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 .
Applicant’s response to the Non-final Office Action dated 12/04/2025, filed with the office on 03/04/2026, has been entered and made of record.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on 03/04/2026 and 03/09/2026 have been reviewed and the listed references have been considered.
Status of Claims
Claims 1-16 and 18-22 are pending. Claims 1, 3, 7, 8, 12, 13 and 19-21 are amended. Claim 17 is cancelled.
Response to Arguments
Applicant’s amendment of independent Claims 1, 19 and 20, which has altered the scope of the claims of the instant application, has necessitated the new ground(s) of rejection presented in this office action with respect to claims of the instant application. Accordingly, because Applicant’s arguments are merely directed to the amended portion of the claims, new analyses have been presented below, which make Applicant’s arguments moot. Consequently, THIS ACTION IS MADE FINAL.
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.
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 1-3, 5-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Black et al. (US 2018/0089495 A1 - disclosed in IDS submitted on 05/04/2023), in view of Yuan (US 2017/0365053 A1) and in further view of Gaire et al. (WO 2017/198790 A1).
Regarding claim 1, Black teaches, A method comprising: accessing, a digital pathology image that depicts a section of a biological sample (Black, ¶0029: “In one embodiment, the method includes: (i) obtaining digital images of stained tissue sections… implemented by a computer”) collected from a subject having a given medical condition; (Black, ¶0028: “patient status includes diagnosis of inflammatory status, disease state”) detecting, within the digital pathology image, a set of individual biological cell depictions, (Black, ¶0007: “digital image analysis to extract sophisticated statistics pertaining to the distribution and patterns of cells”) the set of individual biological cell depictions comprising a first set of individual biological cell depictions of a first class of biological cells (Black, ¶0045: “classification of tissue object subsets (e.g., tumor cell class”; Applicant’s specification, ¶0007: “the first class of biological object is a tumor cell”) and a second set of individual biological cell depictions of a second class of individual biological cells, (Black, ¶0027: “tissue object is one or more of a cell (e.g., immune cell”; Applicant’s specification, ¶0007: “the second class of biological object is an immune cell”) plurality of relational-location representations, a spatial- distribution metric (Black, ¶0047: “extract one or more spatial distribution features and associated summary statistics describing the spatial distribution of tissue objects or tissue object subsets (e.g., tumor cells, biomarker-positive cells, tumor nests”) characterizing a degree to which at least part of the first set of individual biological cell depictions are depicted as being interspersed with at least part of the second set of individual biological cell depictions; (Black, ¶0051: “a series of cell locations, defined by the X and Y positions of the centroids of cells, are used to build a pairwise distance matrix between points”) generating, based on the spatial-distribution metric, a result that corresponds to a prediction regarding a degree to which a given treatment that modulates immunological response will effectively treat the given medical condition of the subject; (Black, ¶0008: “the spatial distribution features are summarized to generate a patient-specific diagnostic score, and this score is evaluated to guide patient treatment decisions”) determining that the subject is eligible for a clinical trial based on the result; (Black, ¶0030: “determine whether or not said patient or patients are candidates for a specified therapy”). However, Black does not explicitly teach, wherein the second set comprises a plurality of individual biological cells; generating a plurality of relational-location representations of the biological cell depictions, each of the one or more relational-location representations indicating a location of a first biological cell depiction relative to each second individual biological cell depiction of the second set of individual biological cell depictions and generating a display including an indication that the subject is eligible for the clinical trial.
In an analogous field of endeavor, Yuan teaches, each of the one or more relational-location representations indicating a location of a first biological cell depiction (Yuan, ¶0076: “a quantitative measurement of each lymphocyte's proximity to cancer cells and spatial location relative to cancer cells”) relative to each second individual biological cell depiction of the second set of individual biological cell depictions (Yuan, ¶0084: “obtaining a lymphocyte-to-cancer measurement for each lymphocyte may be carried out based on a distance measure between a lymphocyte and one or more cancer cells, such as the Euclidean distance”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black using the teachings of Yuan to introduce relational-location of tumor cells around a single lymphocyte. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying the interspersion of cancer cells around a lymphocyte. Therefore, it would have been obvious to combine the analogous arts Black and Yuan to obtain the above-described limitations of claim 1. However, the combination of Black and Yuan does not explicitly teach, wherein the second set comprises a plurality of individual biological cells; generating a plurality of relational-location representations of the biological cell depictions and generating a display including an indication that the subject is eligible for the clinical trial.
In another analogous field of endeavor, Gaire teaches, wherein the second set comprises a plurality of individual biological cells; (Gaire, page 5, lines 14-16: “once the individual immune cells and tumor cells have been identified, the distance computation for individual pairs of neighboring tumor cells and in immune cells may be performed”) generating a plurality of relational-location representations of the biological cell depictions (Gaire, page 14, lines 18-19: “spatial relationship between relevant immune response promoting immune cells and their potential targets, the tumor cells, is provided”) and generating a display including an indication that the subject is eligible for the clinical trial. (Gaire, page 3, line 15: “displaying the classification result on a display device”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan using the teachings of Gaire to introduce a spatial relationship between a plurality of tumor cells and a plurality of immune cells. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying the effectiveness of a treatment based on the spatial relationship between the different types of cells. Therefore, it would have been obvious to combine the analogous arts Black, Yuan and Gaire to obtain the invention in claim 1.
Regarding claim 2, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the spatial-distribution metric comprises: a metric defined based on a K-nearest-neighbor analysis; a metric defined based on Ripley's K-function; a Morisita-Horn index; a Moran's index; a metric defined based on a correlation function; a metric defined based on a hotspot/coldspot analysis; or a metric defined based on a Kriging-based analysis. (Black, ¶0050: “quantification of the pattern would use summative methods such as, but not limited to: nearest neighbor distances, pair correlation functions, Ripley's K-function and related functions, or variogram indexing.”)
Regarding claim 3, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein: the spatial-distribution metric is of a first type of metric; (Black, ¶0050: “a point-pattern analysis approach is defined, quantification of the pattern would use summative methods such as, but not limited to: nearest neighbor distances” and claim 13: “a first spatial distribution feature”) the method further comprises determining, using the plurality of relational-location representations, a second spatial-distribution metric (Black, ¶0050: “a point-pattern analysis approach is defined, quantification of the pattern would use summative methods such as… Ripley's K-function”; and claim 13: “a second spatial distribution feature”) characterizing the degree to which at least part of the first set of individual biological cell depictions are depicted as being interspersed with at least part of the second set of individual biological cell depictions, (Black, ¶0018: “FIG. 10 provides an example of a point-referenced analysis of cells in an image to derive a continuous surface representation of cell density”; also see Fig. 9: Class 1 and Class 2) wherein the second spatial- distribution metric is of a second type of metric (Black, ¶0050: “Ripley's K-function”) that is different from the first type of metric; (Black, ¶0050: “nearest neighbor distances”) and the result is generated further based on the second spatial-distribution metric. (Black, ¶0008: “the spatial distribution features are summarized to generate a patient-specific diagnostic score, and this score is evaluated to guide patient treatment decisions”).
Regarding claim 5, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein generating the result comprises comparing a value of the spatial-distribution metric to a threshold value. (Black, ¶0057: “the thresholding of one or more point-referenced analysis functions by another, point-pattern analysis of point-referenced analysis function summary values”).
Regarding claim 6, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the given medical condition is a type of cancer (Black, ¶0027: “a tissue compartment (e.g., tumor, tumor microenvironment (TME)”) and wherein the given treatment is an immune-checkpoint-blockade treatment. (Black, ¶0068: “patient-specific diagnostic score is evaluated relative to the patient selection criteria defined for the tissue-based assay and drug to guide determination of patient eligibility to receive the drug”).
Regarding claim 7, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the plurality of relational-location representations include, for each individual biological cell depiction of the set of individual biological cell depictions, a set of coordinates that identifies a location of the individual biological cell depiction within the digital pathology image. (Black, ¶0030: “A tissue-based assay enables evaluation of tissue objects and marker stains (e.g., presence and intensity) for biologic molecules (e.g., chromatin, biomarkers) relative to position (e.g., x-y coordinates, polar coordinates) in the tissue”).
Regarding claim 8, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein generating the plurality of relational-location representation of the set of individual biological cell depictions comprises: identifying, for each individual biological cell depiction of the first set of individual biological cell depictions, a first point location within the digital pathology image corresponding to the individual biological cell depiction; (Black, ¶0054: “each tissue object is represented as a point and given a mark (e.g., cell class by biomarker-positive, biomarker-negative, tumor cell) based on one or more image analysis features”) identifying, for each individual biological cell depiction of the second set of individual biological cell depictions, a second point location within the digital pathology image corresponding to the individual biological cell depiction; (Black, ¶0054: “each tissue object is represented as a point and given a mark (e.g., cell class by biomarker-positive, biomarker-negative, tumor cell) based on one or more image analysis features”) and comparing the first point location and the second point location. (Black, ¶0048: “Spatial point-patterns can be used to identify spatial trends in point density and position”).
Regarding claim 9, Black in view of Yuan and in further view of Gaire teaches, The method of claim 8, wherein the first point location within the digital pathology image is selected by calculating, for the individual biological cell depiction of the first set of individual biological cell depictions, a mean point location, a centroid point location, a median point location, or a weighted point location. (Black, ¶0051: “a series of cell locations, defined by the X and Y positions of the centroids of cells, are used to build a pairwise distance matrix between points”).
Regarding claim 10, Black in view of Yuan and in further view of Gaire teaches, The method of claim 8, wherein determining the spatial-distribution metric comprises calculating, for each of at least some of the first set of individual biological cell depictions and for each of at least some of the second set of individual biological cell depictions, a distance between the first point location corresponding to the individual biological cell depiction of the first set of individual biological cell depictions and the second point location corresponding to the individual biological cell depictions of the second set of individual biological cell depictions. (Black, ¶0051: “a series of cell locations, defined by the X and Y positions of the centroids of cells, are used to build a pairwise distance matrix between points”).
Regarding claim 11, Black in view of Yuan and in further view of Gaire teaches, The method of claim 8, wherein determining the spatial-distribution metric further comprises identifying, for each of the at least some of the first set of individual biological cell depictions, one or more of the second set of individual biological cell depictions associated with a distance between the first point location corresponding to the individual biological cell depiction of the first set of the individual biological cell depictions and the second point location corresponding to the individual biological cell depiction of the second set of individual biological cell depictions. (Black, ¶0051: “identify areas of clustering or areas of dispersion of the biomarker in the tissue, where the value of the K-statistic is the cumulative average number of cells lying within a distance r from a typical cell”).
Regarding claim 12, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the plurality of relational-location representations include, for each of a set of image regions in the digital pathology image, (Black, ¶0063: “create a lattice. This lattice can be regularly (e.g., square grid) or irregularly shaped (e.g., tumor nest regions, stroma regions”) a representation of an absolute or relative quantity of individual biological cell depictions of the first class of individual biological cell identified as being located within the region and an absolute or relative quantity of individual biological cell depictions of the second class of individual biological cell identified as being located within the region. (Black, ¶0064: “FIG. 13 illustrates this aspect of the invention whereby a stained-tissue section is evaluated using an area-based analysis framework for biomarker-positive cell density within each grid placed on the image”).
Regarding claim 13, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the plurality of relational-location representations include a distance-based probability of an individual biological cell depiction of the first set of individual biological cell depictions being depicted as located within a given distance from an individual biological cell depiction of the second set of individual biological cell depictions. (Gaire, page 25, lines 6-7: “FIG. 10 depicts a plot indicating the probability density that a particular immune cell lies within a given distance from its nearest tumor cell”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the teachings of Gaire to introduce distance-based probability. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of assessing the probability of healthy cell being located near tumor cells. Therefore, it would have been obvious to combine the analogous arts Black, Yuan, Gaire and Gaire to obtain the invention of claim 13.
Regarding claim 14, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, further comprising accessing genetic sequencing or radiology imaging data for the subject, (Gaire, page 31, lines 13-14: “the known genetic MSS status of the corresponding patient”) wherein the result is generated further based on a characteristic of the genetic sequencing or radiology imaging data. (Black, ¶0024: “determine a patient-specific score, and guiding treatment of the patient based on evaluation of the patient-specific score relative to patient selection criteria”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the additional teachings of Gaire to introduce genetic information of the subject. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of assessing the effectiveness of a particular treatment on a known gene type. Therefore, it would have been obvious to combine the analogous arts Black, Yuan and Gaire to obtain the invention of claim 14.
Regarding claim 15, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the first class of individual biological cells comprises individual tumor cells (Black, ¶0045: “classification of tissue object subsets (e.g., tumor cell class”) and the second class of individual biological cells comprises individual immune cells. (Black, ¶0027: “a tissue object is one or more of a cell (e.g., immune cell)”).
Regarding claim 18, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein generating the display including the indication that the subject is eligible for the clinical trial comprises informing (Black, ¶0004: “inform a physician for determination of diagnosis, prognosis, or to guide future treatment decisions”) the subject of the determination of eligibility for the clinical trial. (Black, ¶0068: “determination of patient eligibility to receive the drug”).
Regarding claim 19, it recites a system with elements corresponding to the steps of the method recited in claim 1. Therefore, the recited elements of system claim 19 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 1. Additionally, the rationale and motivation to combine Black, Yuan and Gaire presented in rejection of claim 1, apply to this claim. Gaire additionally teaches, A system comprising: one or more data processors; and a non-transitory computer readable storage medium communicatively coupled to the one or more data processors, and including instructions which, when executed by the one or more data processors, cause the one or more data processors to perform one or more operations comprising: (Gaire, page 21, lines 22-24: “a computer readable medium comprising instructions that when executed by a processor causes the processor to execute a method”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the additional teachings of Gaire to introduce a computer system. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of automatically performing the steps of method in a computer. Therefore, it would have been obvious to combine the analogous arts Black, Yuan and Gaire to obtain the invention of claim 19.
Regarding claim 20, it recites a computer-readable non-transitory storage media including instructions corresponding to the steps of the method recited in claim 1. Therefore, the recited instructions of computer-readable non-transitory storage media claim 19 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 1. Additionally, the rationale and motivation to combine Black, Yuan and Gaire presented in rejection of claim 1, apply to this claim. Gaire additionally teaches, One or more computer-readable non-transitory storage media including instructions that, when executed by one or more data processors, cause the one or more data processors to perform operations comprising: (Gaire, page 21, lines 22-24: “a computer readable medium comprising instructions that when executed by a processor causes the processor to execute a method”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the additional teachings of Gaire to introduce a computer-readable non-transitory storage media. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of storing the instructions to be executed by a computer. Therefore, it would have been obvious to combine the analogous arts Black, Yuan and Gaire to obtain the invention of claim 20.
Claims 4, 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Black et al. (US 2018/0089495 A1 - disclosed in IDS submitted on 05/04/2023), in view of Yuan (US 2017/0365053 A1), in further view of Gaire et al. (WO 2017/198790 A1) and still in further view of Haimson et al. (US 2020/0272919 A1).
Regarding claim 4, Black in view of Yuan and in further view of Gaire teaches, The method of claim 3, wherein generating the result comprises. However, the combination of Black, Yuan and Gaire does not explicitly teach, processing the first spatial-distribution metric and the second spatial-distribution metric using a trained machine-learning model, the trained machine-learning model having been trained using a set of training elements, each of the set of training elements corresponding to another subject having received a particular treatment associated with the clinical trial, and each of the set of training elements including another set of spatial-distribution metrics and a responsiveness value indicating a degree to which the given treatment activated an immunological response in the other subject.
In an analogous field of endeavor, Haimson teaches, processing the first spatial-distribution metric and the second spatial-distribution metric using a trained machine-learning model, (Haimson, ¶0008: “the performance status score is determined by at least one of a trained machine learning model or a natural language processing algorithm; and provide an output indicative of the performance status score”) the trained machine-learning model having been trained using a set of training elements, each of the set of training elements corresponding to another subject having received a particular treatment associated with the clinical trial, (Haimson, ¶0035: “generate a trained model (e.g., a supervised machine learning system) based on a set of training data associated with a patient, and may use the model to generate a survivability score or a number of other predictions related to the patient”) and each of the set of training elements including another set of spatial-distribution metrics and a responsiveness value indicating a degree to which the given treatment activated an immunological response in the other subject. (Haimson, ¶0036: “Training data 410 may include a plurality of patient medical records (e.g., “Record 1,” “Record 2,” “Record 3,” etc., as shown in FIG. 4) for which various results that are to be estimated by the model are already known (e.g. patient survivability, patient performance status, tumor response, disease progression timeframes, suitability for a clinical trial”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the teachings of Haimson to introduce training a model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of assessing a responsiveness value of a particular treatment. Therefore, it would have been obvious to combine the analogous arts Black, Yuan, Gaire and Haimson to obtain the invention of claim 4.
Regarding claim 21, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein the spatial-distribution metric is a first spatial-distribution metric (Black, ¶0050: “a point-pattern analysis approach is defined, quantification of the pattern would use summative methods such as, but not limited to: nearest neighbor distances” and claim 13: “a first spatial distribution feature”) and the method further comprises: determining, using the plurality of relational-location representations, a second spatial-distribution metric (Black, ¶0050: “a point-pattern analysis approach is defined, quantification of the pattern would use summative methods such as… Ripley's K-function”; and claim 13: “a second spatial distribution feature”) characterizing a degree to which at least part of the first set of individual biological cell depictions are depicted as being interspersed with at least part of the second set of individual biological cell depictions, (Black, ¶0018: “FIG. 10 provides an example of a point-referenced analysis of cells in an image to derive a continuous surface representation of cell density”; also see Fig. 9: Class 1 and Class 2) wherein the second spatial-distribution metric is a different type of metric (Black, ¶0050: “Ripley's K-function”) than the first spatial-distribution metric, (Black, ¶0050: “nearest neighbor distances”) wherein generating the result comprises processing the first spatial-distribution metric and the second spatial-distribution metric (Black, ¶0008: “the spatial distribution features are summarized to generate a patient-specific diagnostic score, and this score is evaluated to guide patient treatment decisions”). However, the combination of Black, Yuan and Gaire does not explicitly teach, using a machine-learning model, the machine-learning model being associated with elements corresponding to another subject having received the given treatment associated with a clinical trial.
In an analogous field of endeavor, Haimson teaches, using a machine-learning model, the machine-learning model (Haimson, ¶0008: “a trained machine learning model or a natural language processing algorithm) being associated with elements corresponding to another subject having received the given treatment associated with a clinical trial. (Haimson, ¶0033: “clinical trial may involve treating the patient using a particular therapy”; and ¶0036: “Training data 410 may include a plurality of patient medical records… patient survivability, patient performance status, tumor response”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the teachings of Haimson to introduce a learning model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of automatically assessing a responsiveness value of a particular treatment. Therefore, it would have been obvious to combine the analogous arts Black, Yuan, Gaire and Haimson to obtain the invention of claim 21.
Regarding claim 22, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, wherein generating the result comprises processing the spatial-distribution metric. However, the combination of Black, Yuan and Gaire does not explicitly teach, using a trained machine-learning model trained by: segmenting a training dataset into a training data portion and a validation data portion; segmenting the training data portion into a plurality of training data subsets; for each training data subset: designating the training data subset as a hold-out subset; training a plurality of machine-learning models on all other training data subsets; and evaluating a performance of the plurality of machine-learning models based on the hold-out subset; selecting a machine-learning model from the plurality of machine-learning models based on the evaluations; and generating the trained machine-learning model by tuning the selected machine-learning model with the validation data portion.
In an analogous field of endeavor, Haimson teaches, using a trained machine-learning model trained by: (Haimson, ¶0036: “Training of model 430 may involve the use of a training data set 410, which may be input into training algorithm 420 to develop the model”) segmenting a training dataset into a training data portion and a validation data portion; (Haimson, ¶0055: “a training data set for training/testing a machine learning model”) segmenting the training data portion into a plurality of training data subsets; (Haimson, ¶0036: “Training data 410 may include a plurality of patient medical records (e.g., “Record 1,” “Record 2,” “Record 3”) for each training data subset: designating the training data subset as a hold-out subset; (Haimson, ¶0040: “remaining portion of training data 410 may be used to test the trained model 430 and evaluate its performance”) training a plurality of machine-learning models on all other training data subsets; (Haimson, ¶0038: “a certain portion of the data) may be placed through a training algorithm 420 to train the model”) and evaluating a performance of the plurality of machine-learning models based on the hold-out subset; (Haimson, ¶0040: “remaining portion of training data 410 may be used to test the trained model 430 and evaluate its performance”) selecting a machine-learning model from the plurality of machine-learning models based on the evaluations; (Haimson, ¶0040: “Where the level of deviation is within a desired limit (e.g., 10%, 5%, or less), one or more models 430 may be deemed suitable for operating”) and generating the trained machine-learning model by tuning the selected machine-learning model with the validation data portion. (Haimson, ¶0040: “Performance measures 460 may be used to update model 430 (e.g., retrain the model) to reduce deviations”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the teachings of Haimson to introduce training and validating a learning model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of selecting and optimizing a learning model. Therefore, it would have been obvious to combine the analogous arts Black, Yuan, Gaire and Haimson to obtain the invention of claim 22.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Black et al. (US 2018/0089495 A1 - disclosed in IDS submitted on 05/04/2023), in view of Yuan (US 2017/0365053 A1), in further view of Gaire et al. (WO 2017/198790 A1) and still in further view of Hong et al. (US 2019/0259499 A1).
Regarding claim 16, Black in view of Yuan and in further view of Gaire teaches, The method of claim 1, further comprising. However, the combination of Black, Yuan and Gaire does not explicitly teach, receiving user input data from a user device comprising an identifier of the subject, wherein the computing system accesses the digital pathology image in response to receiving the identifier; wherein generating the display including the indication that the subject is eligible for the clinical trial comprises providing the indication that the subject is eligible for the clinical trial to the user device.
In an analogous field of endeavor, Hong teaches, receiving user input data from a user device (Hong, ¶0046: “The client 204 receives input from the input device 201”) comprising an identifier of the subject, (Hong, ¶0067: “patient data may include encounter data such as patient identifiers”) wherein the computing system accesses the digital pathology image in response to receiving the identifier; (Hong, ¶0046: “computing device 110 also shown in FIG. 1; a medical data device (e.g., a small or large-format device used in a healthcare setting to collect, manage or generate patient diagnostic data”) wherein generating the display including the indication (Hong, ¶0047: “display output including predicted SOFA scores for a given patient”) that the subject is eligible for the clinical trial comprises providing the indication that the subject is eligible for the clinical trial (Hong, ¶0029: “SOFA scores for a cancer patient may identify the need to… initiate the administration of an additional approved drug(s) in order to moderate future symptoms of the patient as a participant in a clinical trial”) to the user device. (Hong, ¶0033: “outputting the SOFA scores to memory, outputting the SOFA scores to a computing device for display on a user interface”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Black in view of Yuan and in further view of Gaire using the teachings of Hong to introduce a user device. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of communicating the findings of the image analysis through a user device. Therefore, it would have been obvious to combine the analogous arts Black, Yuan, Gaire and Hong to obtain the invention of claim 16.
Conclusion
THIS ACTION IS MADE FINAL. 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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/MEHRAZUL ISLAM/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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