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.
Claim(s) 30-56 and 88-92 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The claim(s) recite(s) a method for determining an intervention based on historical trend generated based on analyzing images of stool. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 30 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
YES
Claim(s) 30 is directed to a method.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES.
The claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 30 comprises a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding Claim(s) 30: the method recites the steps (functions) of:
b. determining one or more characteristics associated with the stool based on the plurality of images of the stool (mental process including observation and evaluation, and can be done mentally in the human mind);
c. generating a historical trend of one or more characteristics based on the plurality of images (mental process including observation and evaluation, and can be done mentally in the human mind and/or using pen and paper);
d. identifying, from the graph, one or more changes with the health or disease state (mental process including observation and evaluation, and can be done mentally in the human mind);
e. determining an intervention, based at least in part on the one or more changes of the health or disease state, to one or more of alleviate or reduce a risk of the subject experiencing at least one symptom of the health or disease state (mental process including observation and evaluation, and can be done mentally in the human mind).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could a gastroenterologist can look at images and stool and determine whether a patient requires a specific intervention based on the changes of stool and health condition over time, either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a device/in a device (e.g. processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO.
The claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim(s) 30 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Claim(s) 30 recite(s) the further limitations of:
receiving a plurality of images of stool corresponding to a plurality of bowel movements over time (insignificant pre-solution extra activity of gathering data);
displaying, via a user device, the historical trend to the subject as a graph of changes of the one or more characteristics over time (insignificant extra solution activity of representing results of the analysis and the user device is a generic computer or computer component)
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
The claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim(s) 30 does not recite any additional elements that are not well-understood, routine or conventional. The use of a computer to perform receiving, extracting, determining, generating, and determining, as claimed in Claim(s) 30 is a routine, well-understood and conventional process that is performed by computers.
Thus, since Claim(s) 30 : (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claim(s) 30 is not eligible subject matter under 35 U.S.C 101.
Regarding claim 31-56 and 88-92: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The additional limitation(s) merely specify the type of information analyzed (stool shape, texture, etc.), the type of recommendation or correlation (diet, lifestyle, medication), generic computer components (processor, computing device, mobile device, camera, display, communication module), generic machine learning model, and presentation of information. These limitations do not integrate the abstract idea into a practical application and do not amount to significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 30-56, 88-89, and 92 are rejected under 35 U.S.C. 103 as being unpatentable over Karlin (US 20200395124) in view of Zedayko (US 20210151137), and further in view of Paineau (US 20210265059).
Regarding claim 30:
Karlin discloses: a method managing a health or disease state of a subject (abstract: “…A related method of long-term monitoring of patient gastrointestinal function, using one or more signal processing tools (e.g. machine learning algorithms) for automatically interpreting patient stool data”, the method comprising:
a. receiving a plurality of images of stool corresponding to a plurality of bowel movements over time (¶ [0025] “FIG. 1A shows a flowchart for an exemplary method that includes the steps of: creating a digital image of a stool sample (step 101)” ; ¶ [0027] “…In this aspect, date and time stamping of a data collection event further enables data to be collected, annotated, and stored in real-time, per event, so that more accurate and therefore improved long-term monitoring (e.g. over months or years, as in longitudinal studies) may be achieved.”; ¶ [0065] “…obtaining a digital image of a stool sample (step 601)”);
b. determining one or more characteristics associated with the stool based on the plurality of images of the stool (¶ [0065] “…classifying the digital image to obtain a classified digital image using at least one machine learning algorithm (step 602)….”; ¶ [0098] “automated classification of stool type by color, texture, consistency, float, and size”)
c. generating a historical trend of the one or more characteristics based on the plurality of images (¶ [0065] “…the method of long-term monitoring of patient gastrointestinal function also includes annotating the classified digital image with patient-assessed information (step 603), to obtain at least one subjective annotation (for example, pain and/or urgency) associated with the classified digital image of the stool sample”; ¶ [0098] “ Automated classification of stool type by color, texture, consistency, float, and size, and associating those classifications with subjective assessments such as pain and urgency as described herein, enables rapid visualization of all analyzed data on a timeline, for example, so as to derive frequency of a patient's various bowel movements.”; ¶ [0027] “…In this aspect, date and time stamping of a data collection event further enables data to be collected, annotated, and stored in real-time, per event, so that more accurate and therefore improved long-term monitoring (e.g. over months or years, as in longitudinal studies) may be achieved.”);
While Karlin teaches generating historical data, long term monitoring, and rapid visualization of analyzed data on a timeline, which includes a graph and enables both patients and clinicians to track changes and other clinical measures in ¶¶ [0065], [0098], and [0027].
Nonetheless, in the same field of endeavor, Paineau explicitly teaches: d. displaying, via a user device, the historical trend to the subject as a graph of changes of the one or more characteristics over time (Paineau teaches a portable device stool module in which the application displays a stool analysis score, stores information related to the score, stores the captured image, and with the plurality of entries stored, development graphs may be created to follow changes in stool consistency and determine stool frequency (Paineau ¶ [0058); also see ¶¶ [0017] and [0050]);
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karlin to incorporate the teachings of Paineau by including: displaying, via a user device, the historical trend to the subject as a graph of changes of the one or more characteristics over time in order to provide actionable health management information to the subject and to present longitudinal stool characteristics changes in a user readable graphical form. The combination merely applies known user facing stool graphing and health recommendation functionality to Karlin’s automated stool monitoring system, yielding predicable results.
e. identifying, from the graph, one or more changes with the health or disease state (Karlin teaches in¶ [0098] “Automated classification of stool type by color, texture, consistency, float, and size, and associating those classifications with subjective assessments such as pain and urgency as described herein, enables rapid visualization of all analyzed data on a timeline, for example, so as to derive frequency of a patient's various bowel movements. This aspect further enables patients, researchers and clinicians to, for example:”; ¶ [0099] “1. Map bowel movements in a patient profile to a basic gastroenterological diagnostic tree, and to note physiological changes associated with relapse and/or warning signs in gastrointestinal (GI) diseases”; ¶ [0100] “2. Analyze in-patient “delta” and “between-cohorts” differences”; ¶ [0084] “…clinical decision making rules 976 of clinical diagnostic tree 972 are configured to build up a process of disease and/or condition identification (i.e. what disease), and disease and/or condition progression (i.e. how severe, in what stage the condition is)”.) Paineau likewise teaches in ¶ [0058] “development graphs may be created in order to allow to follow the changes in the stool consistency, and also in order to determine the frequency in which the stool is provided, , which may provide additional information related to for example diarrhea or constipation.”);
While Karlin discloses in ¶ [0085] “…the set of adverse clinical events 934 are stored in third database 932 and structured by (i) condition or disease, (ii) treatment, and (iii) clinical adverse event typology. Expected and/or requested immediate clinical interventions are associated with all stored adverse event type-disease-treatment trichotomies.”
Karlin does not specifically teach: and e. determining an intervention, based at least in part on the one or more changes of the health or disease state, to one or more of alleviate or reduce a risk of the subject experiencing at least one symptom of the health or disease state.
However, in a related field, Zedayko teaches: and e. determining an intervention, based at least in part on the one or more changes of the health or disease state, to one or more of alleviate or reduce a risk of the subject experiencing at least one symptom of the health or disease state (¶ [0010] “providing a treatment for the animal in view of the health characteristic. The treatment may include a customized health plan for the animal. The customized health plan may include one or more of a behavioral change and a dietary change. The customized health plan may include a recommendation regarding one or more of diet, sleep, exercise, and an activity. The treatment may include one or more of a food, a supplement, and a medicine. The treatment may include a personalized dietary supplement for the animal…The method may further include providing a treatment in view of a combination of the health characteristic and the second health characteristic. The second health characteristic may include a classification on the Bristol stool scale…The method may further include analyzing the output of the model in view of a reference database to determine one or more of the health characteristic and a treatment. The reference database may be a historical database including data from analyses of other biological samples. At least one of the other biological samples may be from the animal; ¶ [0016] “…The biological sample may be stool, where the personalized product includes a personalized dietary product.”; ¶ [0046] “…Examples of animals include, but are not limited to, companion animals (e.g., dogs, cats, and the like), primates, humans, lizards, and other zoological creatures”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karlin and Paineau to incorporate the teachings of Zedayko by including: determining an intervention, based at least in part on the one or more changes of the health or disease state, to one or more of alleviate or reduce a risk of the subject experiencing at least one symptom of the health or disease state in order to take the appropriate action based on the patient’s condition and improve health assessment. Karlin already teaches adverse clinical events structured by condition/disease, treatment, and clinical adverse event typology, with expected immediate clinical intervention associated with adverse event in ¶ [0085], and Zedayko teaches providing a treatment in view of a health characteristic, including customized health plan, behavioral change, diet change and in view of historical database. The combination is merely combining prior art elements according to known methods to yield predictable results.
Regarding claim 31:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: wherein the one or more characteristics comprises one or more of a shape, texture, consistency, fragmentation, fuzziness, or volume of the stool (¶ [0098] “Automated classification of stool type by color, texture, consistency, float, and size, and associating those classifications”; ¶ [0027] “…the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”).
Regarding claim 32:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: wherein identifying one or more changes with the health or disease state comprises identifying one or more medical conditions, illnesses, or diseases for the subject (¶ [0098] “Automated classification of stool type by color, texture, consistency, float, and size, and associating those classifications with subjective assessments such as pain and urgency as described herein, enables rapid visualization of all analyzed data on a timeline, for example, so as to derive frequency of a patient's various bowel movements. This aspect further enables patients, researchers and clinicians to, for example:”; ¶ [0099] “1. Map bowel movements in a patient profile to a basic gastroenterological diagnostic tree, and to note physiological changes associated with relapse and/or warning signs in gastrointestinal (GI) diseases”; ¶ [0100] “2. Analyze in-patient “delta” and “between-cohorts” differences”; ¶ [0084] “…clinical decision making rules 976 of clinical diagnostic tree 972 are configured to build up a process of disease and/or condition identification (i.e. what disease), and disease and/or condition progression (i.e. how severe, in what stage the condition is)”).
Regarding claim 33:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 32 as applied above.
Karlin further discloses: wherein the one or more medical conditions, illnesses, or diseases comprises Irritable Bowel Syndrome, Crohn's Disease, Ulcerative Colitis, Hepatic Encephalopathy, or a combination thereof (¶ [0103] “The classification models derived from the method and system described herein may furthermore be used in (but not only restricted to) diagnostic efforts in following disease areas such as Crohn's disease, ulcerative colitis, irritable bowel syndrome, inflammatory bowel disease, endometriosis, and colon cancer.”).
Regarding claims 34 and 35:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Zedayko further teaches: further comprising identifying one or more correlations between one or more subject conditions and the health or disease state; wherein the one or more subject conditions comprise a diet intake, one or more lifestyle conditions, one or more medications, or a combination thereof (¶ [0010] “…The method may further include receiving metadata associated with the image. The metadata may include a questionnaire response related to one or more of a health, a behavior, a current diet, a supplement, a medication…The biological sample may include one or more of phlegm, snot, urine, vomit, bile, blood, vaginal discharge, semen, and other biological discharge. “; ¶ [0051] “…current techniques for assessing gastrointestinal health may include a user providing a stool specimen and/or questionnaire responses related to health, behavior, current diet, and other ethnographic information, which are then analyzed and compared to a reference database in order to provide a personalized health plan”; ¶ [0179] “The attributes and/or characteristics of an animal that are associated with the biological sample may include the presence or absence of a substance (e.g., one or more of a microbiome characteristic and a metabolome characteristic), a health characteristic, a score (e.g., a mucus health score), and the like, including combinations thereof. By way of example, such attributes and/or characteristics may include, or otherwise assist in the identification of, one or more of an indication of diet (e.g., carbohydrate intake, fresh food intake, protein intake, fat intake, metabolism thereof, and so on), an indication of metabolism, an indication of weight of the animal, an indication of a health condition or sickness (e.g., cancer, irritable bowel syndrome (IBS), obesity, and so on), pathogen presence or absence, parasite presence or absence, cortisol levels in the host animal, and the like “; ¶ [0181] “…further possible correlations will now be described in the context of a stool sample—i.e., correlations from image features and health-related attributes and/or characteristics such as the presence or absence of metabolome subsystems. The eccentricity can have a correlation to the presence or absence of one or more metabolites indicating carbohydrate intake, virulence, disease, and defense”).
Regarding claim 36:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: further comprising determining an effectiveness of a medication based on a change in the health or disease state between the plurality of images (¶ [0102] “4. Monitor dosing (in)tolerability effects, for drugs with assumed interaction with GI system and functions.”; ¶ [0104] “…image-processing-based stool classification models, which would eliminate some or all of the following problems associated with monitoring adverse GI events and intolerability of drug dosing during human clinical trial phases of drug development (i.e. outside of clinical diagnostic process):”; ¶ [0105] “1. Lack of real life, real-time continual assessment of dosing effects and (in)tolerability, using instead sequential reporting (e.g. PRO once every 24 hours, or after an adverse event or status change)”).
Regarding claim 37:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Zedayko further discloses: further comprising providing an intervention recommendation based on the health or disease state (¶ [0007] “…generating more accurate results and more efficient recommendations or treatment plans “; ¶ [0104] “FIG. 4 is a flow diagram illustrating a dynamic recommendation engine with a feedback loop”; ¶ [0173] “…The customized health plan may also or instead include a recommendation regarding one or more of diet, sleep, exercise, an activity, enrichment, a lifestyle change, and the like.”).
Regarding claim 38:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 37 as applied above.
Zedayko further discloses: wherein the recommendation comprises a change to at least one of i) the subject's diet, ii) one or more lifestyle conditions, or iii) one or more medications being received by the subject (¶ [0173] “…The customized health plan may also or instead include a recommendation regarding one or more of diet, sleep, exercise, an activity, enrichment, a lifestyle change, and the like.”).
Regarding claim 39:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: wherein one or more of determining the one or more characteristics, generating a historical trend, identifying one or more changes with the health or disease state, or determining an intervention of comprises using a machine learning algorithm (¶ [0065] “…classifying the digital image to obtain a classified digital image using at least one machine learning algorithm (step 602)….”); ¶ [0072] “…A convolutional neural network (CNN) with multiple outputs (for example, color, shape, size, float, pain, urgency) may be used. The specific CNN method used may depend upon the specific application of the system described herein”; ¶ [0076] “…FIG. 8 adds a machine learning algorithm component to the system of FIG. 3”).
Regarding claim 40:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: wherein the machine learning algorithm uses a trained data set in operative communication with a processor for one or more of determining one or more characteristics, generating a historical trend, identifying one or more changes with the health or disease state, or determining an intervention (¶ [0066] “FIG. 6, in one aspect of the method, use of the BSS method may be used to obtain a classified digital image using a machine learning algorithm comprising the following machine-learning classifiers (step 602): stool ID from noise model; stool color classifier model; stool size classifier model; stool texture classifier model; and stool float classifier model. In addition, a frequency and cadence classifier model may also be used.”; ¶ [0076] “FIG. 8 is a data analysis flow diagram with trained model for one aspect of the method described herein”).
Regarding claim 41:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 40 as applied above.
Karlin further discloses: wherein the trained data set comprises a plurality of past images of stool correlated with one or more characteristics (¶ [0068] “…Image data 702 comprise one or more digital images of stool saved in, for example, cloud-based storage… Annotations 703 comprise patient-assessed information stored in the form of annotations -- for example, annotations on shape (281), color (283), size (285), float (287), urgency (289) and pain (290) of a stool event… Join And Clean Data Process 705. This is a process that connects annotations 703 to associated medical digital image 702 (i.e., patient stool digital images)…. This is a process that splits clinical data 316 into two datasets: one to train classification model 700 (training dataset 716) and one to test classification model 700 (testing dataset 717), allowing for evaluation of the classification model”).
Regarding claim 42:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 41 as applied above.
Karlin further discloses: wherein the processor is a part of a computing device (¶ [0081] “…System 900 also includes server 310 that includes processor 322”).
Regarding claim 43:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 42 as applied above.
Karlin further discloses: wherein the computing device comprises at least one of a mobile device, a desktop, a laptop, or a remote computing server (¶ [0081] “…System 900 includes a mobile computing device 120 (FIG. 1B)”).
Regarding claim 44:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 43 as applied above.
Karlin further discloses: wherein the computing device comprises the mobile device, and wherein the mobile device comprises a smart phone, a tablet, a smartwatch, or any combination thereof (FIG. 1B, ¶ [0026] “…Mobile device 120 may be any form of mobile computing device, such as, for example (and without limitation), a portable computer, a cellular telephone, a smart phone, a tablet computer, or a portable digital assistant.”; ¶ [0081] “…System 900 includes a mobile computing device 120 (FIG. 1B)”).
Regarding claim 45:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
Karlin further discloses: wherein receiving the plurality of images of the stool comprises using a camera in operative communication with the processor and configured to capture the plurality of images (FIG. 1B, ¶ [0026] “…mobile device 120 comprises a camera 121, a processor 122… a camera operable to capture still photos and video feed”).
Regarding claim 46:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 42 as applied above.
Karlin further discloses: wherein the computing device further comprises a camera (FIG. 1B, ¶ [0026] “…mobile device 120 comprises a camera 121, a processor 122… a camera operable to capture still photos and video feed”).
Regarding claim 47:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 42 as applied above.
Karlin further discloses: wherein the computing device is in operative communication with a display to output one or more of the historical trend, the identified one or more changes with the health or disease state, or the intervention (FIG. 1B, ¶ [0026] “…the mobile device may have a display 135”; ¶ [0029] “…provide other information 259 to a user via touchscreen 125 (or display 135) of mobile device 120”; ¶ [0031] “…Automated editing of color in the digital image, using an automated color-editing process 274 (which may be, for example, a color-inversion process 274a) creates a color-edited image 276, which is displayed on the mobile device screen (e.g., touchscreen 125; or display 135). Next, a set of annotations associated with the digital image 272 may be created from stool assessments that may be entered in real-time using the color-edited image 276 and a plurality of stool scale classifications; for example, assessments may relate to shape (280a, 280b, 280c, 280d, 280e, 280f, 280g; FIG. 2P), color (282a, 282b, 282c, 282d, 282e, 282f, 282g, 282h; FIG. Q), size (284a, 284b, 284c; FIG. 2R), and float (286a, 286b, 286c; FIG. 2S), as well as other, more subjective information, such as urgency (288a) and pain (288b) (FIG. 2T). While the figures show use of the BSS method for classifying assessed aspects of the stool sample, other stool scale classifications may be used.”).
Regarding claim 48:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 48 as applied above.
Karlin further discloses: wherein the display is in operative communication with the camera, such that the display provides guiding features to capture the plurality of images (FIG 2G shows the display provides guiding features to capture images).
Regarding claim 49:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 48as applied above.
Karlin further discloses: wherein the guiding features comprises a shape of a toilet seat defining a central area, such that the plurality of images of the stool is located within the central area when the plurality of images are captured (FIG 2G shows a shape of a toilet and a button directing the user to capture an image of their stool).
Regarding claim 50:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
further comprising assessing the stool via one or more of a score or rating relating to each characteristic of the one or more characteristics (Karlin in ¶ [0027] “the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”; and Zedayko ¶ [0080] “…The output of color model may also or instead include a prediction for a Bristol stool score or the like (when the biological sample 103 is a stool sample), which can be used as a health characteristic 152 for formulating a treatment 154 and/or a health assessment 150”).
Regarding claim 51:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 50 as applied above.
wherein the one or more characteristics comprises consistency, wherein the one or more of the score or the rating for the consistency corresponds to a liquid to solid scale of the stool, wherein one end of the scale corresponds to a fully liquid stool, and another end of the scale corresponds to a fully solid stool (Karlin in FIG. 2P, ¶ [0081] “…The mucus health score may thus include a combination and/or scaling of other scores such as a grade corresponding to an amount of mucus present, a color score, an opacity score, a texture score, a viscosity score, a consistency score, a mass score, and so on”; ¶ [0203] “…the labels 1012 include a consistency of the biological specimen and a color of the biological specimen—where, more particularly, the stool sample in this example had a “loose” consistency and a “yellow-ish” color. And, by way of example, further information may be provided with the labels 1012. For example, a label 1012 associated with consistency may further include a Bristol Stool Score (BSS), e.g., where a BSS of 6 is provided by way of example in the figure.”).
Regarding claim 52:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 50 as applied above.
wherein the one or more characteristics comprises fragmentation, wherein the one or more of the score or the rating for the fragmentation corresponds to a degree relating to a number of pieces present in the stool, wherein one end of, wherein one end of a scale corresponds to a single stool piece, and another end of the scale corresponds to a large number of stool pieces (Karlin in FIG. 2P and ¶ [0027] “the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”; ¶ [0029] “…A second assessment screen 280 is used to obtain user entry of a stool shape assessment selected from a set of proposed shape assessments—280a, 280b, 280c, 280d, 280e, 280f and 280g—which is then used to create a shape annotation 281 regarding shape of stool associated with digital image 272. See FIG. 2P”; and Zedayko ¶ [0080] “…The output of color model may also or instead include a prediction for a Bristol stool score or the like (when the biological sample 103 is a stool sample), which can be used as a health characteristic 152 for formulating a treatment 154 and/or a health assessment 150”; ¶ [0010] “…One or more features of the stool sample may include at least one of a color, a texture, a number of binaries, an area, a perimeter, a circularity, a mass, an eccentricity, a major axis, a minor axis”).
Regarding claim 53:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 50 as applied above.
wherein the one or more characteristics comprises fuzziness, wherein the one or more of the score or the rating for the fuzziness corresponds to a degree of a clear boundary existing between the stool and a background in the image, wherein one end of a scale corresponds to a clear distinguishable or substantially distinguishable boundary, and another end of the scale corresponds to an indistinguishable or substantially indistinguishable boundary (Karlin in FIG. 2P and ¶ [0027] “the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”; ¶ [0029] “…A second assessment screen 280 is used to obtain user entry of a stool shape assessment selected from a set of proposed shape assessments—280a, 280b, 280c, 280d, 280e, 280f and 280g—which is then used to create a shape annotation 281 regarding shape of stool associated with digital image 272. See FIG. 2P”; and Zedayko ¶ [0080] “…The output of color model may also or instead include a prediction for a Bristol stool score or the like (when the biological sample 103 is a stool sample), which can be used as a health characteristic 152 for formulating a treatment 154 and/or a health assessment 150”; ¶ [0010] “…One or more features of the stool sample may include at least one of a color, a texture, a number of binaries, an area, a perimeter, a circularity, a mass, an eccentricity, a major axis, a minor axis”).
Regarding claim 54:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 50 as applied above.
wherein the one or more characteristics comprises volume, wherein the one or more of the score or the rating for the volume corresponds to a size of the stool, wherein one end of a scale corresponds to a small size, and another end of the scale corresponds to a large size stool (Karlin in FIG. 2P, 2R, and ¶ [0027] “the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”; ¶ [0029] “…A second assessment screen 280 is used to obtain user entry of a stool shape assessment selected from a set of proposed shape assessments—280a, 280b, 280c, 280d, 280e, 280f and 280g—which is then used to create a shape annotation 281 regarding shape of stool associated with digital image 272. See FIG. 2P”; and Zedayko ¶ [0080] “…The output of color model may also or instead include a prediction for a Bristol stool score or the like (when the biological sample 103 is a stool sample), which can be used as a health characteristic 152 for formulating a treatment 154 and/or a health assessment 150”; ¶ [0010] “…One or more features of the stool sample may include at least one of a color, a texture, a number of binaries, an area, a perimeter, a circularity, a mass, an eccentricity, a major axis, a minor axis”).
Regarding claim 55:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 50 as applied above.
Karlin further discloses: further comprising using a processor to perform an operation comprising one or more of i) sending the stool assessment to a healthcare provider, or ii) receiving input from the healthcare provider (FIG 9B, ¶ [0086] “…Metadata 980 and patient information 990 may then be used for clinical application and, for example, via a data visualization engine.”).; ¶ [0098] “Automated classification of stool type by color, texture, consistency, float, and size, and associating those classifications with subjective assessments such as pain and urgency as described herein, enables rapid visualization of all analyzed data on a timeline, for example, so as to derive frequency of a patient's various bowel movements. This aspect further enables patients, researchers and clinicians to, for example: ¶ [0099] “1. Map bowel movements in a patient profile to a basic gastroenterological diagnostic tree, and to note physiological changes associated with relapse and/or warning signs in gastrointestinal (GI) diseases”; ¶ [0100] “2. Analyze in-patient “delta” and “between-cohorts” differences; ¶ [0101] “3. Add contextual value to other clinical measures, such as electronic PROs, medical history, socio-demographics, quality of life (mood, depression, stress, fatigue), and BMI”; and ¶ [0102] “4. Monitor dosing (in)tolerability effects, for drugs with assumed interaction with GI system and functions.”; and Zedayko in ¶ [0060] “The user 101 may also or instead include a medical professional (e.g., a doctor, a veterinarian, a nurse, and the like), a researcher, a scientist, a laboratory technician, a student, and so on”)).
Regarding claim 56:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 55 as applied above.
Karlin further discloses: wherein the processor is in operative communication with the healthcare provider via a communication module (¶ [0087] “…For example, FIG. 10A illustrates an example of relationships between mobile device 120 and servers 310, 910, and 920, which are described above (and collectively depicted in FIGS. 1B, 3, 9A and 9B, respectively) as individual components of system 900 interconnected via a network 1010. In general, however, any number of mobile devices and servers may be included—for example, the functionality of servers 310, 910 and 920 may be performed by one or more servers.”; and Zedayko in ¶ [0060] “The user 101 may also or instead include a medical professional (e.g., a doctor, a veterinarian, a nurse, and the like), a researcher, a scientist, a laboratory technician, a student, and so on”).
Regarding claim 88:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
wherein the historical trend further comprises a stool diary (Paineau ¶ [0058] teaches a plurality of entries stored, development graphs may be created in order to allow to follow the changes in the stool consistency, and also in order to determine the frequency in which the stool is provided and Karlin ¶ [0029] teaches summary listing of stool events).
Regarding claim 89:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 88 as applied above.
further comprising converting the stool diary to the graph (Paineau ¶ [0058] teaches a plurality of entries stored, development graphs may be created in order to allow to follow the changes in the stool consistency, and also in order to determine the frequency in which the stool is provided).
Regarding claim 92:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 88 as applied above.
wherein the stool diary comprises a) a score for at least some of the one or more characteristics for each bowel movement; and b) one or more images of the stool associated with the each bowel movement (Karlin in FIG. 2P and ¶ [0027] “the Bristol Stool Chart/Bristol Stool Scale (referred to herein “BSS”) may be used to create a set of annotations associated with a digital image of a stool sample captured in real-time”; ¶ [0029] “…A second assessment screen 280 is used to obtain user entry of a stool shape assessment selected from a set of proposed shape assessments—280a, 280b, 280c, 280d, 280e, 280f and 280g—which is then used to create a shape annotation 281 regarding shape of stool associated with digital image 272. See FIG. 2P”; and Zedayko ¶ [0080] “…The output of color model may also or instead include a prediction for a Bristol stool score or the like (when the biological sample 103 is a stool sample), which can be used as a health characteristic 152 for formulating a treatment 154 and/or a health assessment 150”; ¶ [0010] “…One or more features of the stool sample may include at least one of a color, a texture, a number of binaries, an area, a perimeter, a circularity, a mass, an eccentricity, a major axis, a minor axis”. Also see Paineau FIG. 3, ¶¶ [0017] and [0050]).
Claim(s) 90 is rejected under 35 U.S.C. 103 as being unpatentable over Karlin (US 20200395124) in view of Zedayko (US 20210151137), and further in view of Paineau (US 20210265059) and Mitteldorf (US 20180046772).
Regarding claim 90:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
While Karlin ¶¶ [0029] – [0031] teach subject severity assessments for urgency and pain associated with a stool event and digital image. Karlin ¶ [0098] teaches timeline visualization of all analyzed data including subjective assessments such as pain and urgency. And Paineau ¶ [0058] teaches a plurality of entries stored, development graphs may be created in order to allow to follow the changes in the stool consistency, and also in order to determine the frequency in which the stool is provided, which may provide additional information related to for example diarrhea or constipation
Karlin in view of Paineau and Zedayko does not explicitly teach: wherein the graph shows a change in symptom score over time.
However, in a related filed, Mitteldorf teaches: wherein the graph shows a change in symptom score over time (abstract: “generating, by the processing circuitry, graph data based on the symptom data and the time data; generating, by the processing circuitry, a graph data report based on the graph data; and providing, by the processing circuitry, the graph data report to one or more servers via a network”; ¶ [0063] “…The symptom data input interface 914 may include indications of one or more symptoms, and allow the user to specify a severity associated with each symptom (e.g., ranging from none, mild, moderate, severe, and very severe).“; ¶ [0070] “…the graph data may include a step graph for each symptom where severity of the symptom is plotted against the time data“; FIG.3 and ¶ [0074] “one or more servers may be configured to receive the graph data over time from the mobile device via the network and generate/update the graph data report”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karlin, Paineau, and Zedayko to incorporate the teachings of Mitteldorf by including: symptom severity graphing technique to the stool monitoring graph so that stored symptom assessments, such as Karlin’s pain and urgency assessments can be graphically displayed overtime, thereby allowing a user to readily identify improvement or deterioration in symptoms. The combination would have yielded the predicable result of a graph showing changes in symptom score over time.
Claim(s) 91 is rejected under 35 U.S.C. 103 as being unpatentable over Karlin (US 20200395124) in view of Zedayko (US 20210151137), and further in view of Paineau (US 20210265059) and Sparandara (US 20120131507).
Regarding claim 91:
Karlin in view of Paineau and Zedayko teaches the limitations of claim 30 as applied above.
While Karlin ¶¶ [0029] – [0031] teach subject severity assessments for urgency and pain associated with a stool event and digital image. Karlin ¶ [0098] teaches timeline visualization of all analyzed data including subjective assessments such as pain and urgency. And Paineau ¶ [0058] teaches a plurality of entries stored, development graphs may be created in order to allow to follow the changes in the stool consistency, and also in order to determine the frequency in which the stool is provided, which may provide additional information related to for example diarrhea or constipation
Karlin in view of Paineau and Zedayko does not explicitly teach: wherein the graph shows a change in symptom score over time.
However, in a related filed, Sparandara teaches: further comprising narrowing a date range of the graph by selecting, via the user device, a date range of the stool diary ( ¶¶ [0007] – [0009], [0055] – [0056] and FIG. 4 teach a patient information timeline arranged to provide a longitudinal visualization of a patient’s record, navigable to display different portions of the patient’s record based on subset/range of dates currently viewable. ¶ [0061] teaches that in the example of FIG. 4, a scroll bar or line 420 indicates a subset or range of dates and/or other timeline entries shown on the summary portion 440 of the timeline 400. Dots and/or other indicators 430 highlight information available along the timeline 400 for the patient. Indicators 430 falling within the range specified by the scroll line 420 are highlighted for the user).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Karlin, Paineau, and Zedayko to incorporate the teachings of Sparandara by including: timeline and date range navigation into the stool diary graph to allow the user to narrow the date range of the displayed graph. The motivation to combine these references is to improve navigation and reduce visual clutter when viewing numerous time stamped longitudinal medical/stool entries, a predictable use of a known patient timeline user interface control.
Response to Arguments
Applicant's arguments filed 05/20/2026 have been fully considered but they are not persuasive.
Applicant argues that Karlin and Zedayko do not teach or reasonably suggest displaying a historical trend to the subject as a graph of changes of the one or more characteristics over time. This argument is not persuasive. Karlin is not limited to frequency of bowel movements or diagnostic-tree mapping. Karlin expressly teaches automated classification of stool type by color, texture, consistency, float, and size, associating those classifications with pain and urgency, and rapidly visualizing all analyzed data on a timeline (Karlin ¶ [0098]). The phrase 'all analyzed data' includes the stool characteristic classifications and subjective assessments, not merely frequency. Karlin further teaches that this aspect enables patients, researchers, and clinicians (Karlin ¶ [0098]).
Applicant's argument is further overcome by Paineau as applied above in claim 30. Paineau expressly teaches a portable device stool module that displays a stool analysis score, stores score/image information, and creates development graphs from a plurality of stored entries to follow changes in stool consistency over time (Paineau ¶ [0058]). Paineau is directly in the field of portable device stool pattern tracking and therefore expressly teaches the very stool entry to graph functionality added by applicant's amendment.
Applicant argues that neither Karlin nor Zedayko presents subject medical data in a non-medical manner to a subject on the subject's device. This argument is not commensurate with the scope of claim 30 because claim 30 does not recite a 'non-medical manner,' a 'subject-friendly manner,' any particular patient literacy adaptation, or any particular display format beyond a graph. Nonetheless, Karlin expressly includes patients among those enabled by the timeline visualization (Karlin ¶ [0098]), and Paineau expressly teaches a portable device app for parents/caregivers to track stool entries, display scores, and create development graphs (Paineau ¶ [0058]).
Applicant argues there is no reasonable expectation of success in combining Karlin and Zedayko. This argument is not persuasive. Karlin, Zedayko, and Paineau are analogous art directed to gastrointestinal/stool health monitoring, image analysis of stool/biological samples, health assessment, and user facing health information or recommendations. Combining Karlin's automated stool image monitoring and timeline visualization with Zedayko's treatment recommendation and Paineau's portable device stool graphing would merely combine known elements according to their established functions to produce the predictable result of a user facing stool characteristic trend graph and corresponding intervention recommendation.
Regarding applicant's 101 arguments, applicant contends that claim 30 is not a mental process because human observation is variable and because a processor can be trained on standardized ratings. This argument is not persuasive. Independent claim 30 does not recite a particular trained model, a specific image processing algorithm, or a specific technical standardization mechanism. Machine learning appears only in dependent claim 39 with a high level of generality. Claim 30 remains directed to collecting stool image information, determining characteristics, generating/displaying a graph, identifying changes, and determining an intervention, which is information collection, evaluation, comparison, and recommendation.
Applicant also argues that displaying the graph to the subject improves medical data communications by coordinating data distribution to a new population. This is not a technical improvement to computer functionality, display technology, image capture, image processing, or a network/communication protocol. Merely specifying the audience or recipient of information and displaying the information as a graph is presentation of information and does not integrate the abstract idea into a practical application.
Applicant further argues under Step 2B that the graph limitation is not well-understood, routine, or conventional because the previously cited references allegedly did not teach it. This argument is not persuasive because the obviousness analysis and the Step 2B inquiry are distinct from each other. Nevertheless, the record now includes Paineau, which expressly teaches a portable device stool application that creates development graphs from stored stool entries to follow changes in stool consistency, and as mentioned above Karlin also teaches timeline visualization of analyzed stool classifications. Thus, the added display/graph limitation is not sufficient to supply an inventive concept that is not well-understood, routine, or conventional.
Relevant Art not relied on
Abell (US 8417342) discloses GI total symptom score graphs including nausea and vomiting scores plotted against days in FIGS. 7-9.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WASSIM MAHROUKA whose telephone number is (571)272-2945. The examiner can normally be reached Monday-Thursday 8:00-5:00 EST.
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665
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