg discloses the claimed invention according to claim 4, but fails to expressly disclose constructing the data vector further comprising performing colocalization analysis on an overlapping area between any two of the plurality of cells.
Levet relates to single molecule localization microscopy (Abstract). Level further teaches a simple and efficient parameter-free colocalization method, called Coloc-Tesseler (CT), using poly topes (polygons in 2D or polyhedrons in 3D) embedding the localizations to compute the molecular co-organization of 2 and 3-dimensional λSMLM data. Coloc-Tesseler relies on the normalized pair-density parameter computed from the overlapping Voronoï diagrams of the two molecular species to quantify their spatial co-organization in a robust to density and parameter free manner (Page 2) (Figure 1) (See method steps 1 – 9; pp 9 – 10).
In view of the utility, to evaluate overlapping clusters and organization at cell boundaries, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to also include the teachings such as that taught by Levet.
Claim(s) 7, 29 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1) and Khater (US Pub. No. 2020/0300763 A1) in view of Kromp et al. (EP 3 054 279 A1).
With regards to claim 7, Rothenberg discloses the claimed invention according to claim 6, absent some degree of criticality, the recitations that the dimension reduction analysis comprises Principal Component Analysis, PCA such that the feature vector comprises a first number of principal components obtained from the data vector, and wherein the first dimension is the first number is considered only a matter of design choice involving routine skill of the art.
For example, Kromp quantification of biologically meaningful cell populations [0001] (Abstract). Kromp teaches that Principal Component Anaysis (PCA), principal components and PCA-reduced feature spaces/axes [0075], [0078] – [0080], [0088]. Notice that once the two suitable axes are chosen, the gate can be drawn in the ISP. Setting a gate is equal to declaring a region containing a population of interest. While setting the gate, the observations being inside the gate are assigned to one class, while all of the other observations are assigned to another class, see Figure 1F [0076].
In view of the utility, to analyze overlapping clusters and organization at the cell boundaries, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Kromp.
With regards to claim 29, Rothenberg discloses the claimed invention according to claim 6, absent some degree of criticality, the recitations that the data analysis further comprises: performing a partitioning analysis on the reference feature vector such that a PCA space defined by the principal components is partitioned into a second number of regions is considered only a matter of design choice involving routine skill of the art.
For example, Kromp quantification of biologically meaningful cell populations [0001] (Abstract). Kromp teaches that Principal Component Anaysis (PCA), principal components and PCA-reduced feature spaces/axes [0075], [0078] – [0080], [0088]. Notice that once the two suitable axes are chosen, the gate can be drawn in the ISP. Setting a gate is equal to declaring a region containing a population of interest. While setting the gate, the observations being inside the gate are assigned to one class, while all of the other observations are assigned to another class, see Figure 1F [0076].
In view of the utility, to analyze overlapping clusters and organization at the cell boundaries, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Kromp.
With regards to claim 30, Rothenberg discloses the claimed invention according to claim 29, but fails to expressly disclose that the partitioning analysis comprises k-means clustering.
Kromp teaches examples of machine learning refinement classifiers can be, but are not limited to the use of n-fold cross validation in combination with an ensemble of classification methods like Random Forests, Support Vector Machines or k-Nearest Neighbors [0058], [0081]. Kromp further expressly teaches performing a k-means clustering on the RDF or PCA reduced feature space [0058], [0081].
In view of the utility, to analyze overlapping clusters and organization at the cell boundaries and import the sorting as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Kromp.
Claim(s) 12 -14 and 18 - 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1) and Khater (US Pub. No. 2020/0300763 A1) in view of Einsele et al. (US Pub. No. 2020/0209240 A1).
With regards to claim 12, Rothenberg discloses the claimed invention according to claim 1 but fails to expressly disclose that the reference cells of said one or more defined types correspond to diseased cells from patients which are confirmed to be responsive to a specific medical treatment.
Einsele relates to immunotherapy with chimeric antigen receptor (CAR}- engineered T-cells. In particular, the invention relates immunotherapy with chimeric antigen receptor (CAR)-engineered T-cells to target sub-populations of cancer cells that are characterized by low expression of a cancer cell surface antigen, more particular Einsele relates to immunotherapy with chimeric antigen receptor (CAR)-engineered T-cells targeting CD19 (CD19CART) in multiple myeloma, a clonal proliferation of plasma cells (Abstract).
Einsele expressly discloses single-molecule sensitive fluorescence imaging methods such as dSTORM can aid in stratifying myeloma patients according to CD19 expression to identify patients who have the highest chance to benefit from the specified treatment. The treatments are not only for CD19CART in MM, but also for CART approaches targeting alternative antigens in other hematologic and solid tumor malignancies to exploit their full therapeutic potential and to ensure patient safety [0165] – [0169]; [0172]; [0192]-[0194].
In view of the utility, to analyze and/or target sub-populations of cancer cells as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Einsele.
With regards to claims 13, 14 and 18 – 21, see the rejection of claim 12.
Claim(s) 15 – 16, 22 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1), Khater (US Pub. No. 2020/0300763 A1) and Einsele et al. (US Pub. No. 2020/0209240 A1) in view of Ports et al. (WO 2018/102785 A2).
With regards to claim 15, Rothenberg discloses the claimed invention according to claim 13 but fails to expressly disclose that wherein the one or more species of proteins detected includes CAR.
Ports relates to methods and compositions for use of therapeutic T-cells (Abstract). Notice how the recombinant receptor is a chimeric receptor, which optionally is a chimeric antigen receptor (CAR) [0031].
Ports teaches the receptor, e.g. CAR, expressed by the cells, is detectable by quantitative PCR (qPCR) or by flow cytometry in the subject, plasma, serum, blood, tissue and/or disease site thereof, e.g. , tumor site, at a time that is at least about 3 months, at least about 6 months, at least about 12 months, at least about 1 year, at least about 2 years, at least about 3 years, or more than 3 years, following the administration of the cells, e.g. , following the initiation of the administration of the T cells, e.g. , CAR-expressing T cells, and/or the inhibitor [0145], [0166],[0496], [0580].
In view of the utility, to analyze and/or target sub-populations of cancer cells as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Ports.
With regards to claim 16, Rothenberg discloses the claimed invention according to claim 13 but fails to expressly disclose wherein the one or more species of proteins detected correspond to one or more of (i) a surface marker for naive T cells (ii) a surface marker for memory T cells, (iii) a surface marker for effector T cells (iv) a surface marker for exhausted T- cells.
Ports relates to methods and compositions for use of therapeutic T-cells (Abstract). Notice how the recombinant receptor is a chimeric receptor, which optionally is a chimeric antigen receptor (CAR) [0031].
Ports teaches assessing T-cell phenotype, activation, proliferation, persistence, naïve, memory, effector, exhausted T-cell marks and related markers [0117], [0133], [0166], [0363], [0366], [0440].
In view of the utility, to analyze and/or target sub-populations of cancer cells as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Ports.
With regards to claim 22, Rothenberg discloses the claimed invention according to claim 12 but fails to expressly disclose wherein the method involves identifying a suitable medical treatment for a patient from a range of multiple specific medical treatments, and wherein the reference data comprises a plurality of reference feature vectors each relating to reference cells confirmed to be responsive to one of the multiple specific medical treatments.
Ports relates to methods and compositions for use of therapeutic T-cells (Abstract). Notice how the recombinant receptor is a chimeric receptor, which optionally is a chimeric antigen receptor (CAR) [0031].
Ports teaches combination therapy and being responsive to multiple specific medical treatments [0160] – [0182]; [0490].
In view of the utility, to analyze and/or target sub-populations of cancer cells as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Ports.
With regards to claim 31, see the rejections of claims 1, 12, 16 and 22.
Claim(s) 26 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rothenberg et al. (US Pub. No. 2014/0273003 A1) and Khater (US Pub. No. 2020/0300763 A1) in view of Zhang et al. (CN 103902997 A).
With regards to claims 26 and 27, Rothenberg discloses the claimed limitation of claim 1 but fails to expressly disclose the step of classifying the cells data analysis involves evaluating a probability distance metric between the data vector and the one or more reference feature vector; and determining, based on the probability distance metric, whether the cells are classified into one of the defined types, wherein constructing a first probability distribution from the data vector and a second probability distribution from the reference feature vector, wherein constructing the first probability distribution comprises: discretising respective reference feature vector of the reference cells; and constructing a normalized histogram.
Zhang teaches to a biological cell microscope image classification characteristic sub space integration method [0001].
Zhang further teaches a calculation distance Dl, D2, D3 is mapped into a classification probability [0033] and extracting image local texture characteristics [0065] – [0106] (Abstract).
In view of the utility, to improve classification as needed, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify Rothenberg to include the teachings such as that taught by Zhang.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DJURA MALEVIC/Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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