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 .
Formal Matters
Applicant's response, filed 27 January 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claim 1, 2, 4, 6-15, 17-21, 23, and 26-32 are currently pending and have been examined.
Claims 1, 15, 21, and 30-32 have been amended.
Claims 3 and 24 have been canceled.
Claims 1, 2, 4, 6-15, 17-21, 23, and 26-32 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 09 December 2020 claiming benefit to Provisional Application 63/123,205.
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, 2, 4, 6-15, 17-21, 23, and 26-32 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 2, 4, 6-15, 17-21, 23, and 26-32 are directed to systems and a method, which are statutory categories of invention. (Step 1: YES).
Step 2A – Judicial Exception Analysis, Prong One:
Independent claim 1 (exact language hereinafter), independent claim 15, and independent claim 21 recite: display a series of questions from a set of one or more urinary tract health questionnaires; receive a selection of answers from the patient in response to the displayed series of questions, the answers being associated with one or more urinary tract symptoms of the patient; process the selection of answers to output a classification of the one or more urinary tract symptoms of the patient into a respective one of a plurality of diagnostic clusters; and process the selection of answers to output a severity level of the urinary tract disease category of the patient that is associated with the respective one of the plurality of the diagnostic clusters; and causing a recommended treatment to be administered to the patient that is specific to (i) the respective diagnostic cluster for the patient that is output by the first supervised machine learning model and (ii) the severity level of the urinary tract disease category of the patient associated with the respective diagnostic cluster that is output by the second supervised machine learning model, wherein: in response to the respective diagnostic cluster for the patient being associated with bladder pain syndrome, the recommended treatment includes an intravesical instillation, pentosan polysulfate, amitriptyline, a bladder analgesic, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with non-urological urogenital pain, the recommended treatment includes a gynecologic intervention, a topical anesthetic, a topical neuromodulatory agent, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with pelvic floor dysfunction, the recommended treatment includes pelvic floor physical therapy, a trigger point injection, a muscle relaxant, pelvic floor biofeedback, or any combination thereof; and in response to the respective diagnostic cluster for the patient being associated with urgency urinary incontinence, the recommended treatment includes an antimuscarinic oral medication, a sympathomimetic oral medication, an intradetrusor onabotulinum toxin, sacral neuromodulation, posterior tibial nerve stimulation, or any combination thereof; after a first administration of the recommended treatment, re-evaluate the patient by acquiring an updated selection of answers to the series of questions from the one or more urinary tract health questionnaires and reprocessing the updated selection of answers with the second supervised machine learning model to determine an updated severity level of the urinary tract disease category of the patient that is associated with the respective one of the plurality of diagnostic clusters; and automatically update the recommended treatment in response to a difference between the initial severity level and the updated severity level.
The claims, as drafted, cover Certain Methods of Organizing Human Activity. if a claim limitation, under its broadest reasonable interpretation, covers the management of personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claims broadly recite the receiving and modeling of patient data in order to determine a patient diagnosis and a severity thereof and causing a treatment to be administered (notable only the computer implemented system claims as the broadest reasonable interpretation of the system does not actively require the administration of the drug but instead a “causing” of a recommendation of a treatment consistent with the instant specification that does not support a computer based therapy controller). Such activity is considered to cover the management of personal behavior or relationships or interactions between people, as disease diagnosis is ultimately used to inform effective patient care – an example of patient-physician interactions and/or individual patient behavior – which is hampered if conditions are not properly identified and classified (see: Specification, paragraph 5). Accordingly, the claims recite Certain Methods of Organizing Human Activity and, therefore, an abstract idea. (Step 2A, Prong One: YES).
Independent Claim 1 further recites wherein the plurality of diagnostic clusters is generated by a [model] trained according to a clustering algorithm to receive a plurality of training patient response datasets and categorize the plurality of training patient response patient datasets into the plurality of diagnostic clusters, wherein each of the plurality of diagnostic clusters is associated with a distinct urinary tract disease category, and wherein the first supervised machine learning model is trained on the plurality of training patient response datasets and the diagnostic cluster into which each training patient response datasets was categized by the [model]; and the second supervised machine learning model being specific to the respective one of the plurality of diagnostic clusters, the second machine learning model for each respective diagnostic cluster being trained on (i) a portion of the plurality of training patient response datasets categorized into the respective diagnostic cluster by the unsupervised machine learning model, and (ii) a set of corresponding severity level labels.
Independent Claim 21 further recites wherein the plurality of diagnostic clusters is generated by a [model], and wherein each of the plurality of diagnostic clusters is associated with a distinct urinary tract disease category.
In light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claims also recite mathematical concepts and/or a mental process. The training steps recited in the claims constitute mathematical algorithms or mental processes insofar as they amount to merely labeling, classifying, and fitting data to a particular model representation. These techniques include k-means clustering and random forest algorithms, which are widely prevalent training algorithms that leverage mathematical relationships and operations, to sufficiently train their respective predictive models. The training, thus, constitutes abstract activity, and the claims are accordingly rejected under 35 USC § 101.
Dependent Claims 2, 19, and 23 recite wherein the plurality of diagnostic clusters includes an asymptomatic control cluster, a bladder syndrome cluster, a non-urologic urogenital pain cluster, a pelvic floor dysfunction cluster, and a [sic] urgency urinary incontinence cluster.
Dependent Claim 4 recites wherein the first supervised machine learning model is trained using a training dataset, the training dataset comprising a plurality of patient response datasets, each of the plurality of patient response datasets including responses to the one or more urinary tract health questionnaires from one of a plurality of patients.
Dependent Claims 6, 17, and 26 recite wherein the unsupervised machine learning model includes a k-means clustering algorithm, and wherein an elbow method is used to determine a number of the plurality of diagnostic clusters.
Dependent Claim 7 recites wherein the unsupervised machine learning model is trained based on one or more of a Ward's method of hierarchical clustering, an elbow method to determine a number of the plurality of diagnostic clusters, and a k-means clustering algorithm.
Dependent Claim 10 recites predict an effectiveness of the recommended treatment based on the classification.
Dependent Claims 11, 20, and 29 recite wherein the set of one or more urinary tract health questionnaires comprises one or more of Interstitial Cystitis Symptom and Problem Indices (ICSI/ICPI), Overactive Bladder Questionnaire (OABq), Genitourinary Pain Index (GUPI), and Pelvic Floor Disability Index (PFDI-20).
Dependent Claim 12 recites and wherein the random forest model is trained using a dataset labelled using an unsupervised k-means clustering process on data from the set of one or more urinary tract health questionnaires.
Dependent Claim 13 recites train the first supervised machine learning model with the selection of answers from the patient and demographic data to output an updated [model].
Dependent Claim 14 recites wherein the classification of the one or more urinary tract symptoms of the patient into the respective one of the plurality of diagnostic clusters is further based on a set of demographic data describing the patient.
Dependent Claim 18 recites output a severity level for each classification determined by the first [model].
Dependent Claim 30 recites wherein the severity level is determined at a first time; and wherein at a second time after the first time subsequent to the patient undergoing the recommended treatment: receive an updated selection of answers from the patient in response to the displayed series of questions; and determine whether to update the recommended treatment based on a difference between the severity level determined at the first time and the updated severity level determined at the second time.
Dependent Claim 31 recites wherein the severity level is determined at a first time, and wherein the method further comprises, at a second time after the first time subsequent to the patient undergoing the recommended treatment: receiving an updated selection of answers from the patient in response to the displayed series of questions; and determining whether to update the recommended treatment based on a difference between the severity level determined at the first time and the updated severity level determined at the second time.
Dependent Claim 32 recites wherein the severity level is determined at a first time, at a second time after the first time subsequent to the patient undergoing the recommended treatment: receive an updated set of patient response data for the patient associated with the one or more urinary tract symptoms of the patient; and determine whether to update the recommended treatment based on a difference between the severity level determined at the first time and the updated severity level determined at the second time.
Each of the preceding features of the above dependent claims only serve to further limit or specify the abstract features of independent Claims 1, 15, and 21, and, hence, are nonetheless directed towards fundamentally the same abstract idea as the independent claims.
Step 2A – Judicial Exception Analysis, Prong Two:
The judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer (MPEP § 2106.05(f)) and/or insignificant pre-/post-solution activity (MPEP § 2106.05(g)).
The claims are abstract but for the recitation of the additional claim elements including “a display device,” (Claims ) “a user interface,” (Claim 1) “a/the memory,” (Claims 1 and 21) “and a control system coupled to the memory and comprising one or more processors, the control system configured to execute a machine executable code stored thereon to cause the control system to,” (Claim 1) “on the display device,” (Claims 1) “from the user interface,” (Claims 1) “using a first supervised machine learning model,” (Claims 1, 15) “by an/the unsupervised machine learning model,” (Claims 1, 15, and 21) “using a/the second supervised machine learning model,” (Claims 1, 15, 21, and 30-32) “by the unsupervised machine learning model,” (Claim 1) “a/the second supervised machine learning model,” (Claims 1 and 21) “wherein the control system is further configured to execute the machine executable code to cause the control system to,” (Claims 3, 10, and 13) “wherein the second supervised machine learning model is a random forest model,” (Claims 9 and 28) “wherein the first supervised machine learning model is a random forest model,” (Claims 12, 17) “a first supervised machine learning model,” (Claims 13 and 21) “via a user interface,” (Claim 15) “wherein the first supervised machine learning model is trained to,” (Claim 18) “by the first supervised machine learning model,” (Claim 18) “wherein the second supervised machine learning model is a classification or a regression model,” (Claims 8 and 27) “a control system comprising one or more processors coupled to the memory,” (Claim 21) “the control system configured to execute the machine executable code to cause the control system to,” (Claim 21) “wherein the control system is further configured to execute the machine-readable instructions to, at a second time after the first time subsequent to the patient undergoing a treatment regimen,” (Claims 30 and 32) “on the display device,” (Claims 30-31) and “from the user interface.” (Claims 30-31).
Furthermore, the use of a computer to train and implement unsupervised and supervised machine learning models, particularly to group patients into diagnostic clusters and predict the onset of a symptom and the severity thereof (see at least Claims 1-2, 4-9, 12-15, 17-19, 21, 23, 26-28, and 30-32) amounts to applying data to an algorithm, organizing the data, and reporting the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instructions to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2.
The above-identified additional claim elements fail to integrate the abstract idea into a practical application, as they merely recite standard computer components and/or technologies at a high level of generality such that they amount to mere instructions to apply the abstract idea. For example, the first and second supervised models, per paragraphs 11 and 47 of the specification to be any one of a random forest, support vector machine, k-nearest neighbor, and/or convoluted neural networks, which are widely prevalent machine learning algorithms. Moreover, because these commonplace algorithms can be used interchangeably to satisfy the functionality of the invention, it appears the algorithm itself need not be specific to the present limitations and thus the invention can be broadly applied to any standard algorithm. Similarly, in light of paragraphs 131, 133, and 136 of the specification, the computer hardware, namely the memory, display device(s), and processor(s), constitute generic processing circuitry and/or computer storage devices. The same determination is true of the other aforementioned additional elements in the previous paragraph herein. Accordingly, the identified additional claim elements amount to mere instructions to apply the abstract idea (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”). Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014)).
Claim 15 recites “administering a recommended treatment to the patient that is specific to (i) the respective diagnostic cluster for the patient that is output by the first supervised machine learning model and (ii) the severity level of the urinary tract disease category of the patient associated with the respective diagnostic cluster that is output by the second supervised machine learning model, wherein: in response to the respective diagnostic cluster for the patient being associated with bladder pain syndrome, the recommended treatment includes an intravesical instillation, pentosan polysulfate, amitriptyline, a bladder analgesic, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with non- urological urogenital pain, the recommended treatment includes a gynecologic intervention, a topical anesthetic, a topical neuromodulatory agent, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with pelvic floor dysfunction, the recommended treatment includes pelvic floor physical therapy, a trigger point injection, a muscle relaxant, pelvic floor biofeedback, or any combination thereof; and in response to the respective diagnostic cluster for the patient being associated with urgency urinary incontinence, the recommended treatment includes an antimuscarinic oral medication, a sympathomimetic oral medication, an intradetrusor onabotulinum toxin, sacral neuromodulation, posterior tibial nerve stimulation, or any combination thereof.” When determining if a particular treatment and prophylaxis as a practical application under Step 2A Prong Two, Examiner considered the factors presented in MPEP § 2106.04(d)(2).
Factor A. The treatment plan determined from the abstract idea is not "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). Here, the treatment delivered is not specific. Here, the multitude of treatment options are non-specific and do not include any dosage or delivery timing determinations.
Factor B. The treatment limitation does not have a significant relationship to the judicial exception – that is it does not integrate the law of nature into a practical application. The judicial exception is directed towards diagnostic practices, the treatments are listed known treatments for the diagnosis provided by the abstract idea, that is, the judicial exception does not in any way guide the treatment delivery only the diagnostic process.
Factor C. The treatment or prophylaxis limitation does not impose meaningful limits on the judicial exception and is only extra-solution activity or a field-of-use (see MPEP § 2106.05(g))). The administering step of any of the treatments for the diagnoses is well known, nominally related to the inventive concept of diagnosing a patient with pelvic pain, and amount to necessary data output similar to that of In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016). The step does not add a meaningful limitation to the process of determining a treatment plan for a patient.
Therefore, the claims only recite the prophylactic step as a tool which only serves to as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Claim 13 recites “store the first supervised machine learning model in the memory,” and “store the updated first supervised machine learning model in the memory.” Claim 21 recites “[the memory] storing executable code, a first supervised machine learning model, and a second supervised machine learning model.” Storing executable code or trained models in a memory does not impose meaningful limitations on the receiving, modeling, or analyzing of patient response data; it merely explains how the models are handled before and after said modeling and analysis. Therefore, the limitation constitutes insignificant post-solution activity (MPEP § 2106.05(g) – insignificant pre-/post-solution activity) and accordingly cannot serve as a practical application of the established abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application (Step 2A, Prong Two: NO).
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer and/or the performance of extra-solution activity.
The additional elements determined to constitute the use of generic computer or technological components to implement the abstract idea are recited only as a tool for performing the steps of the abstract idea. These additional elements, therefore, only amount to mere instructions to perform the abstract idea using a general purpose computer and are, thus, insufficient to amount to significantly more than the abstract idea (see: MPEP § 2106.05(f) for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the Specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the Specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the Specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the Specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in. III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 15 recites “administering a recommended treatment to the patient that is specific to (i) the respective diagnostic cluster for the patient that is output by the first supervised machine learning model and (ii) the severity level of the urinary tract disease category of the patient associated with the respective diagnostic cluster that is output by the second supervised machine learning model, wherein: in response to the respective diagnostic cluster for the patient being associated with bladder pain syndrome, the recommended treatment includes an intravesical instillation, pentosan polysulfate, amitriptyline, a bladder analgesic, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with non- urological urogenital pain, the recommended treatment includes a gynecologic intervention, a topical anesthetic, a topical neuromodulatory agent, or any combination thereof; in response to the respective diagnostic cluster for the patient being associated with pelvic floor dysfunction, the recommended treatment includes pelvic floor physical therapy, a trigger point injection, a muscle relaxant, pelvic floor biofeedback, or any combination thereof; and in response to the respective diagnostic cluster for the patient being associated with urgency urinary incontinence, the recommended treatment includes an antimuscarinic oral medication, a sympathomimetic oral medication, an intradetrusor onabotulinum toxin, sacral neuromodulation, posterior tibial nerve stimulation, or any combination thereof. Treating a patient with oral medication, physical therapy, or electrical simulation based of a diagnostic determination for pelvic pain is well understood, routine, and conventional. This position is supported by Fall et al., EAU Guidelines on Chronic Pelvic Pain, 46 European Urology 681-689 (2004) in Table 5 on p. 684, Table 6 on p. 685, and § 8. General treatment of chronic pelvic pain on p. 687 (treated as a review under MPEP § 2106.07(a)(III)(C) that describes the state of the art and discusses what is well-known and in common use in the relevant industry). Therefore, the use of the treatment step is not sufficient to amount to significantly more than the recited judicial exception.
Claim 13 recites “store the first supervised machine learning model in the memory,” and “store the updated first supervised machine learning model in the memory.” Claim 21 recites “[the memory] storing executable code, a first supervised machine learning model, and a second supervised machine learning model.” Storing executable code or trained models in a memory amounts to insignificant extra-solution activity (MPEP § 2106.05(g) – insignificant pre-/post-solution activity). Extra-solution activity cannot provide significantly more than the judicial exception if it amounts to well-understood, routine, and conventional activity. A showing can be made that a limitation is well-understood, routine, and conventional by citing one or more court decisions noting the well-understood, routine, and conventional nature of the additional element(s) (MPEP § 2106.05(d)(I)). The storing and retrieving information in memory was deemed to be well-understood, routine, and conventional activity by the Federal Circuit (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.).
Therefore, the storing the supervised machine learning models in a/the memory, as recited in the limitations, constitutes well-understood, routine, and conventional activity, and, thus, cannot amount to significantly more than the judicial exception.
For the reasons stated, claims 1, 2, 4, 6-15, 17-21, 23, and 26-32 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 27 January 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive.
First, Applicant asserts that under Step 2A Prong 2, the claims recite a particular treatment or prophylaxis practical application by requiring a closed loop protocol. Examiner disagrees. The requestioning and reprocessing of the data to determine a patient’s severity level does not affect the outcome of the particular treatment or prophylaxis analysis under MPEP § 2106.04(d)(2). While the machine learning algorithm may be used to determine a particular treatment as in Example 49, using a machine learning algorithm for determining any treatment from a list of known treatments similar to that of Example 49 claim 1 that is determined to be ineligible. Example 49 clearly demonstrates that a singular particular treatment is required. While dosages or timing may aid in the determination that a treatment is particular, it is not the sole consideration – the list of every possible treatment disqualifies the claim.
Next, Applicant asserts that the instant claims amount to an improvement to technology via updating the patient recommendation when new data is available – i.e. a “closed” loop update. Examiner is not persuaded. The application of unique data to known clustering models performing in their intended manner amounts to an improvement to the abstract idea. That is, an improvement to the abstract idea reevaluating a patient when new data is available according to a model does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Finally, Applicant asserts that the “closed loop regimen control” automatically updating treatment recommendations is not well understood, routine, and conventional under Step 2b. Examiner disagrees. Without commenting regarding the conventionality of updating a treatment plan when a patient’s condition severity changes, these steps are considered a part of the abstract idea and not additional elements. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857