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
Claims 1-17 and 19-20 have been canceled. Claims 18, 21, and 23-26 have been amended. Claims 27-39 are new. Now, claims 18 and 21-39 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 15, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 18 and 21-39 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 18 and 21-39 are drawn to a system which is one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 18 recites a system comprising the following:
configured to capture cardiovascular data;
configured to capture activity data;
to perform operations comprising:
(a) obtaining data comprising cardiovascular data comprising a plurality of first time series measurements collected from a target user during a first time period;
(b) obtaining data comprising activity data comprising a plurality of second time series measurements collected from the target user during the first time period; and
(c) predicting a recovery trajectory from an acute condition or debilitating event at least in part by processing the first data and the second data from the first time period, wherein the recovery trajectory comprises a predicted recovery time, wherein the at least in part by:
using a first population cohort comprising a plurality of users, wherein comprises a confirmed case of the acute condition or debilitating event of a first user of the plurality of users and data from the plurality of users, wherein the data from the first user of the plurality of users comprises cardiovascular data and activity data associated with the acute condition or debilitating event, and wherein the first population cohort is selected based at least in part on a temporal pattern of the data from the plurality of users.
These steps are directed to determining and initiating a health intervention for a user, which amounts to certain methods of organizing human activity which includes functions relating to managing relationships or transactions between people, and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Step 2A – Judicial Exception Analysis, Prong 2:
This 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)].
Independent Claim 18 does recite additional elements:
a first wearable sensor configured to capture cardiovascular data;
a second wearable sensor configured to capture activity data;
one or more processors; and
one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to perform operations;
via a machine learning model;
wherein the machine learning model is trained.
These additional elements merely amount to the general application of the abstract idea to a technological environment (“a first wearable sensor configured to capture cardiovascular data”; “a second wearable sensor configured to capture activity data”; “one or more processors”; and “one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to perform operations”; “via a machine learning model”; “wherein the machine learning model is trained”.) and insignificant pre-and-post solution activity (obtaining, predicting, and using). The specification makes clear the general-purpose nature of the technological environment. Paragraphs 60 and 63-68, indicate that while exemplary general purpose systems may be specific for descriptive purposes, any elements or combinations of elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim.
Therefore, considered both individually and as an ordered combination, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional limitations are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea. Accordingly, the Examiner concludes that the claim fails to integrate the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
Under step 2B of the Alice/Mayo framework, it must finally be considered whether the claim includes any additional element or combination of elements that provide an inventive concept (i.e., whether the additional element or elements are sufficient to amount to significantly more than the abstract idea). As indicated above, considered both individually and as an ordered combination, the additional elements do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea
Further, the additional elements (recited above) simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Communicating information (i.e., receiving or transmitting data over a network) has been repeatedly considered well-understood, routine, and conventional activity by the Courts (See MPEP 2106.05(d)). Accordingly, the Examiner asserts that the additional elements, considered both individually, and as an ordered combination, do not provide an inventive concept, and the claim is ineligible for patent.
Dependent claims:
Each of these steps of the dependent claims 21-39 only serve to further limit or specify the features of independent claim 18 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Regarding Claim 21
Claim 21 sets forth:
wherein the first data or the second data is collected daily throughout the first time period.
Such a recitation merely embellishes the abstract idea of determining and initiating a health intervention for a user, including facilitating managing relationships or transactions between people, and human behavior. While the claim does set forth the additional limitation of “wearable sensor”, this recitation is similar to the additional limitations in claim 1, as it does no more than generally link the use of the abstract idea to a particular technological environment. As such, it does not integrate the abstract idea into a practical application, and does not provide an inventive concept. Accordingly, the claim does not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 102
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 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.
18, 21-25, and 27-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by United States Patent Application Publication Number 2017/0000422, Moturu, et al., hereinafter Moturu.
Regarding claim 18, Moturu discloses a system comprising:
a first wearable sensor configured to capture cardiovascular data;
a second wearable sensor configured to capture activity data;
one or more processors; and one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
obtaining, by the first wearable sensor, first wearable sensor data comprising cardiovascular data comprising a plurality of first time series measurements collected from a target user during a first time period, (para. 19, the technology can output accurate measurements of cardiovascular health while requiring a minimal or otherwise reduced amount of effort by a patient, and para. 28, the data collection application can launch on the patient's mobile communication device as a background process that gathers patient data and para. 43, the set of time points can include regularly-spaced time points (e.g., time points spaced apart by an hour, by a day, by a week, by a month, etc.) with a suitable resolution for enabling detection of changes in a cardiovascular state of the patient);
obtaining, by the second wearable sensor, second wearable sensor data comprising activity data comprising a plurality of second time series measurements collected from the target user during the first time period; and
(c) predicting, via a machine learning model, a recovery trajectory from an acute condition or debilitating event at least in part by processing the first wearable sensor data and the second wearable sensor data from the first time period, wherein the recovery trajectory comprises a predicted recovery time, (para. 50, Block S130 thus enables assessment of a past or current cardiovascular of the patient and/or predicts risk that the patient will trend toward a different (e.g., worsened, improved, etc.) cardiovascular state at a future time point), wherein the machine learning model is trained at least in part by:
using a set of training data from a first population cohort comprising a plurality of users, wherein the set of training data comprises a confirmed case of the acute condition or debilitating event of a first user of the plurality of users and wearable sensor data from the plurality of users, wherein the wearable sensor data from the first user of the plurality of users comprises cardiovascular data and activity data associated with the acute condition or debilitating event, and wherein the first population cohort is selected based at least in part on a temporal pattern of the wearable sensor data from the plurality of users, (para. 69, Training data can be labeled with cardiovascular disease risk classification (e.g., high risk, at-risk, optimal risk, etc.), values of a cardiovascular health metric, risk factors (e.g., from a cardiac risk index), fitness level, recommended therapeutic intervention (e.g., therapeutic interventions with positive treatment responses for certain reference profiles), and/or any other suitable label related to cardiovascular health, and para. 76, the method 100 can include forming a subgroup of patients based on medical status (e.g., symptoms, cardiovascular diseases, treatments, medication regimens, etc.); generating a cardiovascular health predictive model from training data (e.g., log of use data, supplementary data, survey data, etc.) associated with patients from the subgroup; and extracting a cardiovascular health parameter from an output of the cardiovascular health predictive model…. the method 100 can include assigning the patient to a cardiovascular subgroup of a set of cardiovascular subgroups, based on a survey dataset including a patient response to a the cardiovascular evaluation digital survey; and retrieving a subgroup predictive model corresponding to the cardiovascular subgroup, wherein generating the cardiovascular health metric is in response to retrieving the subgroup predictive model, and wherein the cardiovascular health predictive model is the subgroup predictive model).
Regarding claim 21, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the first wearable sensor data or the second wearable sensor data is collected daily throughout the first time period, (para. 28, a mobile communication device (e.g., smartphone, tablet, personal data assistant (PDA), personal music player, vehicle, head-mounted wearable computing device, wrist-mounted wearable computing device, etc……the mobile communication device can then upload this data to a database (e.g., remote server, cloud computing system, storage module), at a desired frequency (e.g., in near real-time, every hour, at the end of each day, etc.) to be accessed by the computing system and para. 36, biometric data (e.g., data recorded through sensors within the patient's mobile communication device, data recorded through a wearable or other peripheral device in communication with the patient's mobile communication device) of the patient,).
Regarding claim 22, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the machine learning model comprises a classifier, (para. 69, a cardiovascular health predictive model preferably uses one or more machine learning techniques and training data (e.g., from the patient, from a population of patients), data mining, and/or statistical approaches to generate more accurate models pertaining to the patient's cardiovascular states (e.g., over time, with aggregation of more data). For example, Block S140 can include generating a cardiovascular health predictive model based upon a log of use dataset and a mobility behavior supplementary dataset. Training data can be labeled with cardiovascular disease risk classification (e.g., high risk, at-risk, optimal risk, etc.), values of a cardiovascular health metric and para. 77, the machine learning algorithm can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.),).
Regarding claim 23, Moturu discloses the system of claims 18 and 22 as described above. Moturu further discloses wherein the classifier comprises one or more of a decision tree algorithm or a random forest, (para. 77, the machine learning algorithm can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree).
Regarding claim 24, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the operations further comprise initiating a health intervention on behalf of the target user, (para. 15, generating a cardiovascular health predictive model based upon at least one of the log of use dataset, the supplementary dataset, and the survey dataset S140; extracting a cardiovascular health metric from at least one of an output of the cardiovascular health predictive model, the log of use dataset, the supplementary dataset, and the survey dataset, wherein the cardiovascular health metric is associated with the time period S130; providing a cardiovascular-related notification to the patient S150; and automatically providing a cardiovascular therapeutic intervention for the patient S160).
Regarding claim 25, Moturu discloses the system of claims 18 and 24 as described above. Moturu further discloses wherein initiating the health intervention further comprises causing modification of the interface displayed by the user device of the target user to display a notification configured to change a behavior of the target user, (para. 104, the method 100 can include providing a cardiovascular therapeutic intervention in the form of an audio therapy configured to improve the cardiovascular health metric, and wherein providing the cardiovascular therapeutic intervention includes controlling the mobile computing device to emit the audio therapy. In another example, Block S160 can include transforming a digital user interface associated with the patient mobile computing device. Transforming the digital user interface can include one or more of: modifying a color scheme, modifying a lighting parameter, highlighting a cardiovascular-related notification, highlighting options to initiate a particular cardiovascular therapeutic intervention, and/or any other suitable modifications.).
Regarding claim 27, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the cardiovascular data comprises heart rate data, respiration data, blood oxygen data, blood pressure data, or any combination thereof, (para. 51, heartbeat metric (e.g., instantaneous heart rate, heart rate variability, average heart rate, resting heart rate, heartbeat signature, etc.)).
Regarding claim 28, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the activity data comprises step count data, (para. 59, physical activity level can be inferred from motion sensor data (e.g., steps taken, distance traveled, etc.)).
Regarding claim 29, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the operations further comprise obtaining sleep efficiency data of the target user during the first time period, (para. 35, can therefore facilitate tracking of variations and periods of activity/inactivity for a patient through automatically collected data (e.g., from the patient's mobile communication device), in order to enable identification of periods of activity and inactivity by the patient (e.g., extended periods when the individual was hyperactive on the device or not asleep), and para. 51, metrics correlated with cardiovascular health (e.g., sleep metrics, etc.)).
Regarding claim 30, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the operations further comprise obtaining cardiovascular data and activity data collected from the target user during a second time period, (para. 89, a first passive component (e.g., related to communication behavior) generated from behavior analyzed over a first duration of time, a second passive behavioral component (e.g., related to mobility of the individual) generated from behavior analyzed over a second duration of time overlapping with the first duration of time, scoring of a survey, and a predictive model component for third duration of time (e.g., overlapping with the period of the survey), wherein the predictive model component implements an aggregated learning approach based upon multiple individual models (e.g., each assessing different parameters and/or different time periods of patient behavior)).
Regarding claim 31, Moturu discloses the system of claim 18 and 30 as described above. Moturu further discloses wherein the first time period is prior to an onset of the acute condition or debilitating event, and wherein the second time period is after the onset of the acute condition or debilitating event, (para. 69, Training data can be labeled with cardiovascular disease risk classification (e.g., high risk, at-risk, optimal risk, etc.), values of a cardiovascular health metric, risk factors (e.g., from a cardiac risk index), fitness level, recommended therapeutic intervention (e.g., therapeutic interventions with positive treatment responses for certain reference profiles), and/or any other suitable label related to cardiovascular health, and para. 76, the method 100 can include forming a subgroup of patients based on medical status (e.g., symptoms, cardiovascular diseases, treatments, medication regimens, etc.); generating a cardiovascular health predictive model from training data (e.g., log of use data, supplementary data, survey data, etc.) associated with patients from the subgroup; and extracting a cardiovascular health parameter from an output of the cardiovascular health predictive model…. the method 100 can include assigning the patient to a cardiovascular subgroup of a set of cardiovascular subgroups, based on a survey dataset including a patient response to a the cardiovascular evaluation digital survey; and retrieving a subgroup predictive model corresponding to the cardiovascular subgroup, wherein generating the cardiovascular health metric is in response to retrieving the subgroup predictive model, and wherein the cardiovascular health predictive model is the subgroup predictive model).
Regarding claim 32, Moturu discloses the system of claim 18 and 30-31 as described above. Moturu further discloses wherein the operations further comprise determining a personal baseline for the target user, based at least in part on the first wearable sensor data or the second wearable sensor data, (para. 65, extracting a cardiovascular health metric can be performed automatically at specified time intervals (e.g., every hour, day, week, etc.), in response to satisfaction of a condition (e.g., a user request for a cardiovascular evaluation, a log of use dataset indicating abnormal digital communication behavior, a supplementary dataset indicating irregular mobility behavior, a survey dataset indicating poor dietary habits, etc.), and/or performed at any suitable time. In a variation, Block S130 can be performed in response to satisfaction of a data collection threshold, and para. 85, threshold conditions can be defined in relation to a baseline for each patient, as determined from historical behavior of the patient.)
Regarding claim 33, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the acute condition or debilitating event comprises a surgery, (para. 41. patient is undergoing suprainguinal vascular, intraperitoneal, or intrathoracic surgery).
Regarding claim 34, Moturu discloses the system of claim 18 and 33 as described above. Moturu further discloses wherein the acute condition or debilitating event comprises an illness or an injury, (para. 17, the method 100 can be used to monitor and/or treat cardiovascular disease patients who are suffering from and/or at-risk for any one or more of: rheumatic heart disease, hypertensive heart disease, coronary artery disease, congestive heart failure, cerebrovascular disease, inflammatory heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, venous thrombosis, myocardial infarction, angina, aneurysm, hypertension, atherosclerosis, stroke, transient ischemic attacks, pericardial disease, and/or any other suitable cardiovascular condition. Additionally or alternatively, the method 100 can enable evaluation of patients suffering from cardiovascular disease-related symptoms including any one or more of: aching, breathlessness, burning, cramping, discomfort, fullness, heaviness, indigestion, lightheadedness, nausea, numbness, tinging, pain, pressure, shortness of breath, sweating, dizziness, squeezing, tightness, vomiting, irregular heartbeat, palpitations, chest pounding, fatigue, weakness, and/or any other suitable symptoms).
Regarding claim 35, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the wearable sensor data from the first user is collected in a plurality of time periods, wherein at least one of the plurality of time periods is prior to an onset of the confirmed case of the acute condition or debilitating event and at least one of the plurality of time periods is after the onset of the confirmed case of the acute condition or debilitating event, (para. 69, Training data can be labeled with cardiovascular disease risk classification (e.g., high risk, at-risk, optimal risk, etc.), values of a cardiovascular health metric, risk factors (e.g., from a cardiac risk index), fitness level, recommended therapeutic intervention (e.g., therapeutic interventions with positive treatment responses for certain reference profiles), and/or any other suitable label related to cardiovascular health, and para. 76, the method 100 can include forming a subgroup of patients based on medical status (e.g., symptoms, cardiovascular diseases, treatments, medication regimens, etc.); generating a cardiovascular health predictive model from training data (e.g., log of use data, supplementary data, survey data, etc.) associated with patients from the subgroup; and extracting a cardiovascular health parameter from an output of the cardiovascular health predictive model…. the method 100 can include assigning the patient to a cardiovascular subgroup of a set of cardiovascular subgroups, based on a survey dataset including a patient response to a the cardiovascular evaluation digital survey; and retrieving a subgroup predictive model corresponding to the cardiovascular subgroup, wherein generating the cardiovascular health metric is in response to retrieving the subgroup predictive model, and wherein the cardiovascular health predictive model is the subgroup predictive model).
Regarding claim 36, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the temporal pattern comprises a step count data density, (para. 59, physical activity level can be inferred from motion sensor data (e.g., steps taken, distance traveled, etc.)).
Regarding claim 37, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein predicting the recovery trajectory comprises generating a ranking for the target user relative to the first population cohort, and predicting the recovery trajectory based at least in part on the ranking, (para. 69, Training data can be labeled with cardiovascular disease risk classification (e.g., high risk, at-risk, optimal risk, etc.), values of a cardiovascular health metric, risk factors (e.g., from a cardiac risk index), fitness level, recommended therapeutic intervention (e.g., therapeutic interventions with positive treatment responses for certain reference profiles), and/or any other suitable label related to cardiovascular health, para. 75, the predictive model can incorporate historical data from the patient (e.g., survey responses from a prior week, a history of passive data from the log of use, etc.), with more weight placed upon more recent data, and para. 76, the method 100 can include forming a subgroup of patients based on medical status (e.g., symptoms, cardiovascular diseases, treatments, medication regimens, etc.); generating a cardiovascular health predictive model from training data (e.g., log of use data, supplementary data, survey data, etc.) associated with patients from the subgroup; and extracting a cardiovascular health parameter from an output of the cardiovascular health predictive model…. the method 100 can include assigning the patient to a cardiovascular subgroup of a set of cardiovascular subgroups, based on a survey dataset including a patient response to a the cardiovascular evaluation digital survey; and retrieving a subgroup predictive model corresponding to the cardiovascular subgroup, wherein generating the cardiovascular health metric is in response to retrieving the subgroup predictive model, and wherein the cardiovascular health predictive model is the subgroup predictive model).
Regarding claim 38, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the machine learning model is trained at least in part by receiving a self-reported recovery time from the first user and training the machine learning model to predict the self-reported recovery time, (para. 40, The survey dataset (e.g., patient can include interview and/or self-reported information from the patient. Furthermore, the survey dataset preferably includes quantitative data, but can additionally or alternatively include qualitative data pertaining to a cardiovascular state of the patient corresponding to at least a subset of the set of time points (e.g., a time period)).
Regarding claim 39, Moturu discloses the system of claim 18 as described above. Moturu further discloses wherein the operations further comprise adjusting the predicted recovery trajectory based at least in part on one or more of an age or a gender of the target user, (para. 40, demographic information (e.g., gender, age, weight, ethnicity, etc.) and para. 50, Block S130 thus enables assessment of a past or current cardiovascular of the patient and/or predicts risk that the patient will trend toward a different (e.g., worsened, improved, etc.) cardiovascular state at a future time point).
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 26 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2017/0000422, Moturu, et al., hereinafter Moturu in view of United States Patent Application Publication Number 2020/0185071, Luber, et al., hereinafter Luber.
Regarding claim 26, Moturu discloses the system of claims 18 and 24 as described above. Moturu does not explicitly disclose wherein initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user
However, Luber teaches wherein initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user, (para. 10, the request is authorized and the server processes the request based on commands associated with the user's profile, such commands may include delivery of a diagnostic test kit or a therapeutic to the user and para. 33, the command may initiate an order for an STI diagnostic test kit to be shipped to the user)
One having ordinary skill in the art at the time the invention was filed would combine the therapeutic intervention system of Moturu with the disease diagnostics and testing system of Luber with the motivation of providing accessibility of testing and therapy to a patient (Luber in the Summary para. 8).
Response to Arguments
Applicant's arguments filed April 16, 2026 have been fully considered but they are not persuasive.
As a preliminary matter, as indicated in both Examiner’s interview summary dated April 10, 2026 and Applicant’s interview summary dated April 16, 2026 no agreement was reached with respect to the proposed claim language. Accordingly, statements that indicate otherwise are not accurate. Such statements include:
“As a preliminary matter, and as agreed upon during the examiner interview of April 8, 2026, the sensors of amended claim 18 are analogous to the sensors of Thales in that both systems comprise two sensors.” (see page 11 of Applicant’s Remarks).
Applicant argues that the claimed hardware is analogous to the hardware of Thales.
In response, Examiner respectfully disagrees. In Thales, the inertial sensors disclosed in the '159 patent do not use the conventional approach of measuring inertial changes with respect to the earth. Id. at 7:12- 23. Instead, the platform (e.g., vehicle) inertial sensors directly measure the gravitational field in the platform frame. Id. at 7:12-49, fig. 3D. The object (e.g., helmet) inertial sensors then calculate position information relative to the frame of the moving platform. Id. at 7:41-67, 8:1-17, fig. 3D. By changing the reference frame, one can track the position and orientation of the object within the moving platform without input from a vehicle attitude reference system or calculating orientation or position of the moving platform itself. Id. at 8:34-41.
Thales talks about improvements over technology in how things are sensed, so a true technical improvement, the specific configuration of the sensors is what causes the technical improvement. In the instant application, the sensors measure physiological and behavioral changes associated with a health condition of a user. Thus, Examiner does not find that the claims in the instant application are analogous to In Thales.
Applicant argues that the claimed use of data is analogous to the use of data in Thales.
In response, Examiner respectfully disagrees. In Thales, the claims disclose a system using two inertial sensors—one on the moving object, one on the reference frame—to provide improved tracking accuracy. The inertial sensors disclosed in the '159 patent do not use the conventional approach of measuring inertial changes with respect to the earth. Id. at 7:12–23. Instead, the platform (e.g., vehicle) inertial sensors directly measure the gravitational field in the platform frame. Id. at 7:12–49, fig. 3D. The object (e.g., helmet) inertial sensors then calculate position information relative to the frame of the moving platform. Id. at 7:41–67, 8:1–17, fig. 3D. By changing the reference frame, one can track the position and orientation of the object within the moving platform without input from a vehicle attitude reference system or calculating orientation or position of the moving platform itself. Id. at 8:34–41.
In the instant application, the data is being used to predict or detect the affects of an acute health condition based on physical statistic data. The present claims have nothing to do tracking the position and orientation of the object within the moving platform without input from a vehicle attitude reference system or calculating orientation or position of the moving platform itself as claimed by Thales. Thus, Examiner does not find that the claims in the instant application are analogous to In Thales.
Applicant further argues that a finding of nonapplicability of Thales here treats Thales too narrowly.
In response, Examiner respectfully disagrees. The MPEP 2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022], states that the courts have found that improvements in technology beyond computer functionality may demonstrate patent eligibility. Examples that the courts have indicated may be sufficient to show an improvement in existing technology include: vii. Particular configuration of inertial sensors and a particular method of using the raw data from the sensors, Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017). In Thales, the courts found the manner in which the sensors were configured and the particular method of using the raw data from the sensor made the claims eligible. The sensors in the claims in the instant application were not invented by the applicant, they are generic wearable sensors, not being configured in a way that is sufficient to show an improvement to technology. Additionally, they are not using the data in a particular method that would be sufficient to show an improvement to technology. Thus, Examiner does not find that the claims in the instant application are analogous to In Thales.
Applicant argues that the claims are novel over Moturu and requests withdrawal of the 35 U.S.C. 102 rejections.
In response, Examiner respectfully disagrees. Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that the claims are non-obvious over Moturu in view of Luber and requests withdrawal of the 35 U.S.C. 103 rejections.
In response, Examiner respectfully disagrees. Applicant’s arguments with respect to claim 26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
ADDICTION TREATMENT AND MANAGEMENT (US 20230238140 A1) teaches importing all-source medical records for patients, as well as generating and storing models based on the medical records.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682
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