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 .
Notice to Applicant
Examiner notes that there are two different concurrently filed sets of claims, with no amendment markings present in either set. A call to Applicant’s representative Syed Abedi was made on 01/14/2026 to ascertain which set of claims is intended to be examined, but no response was received. The entered claim set with a listing of claims 1-20 has been examined below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/02/2024 are in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
Priority
Claims PRO 63/348,318 and 371 of PCT/CA2023/050736 priority is acknowledged.
Drawings
The drawings are objected to because figures 3A-4J, 4M-7Z are blurry. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “regular intervals” in claim 1 is a relative term which renders the claim indefinite. The term “regular intervals” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 3 recites the limitation "the type of risk being calculated". There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the Al / ML inference module". There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the computing device". There is insufficient antecedent basis for this limitation in the claim.
Claims 6 and 16 the phrase “over a period of to assess” renders the claim indefinite because it is grammatically incomplete and lacks a necessary noun or duration. It is unclear if image data is collected over a "period of time," a "period of months," or another unspecified duration. This omission makes it impossible for one of ordinary skill in the art to determine the metes and bounds of the temporal requirement for image collection are rejected under 35 U.S.C 112(b) due to their dependence on claim 1, and claims 12-20 are rejected due to their dependence on claim 11.
Note: Claims 2-10
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.
Subject Matter eligibility Rejection 35 U.S.C 101
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis
The claims are directed to statutory subject matter, encompassing the following statutory categories:
Process (Claims 1-10): The language reciting a "computer-implemented method... comprising: collecting user health data... monitoring the user's health condition... and comparing the user's updated health condition" defines a series of acts or steps to be performed to achieve a result. This aligns with the definition of a process in MPEP § 2106.03, as the claims describe a functional sequence of data-gathering and analytical operations. Under the Broadest Reasonable Interpretation (BRI), these claims encompass a method of evaluating dietary supplement efficacy through systematic observation.
Machine (Claims 11-20): The language reciting "a system for assessing the efficacy... having a processor, a memory, and storage, and adapted to: collect... monitor... and compare" describes a concrete thing consisting of parts and organized to perform a particular function. This aligns with the definition of a machine in MPEP § 2106.03. Under BRI, the "system" is interpreted as a physical computing device or networked architecture comprising the structural components (processor, memory, storage) required to execute the recited instructions.
Having confirmed the claims are directed to statutory subject matter, the analysis proceeds to Step 2A Prong one.
Step 2A, Prong One: Judicial Exception Analysis
Step 2A, Prong One is to verify if a claim recites a specific judicial exception before determining if that exception is actually integrated into a practical application under prong two.
The whole invention is an evaluation of nutraceutical polyphenol efficacy that synthesizes health data from biosensors, surveys, and images into a monitored health outcome. As evidenced by the Abstract, Specification, and Drawings (FIGS. 1, 2, and 9), the core concept lies in systematically inferring health improvements from longitudinal biomarker observations. The disclosure characterizes this as an analytical system specifically designed to prevent the premature abandonment of supplement regimens through evidence-based progress tracking (Spec., para. [0004]-[0005], [0019]).
More specifically, the claims 1-20 are directed to a judicial exception because they recite the abstract idea of Mental Processes (evaluation and judgment). Because under the broadest reasonable interpretation (BRI), the claims recite collecting information about a user's health and supplement intake, and comparing that information to a baseline to determine a result (efficacy). This is a fundamental method for gathering and analyzing information, analogous to human observation and clinical judgment.
Independent Claims Recites the following non-bold parts abstract idea:
Claim 1. A computer-implemented method of assessing the efficacy of a nutraceutical polyphenol supplements and dosage regimen, the method executable on a computing device having a processor, a memory, and storage, and comprising:
collecting user health data from one or more biosensors, and from health assessment surveys executed on the computing device to establish a baseline for a user's health condition;
collecting data on the nutraceutical polyphenol supplements and dosage regimen a user is taking over time;
monitoring the user's health condition at regular intervals using one or more biosensors and health assessment surveys, and comparing the user's updated health condition against the user's baseline to determine efficacy of the nutraceutical polyphenol supplements and dosage regimen.
Note: The bolded portions represent additional elements evaluated in Prong Two and Step 2B. The non-bolded portions represent the abstract idea. The referenced applicant language is from public application number US20250349435A1.
Claim Abstract Classification Rationale
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claim 1 and 11 abstract idea recites collecting baseline health and supplement data, monitoring subsequent changes, and comparing the two to determine the effectiveness of a regimen. This process aligns with the following abstract idea category:
Mental Process (MPEP § 2106.04(a)(2)(III)): Concepts performed in the human mind, including an observation, evaluation, and judgment. Independent claims 1 and 11 recites "collecting user health data... to establish a baseline," "monitoring... at regular intervals," and "comparing... to determine efficacy." These cognitive steps represent the gathering of information followed by the analytical act of evaluating that information to reach a clinical conclusion. The specification supports this, stating: "the AI/ML inference module... analyses the data to make inferences about the efficacy... given an initial baseline" (Spec., para. [0069]). This is relevant because "inference" and "efficacy assessment" are analytical terms describing the evaluation of data to form an opinion or judgment, a process that can be performed entirely in the human mind.
Manual Replication Scenario (Human Equivalence)
The abstract nature of the claims is reinforced because the entire process is analogous to fundamental human activities. Even though the computer provides efficiency and speed in data handling, the underlying logic remains an abstract process that a human could do (MPEP 2106.04(a)).
A person could perform the entire claimed process of Claim 1/11 without any hardware or software as follows:
Establish Baseline: A user writes down their current weight and heart rate (measured manually) and answers health questions in a notebook.
Collect Supplement Data: The user marks a calendar every time they take their resveratrol pill and records the dosage amount.
Monitor & Compare: After 30 days, the user measures their heart rate again and answers the same questions.
Determine Efficacy (Limitation 3): The user looks at their initial notebook entry and their new measurements and thinks: "My heart rate is lower; therefore, this supplement is working."
Dependent Claims Analysis
The dependent claims 2-10 are also directed to an abstract idea.
Claims 2-3, 12-13: These claims recite under BRI collecting "multiple user health parameters" selected based on "risk." This is further Mental Process (evaluation and selection based on criteria).
Claims 4-5, 14-15: These claims recite "calculating a risk... by an AI/ML inference module," which is a Mathematical Concept and a Mental Process of making a prognosis based on statistics. This logic is further depicted in the predictive block diagram of FIG. 9.
Claims 6-7, 16-17: These claims recite "collecting image data" and "assessing improvements... against a model." This is the abstract idea of Observation (Mental Process) and comparison against a reference standard, specifically addressed in the image-based assessment of FIG. 8.
Claims 8-10, 18-20: These claims recite "tracking a group," "projecting long-term improvements," and "recommending a change." These are cognitive acts of providing a Mental Process result through observation and professional judgment (recommendation/advice).
The analysis now moves to Step 2A, Prong Two to determine if these additional elements integrate the judicial exception into a practical application.
Step 2A, Prong Two: Integration into a Practical Application
Step 2A, Prong Two evaluates whether the claim as a whole integrates the judicial exception into a practical application by determining if the additional elements impose a meaningful limit on the exception. The additional elements in the current claims fail to overcome this prong because they comprise generic computer components performing generic data-gathering and "apply it" instructions that do not transform the underlying abstract mental process.
Evaluation of Independent Claim 1/11 Additional Elements
Computing Device, Processor, Memory, and Storage: The recitation of general computer hardware fails to integrate the abstract idea because it:
(MPEP § 2106.05(f)) - Mere Instructions: This section specifies that integration is absent when a claim "simply recites a judicial exception and then adds 'apply it' on a computer," which is not overcome here. Claim 1 and 11 recites a method "executable on a computing device" where the Processor is instructed to perform the cognitive acts of "comparing" and "determining." Under BRI (MPEP § 2111), these components act merely as a tool to automate the mental steps, providing no meaningful limitation beyond the instruction to use a computer to execute the exception.
(MPEP § 2106.05(a)) - No Tech Improvement: This section requires an integration to result in "an improvement to the functioning of a computer or an improvement to any other technology," which is not overcome here. The specification admits that the Computing Device, Processor, Memory, and Storage are "generic" (Spec., para. [0125]). Because the claim language uses these elements for their ordinary data-processing and data-retrieval capacities, it fails to resolve a technological problem or improve the computer's internal operation.
(MPEP § 2106.05(h)) - Linking to Environment: This section states that "limiting the use of a judicial exception to a particular technological environment" is insufficient for eligibility, which is not overcome here. The requirement for a Memory and Storage to hold the "baseline" and "updated health condition" merely links the abstract comparison to the field of electronic record-keeping. These elements serve as a general environment for the informational analysis rather than a transformative application.
The recited computing components fail to integrate the abstract idea because they function as a general platform for "mere instructions" rather than providing a "technological improvement" (MPEP 2106.05(a/f)). By utilizing "generic" hardware for its ordinary data-processing capacity, the claim merely "links the exception to a technological environment" without achieving a practical application (MPEP 2106.05(h)). Consequently, these elements do not transform the mental process of efficacy assessment into a patent-eligible invention.
One or More Biosensors: The recitation of biosensors fails to integrate the abstract idea because it:
(MPEP § 2106.05(g)) - insignificant extra-solution activity to the judicial exception)This section defines integration as absent when the additional element "is a mere data gathering step," which is not overcome here. "Biosensors" are recited solely to collect the "user health data" needed to feed the "comparing" step; the specification describes these as general "smart watches" or "heart monitors" used in their standard capacity (Spec., para. [0125]).
The combined hardware and sensor elements fail to integrate the abstract idea because they act merely as generic tools for data gathering and automated mental processing (MPEP 2106.05(f/h)). Because the specification describes the architecture as generic and lacks any specific technological advancement, the claim fails to reach the technical transformation required for a practical application (MPEP 2106.05(a)). Thus, the arrangement remains an abstract informational process linked to a general environment rather than an eligible application.
Dependent Claims Analysis
The dependent claims recite additional software-based elements and narrowing limitations that fail to provide the necessary integration.
AI / ML Inference Module (Claims 5, 7, and 9 and 15, 17 and 19): The recitation of an artificial intelligence or machine learning module fails to integrate the abstract idea because it:
(MPEP § 2106.05(a)) - No Tech Improvement: This section requires that the software provides a specific improvement to computer functionality, which is not overcome here. The AI / ML inference module is used to perform "risk calculation" and "project long-term health improvements," which are improvements to the "accuracy of a mathematically calculated statistical prediction" (abstract idea) rather than a technical improvement to the computer's internal operation.
(MPEP § 2106.05(f)) - Mere Instructions: Using AI/ML to perform the "monitoring" or "assessing" steps merely uses a specific software tool to execute the mental process more efficiently. The specification describes the module's role as "analyzing the user data... to make inferences" (Spec., para. [0069]), which constitutes using the software as a tool for automated mental evaluation.
Other Dependent Claims (Claims 2-4, 6, 8, 10 and 12-14, 16, 18 and 20):
Claims 2-4, 6, 8, 10 and 12-14, 16, 18 and 20: These claims fail to recite any additional non-abstract hardware elements and instead merely narrow the abstract idea to specific parameters or informational outputs. Specifically, limitations such as "multiple user health parameters" (Claim 2), "calculating a risk" (Claim 4), or "recommending a change" (Claim 10) represent additional mathematical or mental steps.
When viewed as a whole, the combination of these elements in the dependent and independent claims does not integrate the abstract idea because they only further specify or automate the information being processed rather than providing a technological solution.
Because the claims are directed to an abstract idea without integrating it into a practical application, the analysis proceeds to Step 2B.
Step 2B: Inventive Concept Analysis
Step 2B determines whether the additional elements, alone or in combination, amount to significantly more than the judicial exception by reciting an inventive concept. Under MPEP 2106.05, these claims fail to overcome Step 2B because the additional elements represent general activities that do not transform the abstract idea into a patent-eligible invention. The generic computer components and general sensors merely automate the mental process of efficacy evaluation without providing a technical solution.
Evaluation of Independent Claim 1/11 Additional Elements
Computing Device, Processor, Memory, and Storage: The recited computing hardware fails to provide an inventive concept because it acts as a general platform for "mere instructions" rather than providing a technological improvement (MPEP 2106.05(a/f)). By utilizing "generic" components for their standard data-processing capacity (Spec., para. 0125), the claim merely "links the exception to a technological environment" (MPEP 2106.05(h)). Consequently, these elements comprise general computer functions that do not amount to significantly more than the abstract mental process.
One or More Biosensors: The recitation of biosensors fails to provide an inventive concept because these elements serve merely to link the abstract mental process to a specific technological environment for data acquisition (MPEP 2106.05(h)). The specification identifies these as general consumer devices: "user wearable biosensors may include smart watches, heart monitors, blood pressure sensors, heart rate sensors" (Spec., para. 0042), which provides no meaningful limitation beyond providing a field-of-use for the judicial exception.
When viewed as a whole, the combination of generic processors and general biosensors is not enough to amount to significantly more than the exception. The arrangement follows a general data-processing sequence that provides no unconventional technological transformation.
Dependent Claims Analysis
Group 1 (Claims 2-4, 6, 8, 10 and 12-14, 16, 18 and 20): These claims fail to recite any additional non-abstract hardware or software elements beyond those already evaluated. Specifically, limitations such as "multiple user health parameters" (Claim 2), "calculating a risk" (Claim 4), and "recommending a change" (Claim 10) merely narrow the abstract idea to specific informational categories or mental outputs. Because these limitations do not add non-abstract physical components or a specific technological improvement, they inherit the abstract nature discussed in Prong One and do not provide an inventive concept in Step 2B.
Group 2 (Claims 5, 7, 9 and 15, 17 and 19): These claims add an AI / ML inference module, which is a software tool used to automate the abstract idea and fails to improve computer functionality (a). The specification confirms this is a general application of inference: "the AI/ML inference module 226 receives relevant data... and analyses the data to make inferences" (Spec., para. 0069), which demonstrates that the AI is used merely to execute the cognitive steps faster than a human mind.
When viewed as a whole, the combination of dependent claims and additional elements is not enough to provide an inventive concept. The elements merely provide further granularity to the informational gathering and mental analysis without departing from general technological practices.
The claims are directed to an abstract idea and lack an inventive concept that would amount to significantly more than the exception. Therefore, claims 1-20 are rejected under 35 U.S.C. § 101.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 and 11-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hadley- US20210196195A1
Claim 1.
Hadley teaches, A computer-implemented method of assessing the efficacy of a nutraceutical polyphenol supplements and dosage regimen, the method executable on a computing device having a processor, a memory, and storage, and comprising: (Hadley et al., Paragraphs 0008, 0044, 0058, table 1, 0037-0038, fig. 2 “FIG. 2 is a high-level block illustrating an example of a computing device 200... chipset 210 coupled to at least one processor 205. Coupled to the chipset 210 is volatile memory 215... storage device 230 representing a non-volatile memory...”).
Hadley et al. describes a patient health management platform that tracks micronutrients (specifically including Flavonoids, which are polyphenols) and the exact dosage taken. Hadley et al. fulfills the requirement because it uses wearable sensors and app-based reports to establish an initial state (baseline) and then tracks improvements over time to quantify the impact of the supplement. Since Hadley et al. describes the specific process of measuring health changes to confirm if a supplement recommendation is improving a user's health, it covers every functional step claimed.
collecting user health data from one or more biosensors, and from health assessment surveys executed on the computing device to establish a baseline for a user's health condition; (Hadley, 0044, 0058 (Table 1), 0129)
Hadley discloses gathering "biosignals" from wearable sensors (biosensors) and "symptom data" via an app (surveys). In Paragraph 0129, Hadley uses this gathered data to generate an initial metabolic state, which functions as a baseline for the user's health. In Table 1, Hadley specifically lists "Energy" and "Mood" as survey-based data points. Therefore, Hadley performs the same function of using sensors and surveys to set a starting health point.
collecting data on the nutraceutical polyphenol supplements and dosage regimen a user is taking over time; (Hadley, 0044, 0058 (Table 1), 0129)
Hadley tracks "supplements" and specifically lists "Flavonoids" in Table 1 as a tracked nutrient. Under BRI, because "Flavonoids" are a specific type (species) of polyphenol, Hadley’s disclosure of tracking flavonoids satisfies the requirement for tracking polyphenols. Furthermore, Hadley tracks the precise dosage and time, which constitutes a "dosage regimen."
and
monitoring the user's health condition at regular intervals using one or more biosensors and health assessment surveys, and comparing the user's updated health condition against the user's baseline to determine efficacy of the nutraceutical polyphenol supplements and dosage regimen. (Hadley, 0006, 0008, 0095)
Hadley describes monitoring the user "regularly" using a "time series" of data from sensors and surveys. The platform then "captures changes" and "quantifies the impact" by comparing the new data to the earlier history (the baseline). In plain terms, Hadley is looking at the "before" and "after" data to see if the supplement recommendation worked, which is "determining efficacy."
Claim 2.
Hadley teaches, The computer-implemented method of claim 1, wherein the collected user health
data includes multiple user health parameters responsive to the nutraceutical polyphenol supplements and dosage regimen tracked simultaneously over time. (Hadley, 0044, 0045, 0058 (Table 1))
Hadley describes a "Patient Health Management Platform" that is specifically designed to understand how various health markers interact. Instead of just checking if a user's blood sugar goes down, Hadley’s system tracks a "combination" of factors including heart rate, weight, and blood pressure—all at once.
Claim 3.
Hadley teaches, The computer-implemented method of claim 2, wherein the user health parameters are selected based on the type of risk being calculated for a user. (Hadley, 0007, 0008, 0078, 0082, 0084, 0120, 0123, 0128, 0142-0143, 0154,0157-0159, 0162)
Hadley et al. discloses a "Rule-based Model" and a "Recommendation Module" that do not simply monitor all data points equally. Instead, the system selectively evaluates specific "biosignals" (parameters) to calculate the risk of specific medical conditions.
For example, the system specifically selects creatinine levels to assess kidney-related risks/medications, and HbA1c to assess diabetes-related risks. The "Rule-based Model" codifies medical expertise to ensure that only the parameters "responsive" to the specific medical concern (risk type) are utilized for that user's treatment plan. Because the system's logic picks specific parameters based on the specific "rules" or "risks" being addressed, it fulfills the requirement of selecting parameters based on the type of risk.
Claim 4.
Hadley teaches, The computer-implemented method of claim 1, further comprising calculating a risk of developing or suffering a medical condition within a set timeframe based on observed changes in the user's health condition resulting from the nutraceutical polyphenol supplements and dosage regimen. (Hadley, 0007, 0008, 0078, 0084, 0120, 0123, 0128, 0142-0143, 0162)
Hadley et al. describes a system that calculates a "representation of the patient's metabolic state" [0127]. This "metabolic state" is explicitly linked to medical conditions like "diabetic" or "pre-diabetic" [0078]. Because Hadley predicts these future states over specific durations (e.g., a "14 day sequence" or "long-range"), it is performing a calculation of the probability or risk of the patient being in that condition.
Claim 5.
Hadley teaches, The computer-implemented method of claim 4, wherein the risk calculation is made by an Al / ML inference module based on any or all of the user's health data as collected for the baseline and any subsequent measurement. (Hadley, 0006, 0018, 0118, 0044, 0009)
Hadley describes a "machine learned model" that acts as an inference module. The purpose of this module is to "predict" a metabolic response. In the context of healthcare data management, predicting a metabolic response or state constitutes a "risk calculation," as it determines the likelihood of the patient achieving a target metabolic outcome versus remaining in a state of "impaired metabolism." The function of the Hadley "model" is identical to your "inference module" it takes raw data and calculates a health-related conclusion.
Note: Claim(s) 1-5 were used to anticipate too claims 11-15, for being very similar.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-10 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hadley-US20210196195A1 and further in view of Davis - US20140316235A1
Claim 6.
Hadley teaches, The computer-implemented method of claim 1, further comprising .
Hadley et al. teaches claim 1, however is missing the specific requirement of capturing and analyzing skin images.
Davis et al. teaches the collecting image data of a user's skin condition... to assess skin texture and appearance, describing the use of a large, crowd-sourced, image reference library that depicts skin rashes and other dermatological conditions (Abstract) where dimensions of differential diagnosis in dermatology include location on body, color, texture, shape, and distribution (para. 0004). Davis et al. further teaches assessing these conditions in response to a nutraceutical supplement, stating the system may report that 27% of people having a skin condition like that depicted in the user's query image report taking Vitamin A supplements (para. 0011) and identifying correlated factors... so that possibly causative factors might be addressed (para. 0013).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Hadley et al. with Davis et al. because both references are directed to the field of digital health monitoring via portable devices to optimize physiological outcomes. Hadley et al. teaches a Metabolic Health Management System 100 (FIG. 1) that consumes biosignals recorded for a patient by a variety of sources (para. [0048]), and Davis et al. teaches that dermatological diagnosis tends to be based on very casual techniques... More rigorous diagnostic techniques can be applied... using Smartphone Snapshots (para. 0002-0005). Combining the references would integrate skin-based "biosignals" into the metabolic profile, as Davis et al. explicitly identifies diet, medications... and environmental factors (para. 0004) as relevant markers for skin health.
The combination makes the full limitation obvious because it integrates visual dermatological tracking into a comprehensive metabolic platform. A PHOSITA would integrate the image data of a user's skin condition into the system of Hadley et al., which already includes video capabilities (e.g., a microphone for recording, a display screen for text and/or video) (para. [0030]), to achieve the benefit of a holistic assessment of treatment efficacy. Davis et al. teaches that 3D information about the surface topology of the skin... is used in the matching process (para. 0009) and that this data helps identify correlated factors... such as diet (para. 0013). Assessing the polyphenol supplements specifically is a predictable application of Davis et al.’s teaching to monitor lifestyle habits including diet, medications (para. 0004) and supplements (para. 0011).
The combination makes the full limitation obvious because it leverages the skin as a known external biomarker for internal metabolic success. A PHOSITA would integrate the image data of a user's skin condition of Davis et al. into the system of Hadley et al. to provide a non-invasive, visual feedback loop to monitor the efficacy of the prescribed nutritional regimen. Hadley et al. emphasize the need to capture a deep understanding of the combination of continuous biosignals (para. 0006) to monitor metabolic states, while Davis et al. argue that skin "texture" and "appearance" are direct indicators of "environmental factors including diet and medications" (para. 0004). This diagnostic link is evidenced by Davis et al.'s mapping of visual skin markers to physiological inputs; specifically, the system pinpointing dermatological changes in users taking Vitamin A (para. 0011) provides a clear technical roadmap for a PHOSITA to monitor other nutraceuticals, such as polyphenols, which are likewise known to influence cutaneous health. Therefore, using visual assessment to monitor the response to the nutraceutical polyphenol supplements is a predictable application of Davis et al.’s teaching that certain skin conditions correlate to "Vitamin A supplements" (para. 0011). A PHOSITA would recognize that for a patient managed for metabolic dysfunction, tracking skin texture provides a verifiable, real-time metric for the "optimal metabolic outcomes" sought by Hadley et al.
A PHOSITA would have had a reasonable expectation of success because the technical integration is routine. Hadley et al. already utilizes near real-time biological data recorded by wearable sensors (para. [0006]), and Davis et al. confirms that high quality imagers on Smartphones... facilitates creation of a large... image reference library (Abstract). Since both systems rely on standard mobile hardware and machine learning modules to process data, combining the image-based "skin twin" into the "metabolic twin" is a straightforward software integration using existing APIs.
Claim 7.
Hadley in view of Davis teaches, The computer-implemented method of claim 6, wherein any improvements to the user's skin condition are assessed by an Al / ML inference module based on comparing the collected image data of the user's skin condition against a model. (Davis, par. 0238-0239, 0065)
Davis et al. describes how image analysis techniques are employed to identify salient similarities between features of the uploaded image, and features of images in this reference library (Abstract) and utilizes neuromorphic processing techniques (sometimes termed “machine learning,” “deep learning,” or “neural network technology”) (para. 0238) to classify a type of mole or condition (para. 0239). Davis et al. explicitly teaches assessing changes over time (improvements), stating the system makes use of changes in the user's depicted symptoms over time where the system determines data about a change in the depicted skin symptom... used in further refining diagnostic information (para. 0065).
Claim 8.
Hadley taches, The computer-implemented method of claim 1, further comprising tracking a group of users by .
Hadley et al. teaches a computer-implemented method comprising tracking a group of users, stating the machine learned model(s) are trained based on a large body of historical patient data... for a population of patients (para. 0007). Hadley et al. further teaches showing efficacy of the nutraceutical polyphenol supplements, describing how the research device 150... may evaluate the effectiveness of the treatment recommendation as a whole (para. 0034) and updates nutrition data with macronutrient, micronutrient, and biota information (para. 0520). However, Hadley et al. fails to disclose the specific Missing Element of anonymizing their data while tracking the group.
Davis et al. teaches tracking a group of users by anonymizing their data, describing a large, crowd-sourced, image reference library where a user submits a query image to the system (typically with anonymous enrollment/contextual information, such as age, gender, location, and possibly medical history, etc.) (para. 0006-0007). Davis et al. further teaches showing efficacy for the group, stating those reference images whose derivatives most closely correspond to the query image are determined... statistically relevant correlations emerge (para. 0007) and identifying correlated factors... so that possibly causative factors might be addressed (para. 0013). Davis et al. explicitly notes the benefit of this anonymous crowd-sourced data, stating unprecedented knowledge will be revealed as the present system grows to large scale (para. 0070).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Hadley et al. with Davis et al. because both references rely on population-level data to refine treatment predictions and improve patient outcomes. Hadley et al. utilizes a training dataset... for a population of patients (para. 0007). Davis et al. teaches that expert medical practitioners have the opportunity to “seed” such databases and that user-submitted information is added to the knowledge base (para. 0014, 0068, 0166, 0006-0007). Combining these would allow for the large-scale "research" functions of Hadley et al. to be performed using the privacy-preserving "anonymous enrollment" methods of Davis et al., which is standard practice in medical data collection to ensure patient privacy while maximizing the data set size.
The combination makes the full limitation of Claim 8 obvious because it utilizes a known data-handling technique (anonymization) to facilitate a known analytical goal (efficacy tracking across a population). A PHOSITA would integrate the anonymizing protocol of Davis et al. into the group-tracking system of Hadley et al. to aggregate the nutraceutical polyphenol supplements and dosage regimen data. Hadley et al. describes a research device to evaluate the effectiveness of the treatment recommendation (para. 0034). Davis et al. shows how this is done with a group by identifying statistically-significant co-occurrence information where the system can report that 27% of people having a skin condition... report taking Vitamin A supplements (para. 0011). A PHOSITA would realize that anonymizing this data, as taught by Davis et al. (para. 0007), is necessary to legally and ethically evaluate the effectiveness of the polyphenols for the population of patients described in Hadley et al. (para. 0007).
A PHOSITA would have had a reasonable expectation of success because anonymizing medical data for research is a routine requirement in the field of health informatics. Davis et al. provides a clear example of implementing anonymous enrollment/contextual information (para. 0007) within a digital image library. Applying this data-masking technique to the population datasets already described in Hadley et al. involves standard database management practices and does not require a significant departure from the technical capabilities disclosed in either reference.
Claim 9.
Hadley and Davis teaches, The computer-implemented method of claim 8, wherein the Al / ML inference module is adapted to learn from the data collected for a group of users, and project long- term health improvements for the group of users taking the nutraceutical polyphenol supplements and dosage regimen. (Hadley, par. 0007-0008, 0128, 0123-0124, 0143)
The claim is about "Group Learning" and "Forecasting." Hadley's system is built on exactly these pillars. It uses data from a "universe of patients" (the group) to "train" its models. It then uses those models to create "long-range predictions" (projections) of how a patient's health will change if they follow a "recommendation" (the regimen).
Claim 10.
Hadley and Davis teaches The computer-implemented method of claim 9, wherein the method further comprises recommending a change in the nutraceutical polyphenol supplements or the dosage regimen based on the long-term health of a group of users. (Hadley, 0008, 0095, 0116, 0124)
Hadley teaches a "feedback loop" designed to "refine and optimize" treatments based on quantified changes in metabolic states. This optimization process involves "dynamically revising" recommendations (changing the regimen) when the system detects that health is not improving as expected. Because these refinements are informed by a model trained on "population-level data" (group users) and updated "continuously," the act of recommending a change based on group-level long-term outcomes is fully described.
Note: Claims 16-20 are rejected with claims 6-10 analysis above for being very similar.
Conclusion
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/ Supervisory Patent Examiner, Art Unit 3684