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 .
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to non-transitory medium and methods, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites receiving input indicative of a request to determine whether an individual whose eye is imaged as part of a diagnostic session should be referred for treatment of a pathological condition; applying a model to a digital image of the eye, so as to produce an output that is associated with a proposed next step diagnosis for the pathological condition; determining, based on the output, that the digital image includes evidence of the pathological condition; and causing display of a notification that specifies the individual requires the proposed next step diagnosis by a healthcare professional.
Independent claim 10 recites acquiring multiple digital images that are generated for the purpose of remotely diagnosing multiple individuals, wherein each digital image of the multiple digital images is associated with a corresponding individual of multiple individuals; applying a model to the multiple digital images, so as to produce multiple outputs, wherein each output of the multiple outputs is associated with a proposed next step diagnosis for a pathological condition for the corresponding individual; and stratifying the multiple individuals based on the multiple outputs.
Independent claim 16 recites acquiring a digital image that is generated as part of a diagnostic session in which an eye of an individual is imaged; applying a model to the digital image to produce an output that indicates whether pathological features that are indicative of diabetic retinopathy are present in the digital image; determining, based on the output, that the digital image includes at least one pathological feature that is indicative of diabetic retinopathy; and causing display of a notification that specifies further examination of the digital image by a healthcare professional is needed.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the invention is “for stratifying patients for examination by healthcare professionals based on analysis of the pathological features in digital images” (see: specification paragraph 2). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a problem where “[m]ost of the latency in due to the availability of graders that may wait until breaks (e.g., lunch) or the conclusion of the workday to examine digital images…the grader may be delayed in actually performing that examination” (see: specification paragraph 22) in a context where the “speed of referral for treatment can make a major difference in the speed and adherence of treatment, and therefore in saving vision” (see: specification paragraph 23). The present invention addresses these problems with “approaches to assessing digital images…so as to stratify patients for examination…if patients with more severe diseases are examined before patients with less severe diseases, better outcomes are generally achieved - and also improves the efficiency of examination and reduces consumption of necessary human resources and computational resources” (see: specification paragraph 24). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising…” (claim 1), “is trained…” (claim 2), “by an imaging device…from the imaging device…” (claim 7), “by a processor…by the processor…by the processor…” (claim 10), and “by the processor…” (claim 14), which are additional elements that are recited at a high level of generality (e.g., the “non-transitory medium” is configured to perform functions through no more than a statement that “instructions stored thereon” are to be “executed by a processor of a computing device”; the “processor” is configured through no more than a statement than that functions are to be performed “by” said processor; a “trained” model is configured though no more than a statement than that each of multiple models “is” trained for a desired outcome) such that they amount to no more than mere instruction to apply the exception using generic computer elements. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer elements. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic elements. Mere instructions to apply an exception using generic elements cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic elements that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea(s). The originally filed specification supports this conclusion:
Page 10, where “The detailed descriptions which follow are presented in part in terms of algorithms and symbolic representations of operations on data bits within a computer memory representing alphanumeric characters or other information. A computer generally includes a processor for executing instructions and memory for storing instructions and data. When a general purpose computer has a series of machine encoded instructions stored in its memory, the computer operating on such encoded instructions may become a specific type of machine, namely a computer particularly configured to perform the operations embodied by the series of instructions. Some of the instructions may be adapted to produce signals that control operation of other machines and thus may operate through those control signals to transform materials far removed from the computer itself. These descriptions and representations are the means used by those skilled in the art of data processing arts to most effectively convey the substance of their work to others skilled in the art.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-9, 11-15, and 17-20, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication 2020/0321102 to Barnes.
As per claim 1, Barnes discloses a non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:
receiving input indicative of a request to determine whether an individual whose eye is imaged as part of a diagnostic session should be referred for treatment of a pathological condition (Para [0017]-"ln some embodiments, the diagnostic feature metric is derived through multivariate Cox modeling taking into account multiple computed image feature metrics. In some embodiments, the multiple computed image feature metrics for Cox modeling are predetermined (e.g. multiple expression scores as determined by a pathologist)."; Para [0039]-"As used herein, the term "biological sample" or "tissue sample" refers to any sample including a biomolecule (such as a protein, a peptide, a nucleic acid, a lipid, a carbohydrate, or a combination thereof) that is obtained from any organism including viruses. Other examples of organisms include mammals (such as humans; veterinary animals like cats, dogs, horses, cattle, and swine; and laboratory animals like mice, rats and primates), insects, annelids, arachnids, marsupials, reptiles, amphibians, bacteria, and fungi. Biological samples include tissue samples (such as tissue sections and needle biopsies of tissue), cell samples (such as cytological smears such as Pap smears or blood smears or samples of cells obtained by microdissection), or cell fractions, fragments or organelles (such as obtained by lysing cells and separating their components by centrifugation or otherwise). Other examples of biological samples include blood, serum, urine, semen, fecal matter, cerebrospinal fluid, interstitial fluid, mucous, tears, sweat, pus, biopsied tissue (for example, obtained by a surgical biopsy or a needle biopsy), nipple aspirates, cerumen, milk, vaginal fluid, saliva, swabs (such as buccal swabs), or any material containing biomolecules that is derived from a first biological sample. In certain embodiments, the term "biological sample" as used herein refers to a sample (such as a homogenized or liquefied sample) prepared from a tumor or a portion thereof obtained from a subject."; Para [0040]-"A biomarker may be used to determine how well the body responds to a treatment for a disease or condition or if the subject is predisposed to a disease or
condition.");
applying a model to a digital image of the eye, so as to produce an output that is associated with a proposed next step diagnosis for the pathological condition (Para [0077]-"ln some embodiments, a multivariate Cox model module 208 may use a plurality of derived image feature metrics computed using the image feature extraction module 205 or may use prognostic features determined to be most relevant through machine learning using the prognostic feature derivation module 209.");
determining, based on the output, that the digital image includes evidence of the pathological condition (Para [0020]-"ln some embodiments, the patient outcome data is a primary end point data. In some embodiments, the primary end point data is at least one of overall patient survival time, recurrence free survival, drug response, or pathological complete response."; Para [0056]-"the database 212 may facilitate the storage of any primary and secondary endpoint data, as well as any associated patient data (e.g. patient name or identification, age, sex, weight, ethnicity, tumor size, tumor type, genetic information, pathological finds, etc.), whereby the data stored therein may be retrieved by the digital pathology system 200 and be used in further downstream processing (e.g. statistical analyses)."); and
causing display of a notification that specifies the individual requires the proposed next step diagnosis by a healthcare professional (Para [0009]- "The systems and methods described herein enable the skilled artisan to retrospectively analyze clinical trial data, i.e. patient outcome data and/or collected image data corresponding to patient biological samples, and decipher unexpected clinical trial outcomes, or guide medical professionals in identifying necessary changes before further clinical trials are conducted."; Para [0012]- "In some embodiments, the multiple image feature metrics or expression scores that are combined with the multivariate Cox model are pre-determined (e.g. determined by a pathologist or other medical profession, or based on diagnostic guidelines)."; Para [0043]-"The test helps a health care professional determine whether a particular therapeutic product's benefits to patients will outweigh any potential serious side effects or risks. In some embodiments, the clinical performance of the companion diagnostic is the ability of the test developed for a predictive biomarker (the companion diagnostic) to distinguish treatment responders from non-responders. Companion diagnostics can: (i) identify patients who are most likely to benefit from a particular therapeutic product; (ii) identify patients likely to be at increased risk for serious side effects as a result of treatment with a particular therapeutic product; and/or (iii) monitor response to treatment with a particular therapeutic product for the purpose of adjusting treatment to achieve improved safety or effectiveness."; Para [0162]-"ln some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.").
As per claim 2, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the operations further comprise: selecting the model from among multiple models stored in a data structure, wherein each model of the multiple models is trained to identify evidence of a different pathological condition through analysis of pixel information (Para [0109]-"An appealing feature of the Cox model is that the baseline hazard function is estimated non-parametrically, and so unlike most other statistical models, the survival. times are not assumed to follow a particular statistical distribution. As applied here, the p covariates are the various diagnostic features values under consideration."; Para [0111]-"ln some embodiments, for each patient sample of the test cohort, data is obtained regarding the outcome being tracked (time to death, time to recurrence, or time to progression) and the feature metric for each biomarker being analyzed. Candidate Cox proportional models are generated by entering the diagnostic feature data and survival data for each individual of the cohort into a computerized statistical analysis-software suite"; Para [0138]-"The multi-spectral image provided by the imaging acquisition module 202 is a weighted mixture of the underlying spectral signals associated the individual biomarkers and noise components. At any particular pixel, the mixing weights are proportional to the biomarker expressions of the underlying co-localized biomarkers at the particular location in the tissue and the background noise at that location. Thus, the mixing weights vary from pixel to pixel. The spectral unmixing methods disclosed herein decompose the multi-channel pixel value vector at each and every pixel into a collection of constituent biomarker end members or components and estimate the proportions of the individual constituent stains for each of the biomarkers.").
As per claim 3, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the operations further comprise: iterating through multiple models corresponding to different pathological conditions to determine a set of outputs indicative of a set of next step diagnoses corresponding to the different pathological conditions; wherein the model is one of multiple models applied to the digital image of the eye, such that multiple outputs are produced, and wherein each output of the multiple outputs is indicative of a proposed diagnosis for a different pathological condition (Para [0040]-"As used herein, the terms "biomarker" or "marker'' refer to a measurable indicator of some biological state or condition. In particular, a biomarker may be a protein or peptide, e.g. a surface protein, that can be specifically stained and which is indicative of a biological feature of the cell, e.g. the cell type or the physiological state of the cell. An immune cell marker is a biomarker that is selectively indicative of a feature that relates to an immune response of a mammal. A biomarker may be used to determine how well the body responds to a treatment for a disease or condition or if the subject is predisposed to a disease or condition. In the context of cancer, a biomarker refers to a biological substance that is indicative of the presence of cancer in the body. A biomarker may be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Genetic, epigenetic, proteomic, glycomic, and imaging biomarkers can be used for cancer diagnosis, prognosis, and epidemiology. Such biomarkers can be assayed in non-invasively collected biofluids like blood or serum. Several gene and protein based biomarkers have already been used in patient care including but, not limited to, AFP (Liver Cancer), BCR-ABL (Chronic Myeloid Leukemia), BRCA 1/BRCA2 (BreasUOvarian Cancer), BRAF V600E (Melanoma/Colorectal Cancer), CA-125 (Ovarian Cancer), CA19.9 (Pancreatic Cancer), CEA (Colorectal Cancer), EGFR (Nonsmall- cell lung carcinoma), HER-2 (Breast Cancer), KIT (Gastrointestinal stromal tumor), PSA (Prostate Specific Antigen), S100 (Melanoma), and many others. Biomarkers may be useful as diagnostics (to identify early-stage cancers) and/or prognostics (to forecast how aggressive a cancer is and/or predict how a subject will respond to a particular treatment and/or how likely a cancer is to recur)."; Para [0129]-"As a byproduct of the whole tumor image analysis, a large quantity of image feature metrics are computed for each patient tissue slide. If genomic analysis of the patient's tissue sample is also performed, similarly a set of molecular and genomic variants are output for each patient (tissue analysis data). A feature vector may be generated by combining the image feature metrics and the genomic features/tissue analysis from each patient along with certain clinical attributes. In some embodiments, from the generated feature vectors of all of the patients in a given cohort, a feature matrix may be constructed. In some embodiments, a statistical analysis of the feature matrix (e.g. principal component analysis, hierarchical clustering of features followed up with feature selection) will yield a condensed feature matrix for the cohort called a "cohort signature," i.e. a matrix that characterizes the tissue feature variation along with feature correlations. In some embodiments, the statistical analysis allows one to determine whether two given cohorts are similar or different. Specifically, the statistical analysis can facilitate a determine of how similar or how different two datasets are, e.g. how similar or how different Phase II and Phase Ill cohorts are.").
As per claim 4, Barnes teaches the invention as claimed, see discussion of claim 3, and further teaches:
wherein the operations further comprise: configuring the notification based on which of the multiple outputs indicate presence of a corresponding pathological condition, wherein said configuring includes selecting the healthcare professional from among multiple healthcare professionals or assigning multiple pathological conditions to the healthcare professional (Para [0012]-"ln some embodiments, the multiple image feature metrics or expression scores that are combined with the multivariate Cox model are pre-determined (e.g. determined by a pathologist or other medical profession, or based on diagnostic guidelines)."; Para [0043]-"The test helps a health care professional determine whether a particular therapeutic product's benefits to patients will outweigh any potential serious side effects or risks. In some embodiments, the clinical performance of the companion diagnostic is the ability of the test developed for a predictive biomarker (the companion diagnostic) to distinguish treatment responders from non-responders. Companion diagnostics can: (i) identify patients who are most likely to benefit from a particular therapeutic product; (ii) identify patients likely to be at increased risk for serious side effects as a result of treatment with a particular therapeutic product; and/or (iii) monitor response to treatment with a particular therapeutic product for the purpose of adjusting treatment to achieve improved safety or effectiveness. The clinical performance of the companion diagnostic not only directly affects the number of patients who are potentially eligible for treatment but also affects the net benefit enrichment achieved, as patients who are selected by the companion diagnostic and are non-responders also receive treatment, thereby reducing the observed average response. As such, if the diagnostic test is inaccurate, then the treatment decision based on that test may not be optimal.").
As per claim 5, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the notification is displayed to the healthcare professional who is responsible for rendering an actual diagnosis corresponding to the proposed next step diagnosis (Para [0009]-"The systems and methods described herein enable the skilled artisan to retrospectively analyze clinical trial data, i.e. patient outcome data and/or collected image data corresponding to patient biological samples, and decipher unexpected clinical trial outcomes, or guide medical professionals in identifying necessary changes before further clinical trials are conducted."; Para [0152]-"The apparatus may additionally contain a display in which appears at least one visually perceivable image of the tissue from the sequence of acquired images."; Para [0162]-"ln some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. ").
As per claim 6, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the input is representative of receipt of the digital image from a source, and wherein said applying is performed in response to said receiving, said determining is performed in response to said applying, and said causing is performed in response to said determining, such that the notification is produced in near real time with the generation of the digital image (Para [0065]-"The images or image data (used interchangeably herein) may be acquired using the imaging apparatus 12, such as in real-time. In some embodiments, the images are acquired from a microscope or other instrument capable of capturing image data of a specimen-bearing microscope slide, as noted herein. In some embodiments, the images are acquired using a 2D scanner, such as one capable of scanning image tiles, or a line scanner capable of scanning the image in a line-by-line manner, such as the BARNES DP 200 scanner. Alternatively, the Images may be images that have been previously acquired (e.g. scanned) and stored in a memory 201 (or, for that matter, retrieved from a server via network 20).").
As per claim 7, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the digital image is one of multiple digital images that are generated by an imaging device during the diagnostic session, and wherein the multiple images are received from the imaging device following a conclusion of the diagnostic session (Para [0065]-"The images or image data (used interchangeably herein) may be acquired using the imaging apparatus 12, such as in real-time. In some embodiments, the images are acquired from a microscope or other instrument capable of capturing image data of a specimen-bearing microscope slide, as noted herein. In some embodiments, the images are acquired using a 2D scanner, such as one capable of scanning image tiles, or a line scanner capable of scanning the image in a line-by-line manner, such as the BARNES DP 200 scanner. Alternatively, the images may be images that have been previously acquired (e.g. scanned) and stored in a memory 201 (or, for that matter, retrieved from a server via network 20)."; Para [0121]-"Once the optimal cutoff point is determined (step 330), patients may be stratified into diagnostic positive and diagnostic negative groups (step 340). This automatic diagnostic cut point and the resultant stratification can, in some embodiments, be compared against a manually selected diagnostic cut point and stratification (step 350). In some embodiments, the comparison may assist in determining whether the correct companion diagnostic was used, or if the threshold set in a clinical trial was too high or too low, i.e. the impact of the manually selected diagnostic cut point may be determined (step 360).").
As per claim 8, Barnes teaches the invention as claimed, see discussion of claim 1, and further teaches:
wherein the input is representative of receipt of the digital image from a source, wherein the operations further comprise: establishing that quality of the digital image is sufficient so as to be gradable through visual analysis, and wherein said applying is performed in response to said establishing (Para [0081]-''To accomplish this, along with color intensities in the input image, image gradient information is also used in radial symmetry voting and combined with an adaptive segmentation process to precisely detect and localize the cell nuclei. A "gradient'' as used herein is, for example, the intensity gradient of pixels calculated for a particular pixel by taking into consideration an intensity value gradient of a set of pixels surrounding said particular pixel. Each gradient may have a particular "orientation" relative to a coordinate system whose x- and y-axis are defined by two orthogonal edges of the digital image.").
As per claim 9, Barnes teaches the invention as claimed, see discussion of claim 8, and further teaches:
wherein said establishing involves the implementation of a rule, heuristic, or algorithm that considers how signal-to-noise ratio, blurriness, contrast, vignetting, or field of view compares to a threshold (Para [0074]-"This identification may be enabled by image analysis operations such as edge detection, etc. A tissue region mask may be used to remove the non-tissue background noise in the image, for example the non-tissue regions. In some embodiments, the generation of the tissue region mask comprises one or more of the following operations (but not limited to the following operations): computing the luminance of the low resolution input image, producing a luminance image, applying a standard deviation filter to the luminance image, producing a filtered luminance image, and applying a threshold to filtered luminance image, such that pixels with a luminance above a given threshold are set to one, and pixels below the threshold are set to zero, producing the tissue region mask.").
As per claim 10, Barnes teaches a method comprising:
acquiring, by a processor, multiple digital images that are generated for the purpose of remotely diagnosing multiple individuals (Para [0010]-"Within this in mind, in one aspect of the present disclosure is an automated method for deriving a diagnostic cut point, the diagnostic cut point used to identify a patient in a cohort population as positive or negative for a particular diagnostic test comprising: (a) computing one or more image feature metrics from a plurality of images derived from biological samples of patients in the cohort population,"; Para [0017]-"ln some embodiments, the diagnostic feature metric is derived through multivariate Cox modeling laking into account multiple computed image feature metrics. In some embodiments, the multiple computed image feature metrics for Cox modeling are predetermined (e.g. multiple expression scores as determined by a pathologist).", wherein each digital image of the multiple digital images is associated with a corresponding individual of multiple individuals (Para [0016]" computing one or more image feature metrics from a plurality of images derived from biological samples of patients in a cohort population");
applying, by the processor, a model to the multiple digital images, so as to produce multiple outputs, wherein each output of the multiple outputs is associated with a proposed next step diagnosis for a pathological condition for the corresponding individual (Para [0129J-"As a byproduct of the whole tumor image analysis, a large quantity of image feature metrics are computed for each patient tissue slide. If genomic analysis of the patient's tissue sample is also performed, similarly a set of molecular and genomic variants are output for each patient (tissue analysis data). A feature vector may be generated by combining the image feature metrics and the genomic features/tissue analysis from each patient along with certain clinical attributes. In some embodiments, from the generated feature vectors of all of the patients in a given cohort, a feature matrix may be constructed. In some embodiments, a statistical analysis of the feature matrix (e.g. principal component analysis, hierarchical clustering of features followed up with feature selection) will yield a condensed feature matrix for the cohort called a "cohort signature," i.e. a matrix that characterizes the tissue feature variation along with feature correlations. In some embodiments, the statistical analysis allows one to determine whether two given cohorts are similar or different. Specifically, the statistical analysis can facilitate a determine of how similar or how different two datasets are, e.g. how similar or how different Phase II and Phase Ill cohorts are."); and
stratifying, by the processor, the multiple individuals based on the multiple outputs (Para [0018]-"ln some embodiments, machine learning is used to determine those computed image feature metrics that most accurately may be used to stratify patient cohorts. In some embodiments, the image feature metrics that most accurately may be used to stratify patient cohorts are fed to a multivariate Cox model to provide the diagnostic feature metric.").
As per claim 11, Barnes teaches the invention as claimed, see discussion of claim 10, and further teaches:
wherein said stratifying comprises: assigning the multiple individuals among a first category, a second category, and a third category, wherein the first category includes those individuals, if any, for which an additional digital image of higher quality is needed, wherein the second category includes those individuals, if any, for which the corresponding outputs are representative of negative diagnoses, and wherein the third category includes those individuals, if any, for which the corresponding outputs are representative of positive diagnoses (Para [0010]-''Within this in mind, in one aspect of the present disclosure is an automated method for deriving a diagnostic cut point, the diagnostic cut point used to identify a patient in a cohort population as positive or negative for a particular diagnostic test comprising: (a) computing one or more image feature metrics from a plurality of images derived from biological samples of patients in the cohort population,"; Para [0114]-"and a classifier may be used to determine those image and clinical features which are believed to be important in a binary categorization of patients. In other embodiments, the input data (image data and patient data) may be derived from a drug study arm of a clinical trial (either Phase II and/or Phase Ill), and a classifier may be used to determine those image and clinical features which are believed to be important in a binary categorization of patients. For example, input images from a patient population may comprise 100 image feature metrics after image analysis. A machine learning algorithm may be utilized to determine which of those 100 image feature metrics best predict the patient population outcome and, by way of example, this may be 10 top image feature metrics from the 100 total image feature metrics.").
As per claim 12, Barnes teaches the invention as claimed, see discussion of claim 10, and further teaches:
wherein said stratifying comprises: producing, based on the multiple outputs, (i) a first list that includes a first subset of the multiple individuals and (ii) a second list that includes a second subset of the multiple individuals, wherein the first list is associated with a first type of healthcare professional, and wherein the second list is associated with a second type of healthcare professional (Para [0009]- ''The systems and methods described herein enable the skilled artisan to retrospectively analyze clinical trial data, i.e. patient outcome data and/or collected image data corresponding to patient biological samples, and decipher unexpected clinical trial outcomes, or guide medical professionals in identifying necessary changes before further clinical trials are conducted."; Para [0012]- "In some embodiments, the multiple image feature metrics or expression scores that are combined with the multivariate Cox model are pre-determined (e.g. determined by a pathologist or other medical profession, or based on diagnostic
guidelines).").
As per claim 13, Barnes teaches the invention as claimed, see discussion of claim 10, and further teaches:
wherein said stratifying comprises: producing, based on the multiple outputs, a ranked list of the multiple individuals, such that individuals who are determined to exhibit more evidence of the pathological condition are ranked higher than individuals who are determined to exhibit less evidence of the pathological condition (Para [0014J-"ln some embodiments, the statistical minimization method is a log rank statistic minimization. In some embodiments, the method further comprises stratifying the patients into diagnostic positive and diagnostic negative groups based on the determined cut point value. In some embodiments, the method further comprises generating Kaplan-Meier response curves. In some embodiments, the method further comprises calculating hazard ratios based on the generated Kaplan-Meier response curves. In some embodiments, the method further comprises comparing the determined cut point value to a manually selected diagnostic cutoff value.").
As per claim 14, Barnes teaches the invention as claimed, see discussion of claim 13, and further teaches:
causing, by the processor, presentation of the multiple individuals to at least one healthcare professional for further examination in order of the ranked list (Para [0009]- "The systems and methods described herein enable the skilled artisan to retrospectively analyze clinical trial data, i.e. patient outcome data and/or collected image data corresponding to patient biological samples, and decipher unexpected clinical trial outcomes, or guide medical professionals in identifying necessary changes before further clinical trials are conducted."; Para [0020]-"n some embodiments, the patient outcome data is a primary end point data. In some embodiments, the primary end point data is at least one of overall patient survival time, recurrence free survival, drug response, or pathological complete response. In some embodiments, the statistical minimization method is a log rank statistic minimization."; Para [0162]-"ln some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.").
As per claim 15, Barnes teaches the invention as claimed, see discussion of claim 10, and further teaches:
wherein said acquiring, said applying, and said stratifying are performed in near real time with the generation of the multiple digital images, such that the multiple individuals are promptly ranked for examination purposes based on severity of the pathological condition (Para [0012]-"ln some embodiments, the multiple image feature metrics or expression scores are combined using a proportional hazard model. In some embodiments, the proportional hazard model is a multivariate Cox model. In some embodiments, the multiple image feature metrics or expression scores that are combined with the multivariate Cox model are pre-determined (e.g. determined by a pathologist or other medical profession, or based on diagnostic guidelines). "; Para (0014]" ln some embodiments, the method further comprises generating Kaplan-Meier response curves. ln some embodiments, the method further comprises calculating hazard ratios based on the generated Kaplan-Meier response curves. In some embodiments, the method further comprises comparing the determined cut point value to a manually selected diagnostic cutoff value."; Para [0065]-"The images or image data (used interchangeably herein) may be acquired using the imaging apparatus 12, such as in real-time. In some embodiments, the images are acquired from a microscope or other instrument capable of capturing image data of a specimen-bearing microscope slide, as noted herein.).
Claim(s) 1, 10, and 16-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication 2015/0265144 Burlina.
Regarding claim 1, Burlina teaches a non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising (Abstract; para [0010]computer-readable medium for detecting, and classifying severity of, a retinal disease using retinal images according to an embodiment of the current invention includes non-transitory computer-executable code which, when executed by a computer, causes the computer to):
receiving input indicative of a request to determine whether an individual whose eye is imaged as part of a diagnostic session should be referred for treatment of a pathological condition (para [0010], [0022]. [0061]- one of receive, retrieve or generate reference data that includes information concerning occurrences of key image features for each of a plurality of retinal disease and disease severity conditions; an application of the current invention can be to implement these algorithms in a public monitoring or screening system that is convenient and easily accessible to the general public; receive a retinal image of an individual; system 100 can also include an input device 110 which can allow a patient to input data into the system.);
applying a model to a digital image of the eye, so as to produce an output that is associated with a proposed next step diagnosis for the pathological condition (para [0021]-[0022], [0044]- analyze fundus images of an individual and quickly provide results including a grade of AMO severity and, if necessary, a recommendation to see an ophthalmologist for further evaluation, while avoiding false positive referrals; determining at least one of a likelihood of a presence of a retinal disease or a likelihood of developing a retinal disease based on a comparison of the number of occurrences of each of the key image features in the retinal image of the individual to the reference data; automated retinal image analysis (ARIA) algorithms. While ARIA algorithms for diabetic retinopathy or glaucoma are showing promise9, less progress, in the opinion of the authors, has been made in the area of AMO);
determining, based on the output, that the digital image includes evidence of the pathological condition (para [0022)analyze fundus images of an individual and quickly provide results including a grade of AMD severity and, if necessary, a recommendation to see an ophthalmologist for further evaluation, while avoiding false positive referrals); and
causing display of a notification that specifies the individual requires the proposed next step diagnosis by a healthcare professional (para (0061]- system 100 can also include also include one or more data output devices such as a video screen 112 (e.g., but not limited to, an LCD display). In some embodiments, the video screen 112 can provide, for example, an echo of the input from 110, patient instructions and/or marketing.).
As per claim 10, Burlina teaches a method comprising (Abstract):
acquiring, by a processor, multiple digital images that are generated for the purpose of remotely diagnosing multiple individuals, wherein each digital image of the multiple digital images is associated with a corresponding individual of multiple individuals (para [0010], [0022), [0061]- an application of the current invention can be to implement these algorithms in a public monitoring or screening system that is convenient and easily accessible to the general public; receive a retinal image of an individual; system 100 can also include an input device 110 which can allow a patient to input data into the system.);
applying, by the processor, a model to the multiple digital images, so as to produce multiple outputs, wherein each output of the multiple outputs is associated with a proposed next step diagnosis for a pathological condition for the corresponding individual (para [0021]-[0022], [0044]- analyze fundus images of an individual and quickly provide results including a grade of AMO severity and, if necessary, a recommendation to see an ophthalmologist for further evaluation, while avoiding false positive referrals; determining at least one of a likelihood of a presence of a retinal disease or a likelihood of developing a retinal disease based on a comparison of the number of occurrences of each of the key image features in the retinal image of the individual to the reference data; automated retinal
image analysis (ARIA) algorithms. While ARIA algorithms for diabetic retinopathy or glaucoma are showing promise9, less progress, in the opinion of the authors, has been made in the area of AMO); and
stratifying, by the processor, the multiple individuals based on the multiple outputs (para [0022]-[0024]- classifying AMO patients can include automatically finding drusen in fundus images (which is the aim of most of the above cited studies) and then using this lo detect and classify the severity of AMO).
As per claim 16, Burlina teaches a method comprising (Abstract - detecting, and classifying severity of, a retinal disease using retinal images):
acquiring a digital image that is generated as part of a diagnostic session in which an eye of an individual is imaged (para [0010], [0022], [0061]- one of receive, retrieve or generate reference data that includes information concerning occurrences of key image features for each of a plurality of retinal disease and disease severity conditions; an application of the current invention can be to implement these algorithms in a public monitoring or screening system that is convenient and easily accessible to the general public; receive a retinal image of an individual; system 100 can also include an input device 110 which can allow a patient to input data into the system.);
applying a model to the digital image to produce an output that indicates whether pathological features that are indicative of diabetic retinopathy are present in the digital image (para [0021]-[0022], [0037], [0044]- analyze fundus images of an individual and quickly provide results including a grade of AMD severity and, if necessary, a recommendation to see an ophthalmologist for further evaluation, while avoiding false positive referrals; determining at least one of a likelihood of a presence of a retinal disease or a likelihood of developing a retinal disease based on a comparison of the number of occurrences of each of the key image features in the retinal image of the individual to the reference data; automated retinal image analysis (ARIA) algorithms. While ARIA algorithms for diabetic retinopathy or glaucoma are showing promise9, less progress, in the opinion of the authors, has been made in the area of AMD; Regional histograms are then concatenated back into a single large histogram for the entire image. This concatenated vector forms the final 'feature vector' used for classification); determining, based on the output, that the digital image includes at least one pathological feature that is indicative of diabetic retinopathy (para [0022], [0039], [0044], [0050] - random forest algorithm uses the consensus of a large number of weak (only slightly better than chance) binary decision trees to classify the testing images into different severity classes; analyze fundus images of an individual and quickly provide results including a grade of AMO severity and, if necessary, a recommendation
to see an ophthalmologist for further evaluation, while avoiding false positive referrals; determining at least one of a likelihood of a presence of a retinal disease or a likelihood of developing a retinal disease based on a comparison of the number of occurrences of each of the key image features in the retinal image of the individual to the reference data; following is a list of retinal diseases to which this
method could apply .... Diabetic retinopathy); and
causing display of a notification that specifies further examination of the digital image by a healthcare professional is needed (para [0022], [0061] - analyze fundus images of an individual and quickly provide results including a grade of AMD severity and, if necessary, a recommendation to see an ophthalmologist for further evaluation, while avoiding false positive referrals; system 100 can filso include also include one or more data output devices such as a video screen 112 (e.g., but not limited to, an LCD display). In some embodiments, the video screen 112 can provide, for example, an echo of the input from 110, patient instructions and/or marketing.).
As per claim 17, Burlina teaches the invention as claimed, see discussion of claim 16, and further teaches:
wherein the model is a binary classification model that indicates, based on analysis of the digital image, whether the eye is exhibiting evidence of diabetic retinopathy (para [0039], [0062]- binary decision trees to classify the testing images into different severity classes.29 For the two class problems the random forest consisted of 1,000 decision trees; determining at least one of a likelihood of a presence of a disease).
As per claim 18, Burlina teaches the invention as claimed, see discussion of claim 16, and further teaches:
wherein the model is a non-binary model that specifies, based on analysis of the digital image, one of multiple severity classifications to which to assign the individual (para [0021], [0023]-[0024], [0045], [0058]- detect and classify the severity; number of severity levels, such as four).
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