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 1-26 are pending.
Priority
This application is a continuation in part of U.S. Application No. 18/646,596, filed April 25, 2024, which is a continuation of U.S. Application No. 16/926,510, filed July 10, 2020, now issued as U.S. Patent No. 12,033,761, on July 9, 2024, which claims the benefit of priority of U.S. Provisional Application No. 62/968,086, filed January 30, 2020, U.S. Provisional Application No. 63/001,199, filed March 27, 2020, U.S. Provisional Application No. 63/002,257, filed March 30, 2020, and U.S. Provisional Application No. 63/032,450, filed May 29, 2020. Applicant' s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 18, 2025 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 1-26 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 1-17 are drawn to a method, and claims 18-26 are drawn to a system which is one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method comprising the following:
obtaining health data of a user, wherein the health data comprises a time series stream of health events of the user;
determining a trajectory of the user in a latent health space based at least in part on the time series stream of the health events of the user;
standardizing the health data of the user to generate standardized health data of the target user, wherein said standardizing comprising transforming the health data based at least in part on one or more attributes;
selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space; and
causing initiation of the health intervention on behalf of the user via transmitting a notification of the target user.
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 interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, 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).
Independent claim 5 recites a method comprising the following:
accessing a set of training data for a plurality of users of a population, the training data representative of physical statistics and symptoms for the plurality of users for each of a plurality of time periods;
predict, for a first acute health condition, acute health condition onset for a user based on physical statistics of the user, the physical statistics comprising data corresponding to (i) a weather condition corresponding to the user, (ii) a planned event corresponding to the user, and a (iii) a geographical location corresponding to the user; and
in response to determining a probability of acute health condition onset for a user exceeds a threshold, performing one or more intervention actions on behalf of the target user.
These steps are directed to predicting the acute health condition onset for a user, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, 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).
Independent claims 8 and 18 recite a method and a system comprising the following:
obtaining data comprising a plurality of time series measurements collected from a target user during a first time period; and
predicting a recovery trajectory from an acute condition or debilitating event at least in part by processing the data comprising the plurality of time series measurements from the first time period, wherein the classifier is trained at least in part by: using a set of training data from a first population cohort comprising a plurality of users, the set of training data comprising 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, data from the first user of the plurality of users comprising physical statistics and symptoms collected over a plurality of time periods and associated with the acute condition or debilitating event, wherein at least one of the consecutive time periods is prior to an onset of the acute condition or debilitating event and at least one of the consecutive time periods is after the onset of the acute condition or debilitating event, wherein the first user is placed in the first population cohort based at least in part on a pattern of the data from the first user.
These steps are directed to predicting a recovery period from an acute condition or debilitating event of a user, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, 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).
Dependent claim 2 recites, in part, wherein the health data of the user comprises one or both of behavior data or medical data.
Dependent claim 3 recites, in part, wherein the time series stream of the health events of the user comprises a plurality of physical statistics data of the user over a plurality of time periods.
Dependent claims 9 and 19 recite, in part, wherein the time series measurements correspond to at least one of sleep efficiency, step count, or heart rate.
Dependent claims 10 and 20 recite, in part, wherein the wherein the time series measurements correspond to at least two of sleep efficiency, step count, and heart rate.
Dependent claims 11 and 21 recite, in part, wherein the wearable sensor data is collected daily throughout the first time period.
Dependent claims 14 and 24 recite, in part, further comprising initiating a health intervention on behalf of the target user.
Dependent claims 17 and 27 recite, in part, wherein the acute or debilitating event is a surgery.
Each of these steps of the preceding dependent claims 2-4, 6, 7, 9-17, and 19-26 only serve to further limit or specify the features of independent claims 1, 5, 8, and 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.
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 1 recites, in part, a wearable sensor, a user device comprising the wearable sensor, and training using the accessed set of training data. The specification defines a wearable sensor as a wearable device may comprise one or more sensors to measure physical attributes of a human subject, (Specification in ¶ 00124), a user device comprising the wearable sensor as the wearable device 1810 may comprise one or more wearable device sensors (also referred to herein as "sensors") for collecting patient health data and may include a capability to connect to a network (e.g., the network 1830) to transfer the sensor data to other components of the system, (Specification in ¶ 00136), and training using the accessed set of training data as generating AHC impact models that can be used by the machine learning prediction system 110 to estimate the burden of the associated AHC on a population or to predict the onset of the AHC in an individual (Specification in ¶ 0081). The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Independent Claim 5 recites, in part, the machine learning prediction system, a wearable sensor, and a machine learning model. The specification defines the machine learning prediction system as can learn individual acute health condition patterns (and, in some embodiments, train a machine learning model to predict acute health condition impact) by analyzing differences in user activity with respect to the characteristics of the determined baseline periods (Specification in ¶ 0009), a wearable sensor as a wearable device may comprise one or more sensors to measure physical attributes of a human subject, (Specification in ¶ 00124), and a machine learning model to perform analysis of wearable data received from the subject, (Specification in ¶ 00138). The limitation of a wearable sensor is only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception. The limitations of the machine learning prediction system and a machine learning model are only recited as a tool to apply data to an algorithm and report 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 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).
Independent Claim 8 recites, in part, a wearable sensor and a machine learning model. The specification defines a wearable sensor as a wearable device may comprise one or more sensors to measure physical attributes of a human subject, (Specification in ¶ 00124), and a machine learning model to perform analysis of wearable data received from the subject, (Specification in ¶ 00138). The limitation of a wearable sensor is only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception. The limitations of a machine learning model are only recited as a tool to apply data to an algorithm and report 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 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).
Independent Claim 18 recites, in part, one or more processors, one or more memories, a wearable sensor, and a machine learning model. The specification defines one or more processors as a central processing unit (CPU, also "processor" and "computer processor" herein) 2105, which can be a single core or multi core processor, or a plurality of processors for parallel processing (Specification in ¶ 00240), one or more memories as e.g., read-only memory, random-access memory, flash memory) or a hard disk (Specification in ¶ 00247), a wearable sensor as a wearable device may comprise one or more sensors to measure physical attributes of a human subject, (Specification in ¶ 00124), and a machine learning model to perform analysis of wearable data received from the subject, (Specification in ¶ 00138). The limitation of a wearable sensor is only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception. The limitations of a machine learning model are only recited as a tool to apply data to an algorithm and report 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 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). The limitations of a processor and a memory are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claim 4 recites, in part, the user device comprises a wrist-adapter to reversibly attach to the wrist of the target user. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claims 6, 12, 13, 22, and 23, recite in part, a machine learning model, and/or a decision tree algorithm, and/or a random forest, and/or a classifier. These limitations are only recited as a tool to apply data to an algorithm and report 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 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).
Dependent claim 7 recites in part, wherein the physical statistics of the user are obtained via a smartwatch worn by the user. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claims 11 and 21, recite in part, a wearable sensor. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claims 15 and 25, recite in part, 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. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claims 16 and 26, recite in part, wherein initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user. The limitations are only recited as a tool which only serves as extra solution activities incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
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.
Independent Claim 1 recites, in part, a wearable sensor, a user device comprising the wearable sensor, and training using the accessed set of training data. Independent Claim 5 recites, in part, the machine learning prediction system, a wearable sensor, and a machine learning model. Independent Claim 8 recites, in part, a wearable sensor and a machine learning model. Independent Claim 18 recites, in part, one or more processors, one or more memories, a wearable sensor, and a machine learning model. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of a wearable sensor to obtain data, use of a user device to obtain and transmit data and training using the accessed set of training data to train the machine learning model and apply an algorithm. Use of the machine learning prediction system to access data and a machine learning model to determine a probability of an acute health condition and to apply an algorithm. Use of the one or more processors to process data and one or more memories to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claim 4 recites, in part, the user device comprises a wrist-adapter to reversibly attach to the wrist of the target user. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claims 6, 12, 13, 22, and 23, recite in part, a machine learning model, and/or a decision tree algorithm, and/or a random forest, and/or a classifier. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claim 7 recites in part, wherein the physical statistics of the user are obtained via a smartwatch worn by the user and as recited in the specification at para. [0067], “A health sensor 125 can be a wearable device or other device capable of providing physical statistics about the user 120. For example, a health sensor 125 can be a dedicated fitness tracker, a pedometer, a sleep tracker, a smart watch, smartphone, or mobile device with physical statistic monitoring functionality”, which is well-understood routine and conventional. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claims 11 and 21, recite in part, a wearable sensor and as recited in the specification at para. [0067], “A health sensor 125 can be a wearable device or other device capable of providing physical statistics about the user 120. For example, a health sensor 125 can be a dedicated fitness tracker, a pedometer, a sleep tracker, a smart watch, smartphone, or mobile device with physical statistic monitoring functionality”, which is well-understood routine and conventional. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claims 15 and 25, recite in part, 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. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claims 16 and 26, recite in part, wherein initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user. The additional element of claims 16 and 26 includes wherein initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user, which is well-understood, routine, and conventional. This position is supported by Applicant’s specification at para. [00115], “"testing at home" technology can be used to mail test kits to user 120 to take a diagnostic test for the ILI in addition to "point of care testing" performed at a healthcare facility or other specific site”. Therefore, the initiating the health intervention further comprises causing a test kit corresponding to the acute health condition to be sent to the target user, this additional element is not sufficient to amount to significantly more than the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-26 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 8-15, and 18-25 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 8, Moturu discloses a method comprising:
(a) obtaining wearable sensor data comprising a plurality of 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); and
(b) predicting, via a machine learning model, a recovery trajectory from an acute condition or debilitating event at least in part by processing the wearable sensor data comprising the plurality of time series measurements from the first time period, (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 classifier is trained at least in part by:
using a set of training data from a first population cohort comprising a plurality of users, the set of training data comprising 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, wearable sensor data from the first user of the plurality of users comprising physical statistics and symptoms collected over a plurality of time periods and associated with the acute condition or debilitating event, wherein at least one of the consecutive time periods is prior to an onset of the acute condition or debilitating event and at least one of the consecutive time periods is after the onset of the acute condition or debilitating event, wherein the first user is placed in the first population cohort based at least in part on a pattern of the wearable sensor data from the first user, (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 9, Moturu discloses the method of claim 8 as described above. Moturu further discloses wherein the time series measurements correspond to at least one of sleep efficiency, step count, or heart rate, (para. 51, measures indicative of atherosclerosis or other cardiovascular disease, heartbeat metric (e.g., instantaneous heart rate, heart rate variability, average heart rate, resting heart rate, heartbeat signature, etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.), physical activity metric (e.g., motion metrics, fitness metrics, etc.), metrics correlated with cardiovascular health (e.g., sleep metrics, etc.), vital signs, pulse oximetry metric, measures of blood vessel stiffness, respiration metric (e.g., respiratory rate, respiratory patterns, etc.), and/or any other suitable metric relating to cardiovascular health.).
Regarding claim 10, Moturu discloses the method of claim 8 as described above. Moturu further discloses wherein the wherein the time series measurements correspond to at least two of sleep efficiency, step count, and heart rate, (para. 51, the cardiovascular health metric preferably indicates the cardiovascular health of the patient during the time period, but can indicate cardiovascular health of any suitable individual during any suitable time. Additionally or alternatively, the cardiovascular health can include any one or more of a: blood pressure metric (e.g., instantaneous blood pressure, blood pressure variability, etc.), measures indicative of atherosclerosis or other cardiovascular disease, heartbeat metric (e.g., instantaneous heart rate, heart rate variability, average heart rate, resting heart rate, heartbeat signature, etc.), pulse rate metric (e.g., instantaneous pulse rate, pulse rate variability, etc.), physical activity metric (e.g., motion metrics, fitness metrics, etc.), metrics correlated with cardiovascular health (e.g., sleep metrics, etc.), vital signs, pulse oximetry metric, measures of blood vessel stiffness, respiration metric (e.g., respiratory rate, respiratory patterns, etc.), and/or any other suitable metric relating to cardiovascular health).
Regarding claim 11, Moturu discloses the method of claim 8 as described above. Moturu further discloses wherein the 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).
Regarding claim 12, Moturu discloses the method of claim 8 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 13, Moturu discloses the method of claim 8 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 14, Moturu discloses the method of claim 8 as described above. Moturu further discloses further comprising 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 15, Moturu discloses the method of claims 8 and 14 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 17, Moturu discloses the method of claim 8 as described above. Moturu further discloses wherein the acute or debilitating event is a surgery, (para. 41. patient is undergoing suprainguinal vascular, intraperitoneal, or intrathoracic surgery).
Regarding claims 18-25, these claims are rejected for the same reasons as set forth above with regard to claims 8-15. Moturu further discloses 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, (para. 140, The instructions can be executed by computer-executable components integrated by computer-executable components integrated with apparatuses and networks of the type described above. The computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component can be a processor, though any suitable dedicated hardware device can (alternatively or additionally) execute the instructions).
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.
Claims 1-4 and 6 are 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 2020/0312456, Yadid-Pecht, et al., hereinafter Yadid-Pecht.
Regarding claim 1, Moturu discloses a method for performing a health intervention, comprising:
(a) obtaining, from a wearable sensor, health data of a user, wherein the health data comprises a time series stream of health events of the user, (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);
(b) determining a trajectory of the user in a latent health space based at least in part on the time series stream of the health events of the user, (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.);
(d) selecting a health intervention for the user based at least in part on the trajectory of the user in the latent health space, (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.); and
(e) causing initiation of the health intervention on behalf of the user via transmitting a notification to a user device of the target user, wherein the user device comprises the wearable sensor, (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).
Moturu does not explicitly disclose the following:
(c) standardizing the health data of the user to generate standardized health data of the target user, wherein said standardizing comprising transforming the health data based at least in part on one or more attributes of the wearable sensor.
However, Yadid-Pecht teaches (c) standardizing the health data of the user to generate standardized health data of the target user, wherein said standardizing comprising transforming the health data based at least in part on one or more attributes of the wearable sensor, (para. 34, the data may be input to the system by various means including automatic data input using various devices (such as wearable devices) that automatically collect data and para. 77, Medical and activity data 214 and 216 gathered from various data-input sources 106 may have different formats. More importantly, medical and activity data 214 and 216 may have multiple missing data-points which may make the neural-network training impossible. Therefore, it is important to convert all input data into a standard format and supplement missing data-points);
One having ordinary skill in the art at the time the invention was filed would combine the therapeutic intervention system of Moturu with the machine learning based medical analysis system of Yadid-Pecht with the motivation of analyzing users' health conditions and give treatment suggestions (Yadid-Pecht, para. 3).
Regarding claim 2, Moturu discloses the method of claim 1 as described above. Moturu further discloses wherein the health data of the user comprises one or both of behavior data or medical data, (para. 33, data received in Block S110 and S120 can be processed to track behavior characteristics of the patient).
Regarding claim 3, Moturu discloses the method of claim 1 as described above. Moturu further discloses wherein the time series stream of the health events of the user comprises a plurality of physical statistics data of the user over a plurality of time periods, (para. 33, receiving a supplementary dataset associated with the time period, which functions to unobtrusively receive non-communication-related data from a patient's mobile computing device and/or other device configured to receive contextual data from the patient. Block S120 can include receiving non-communication-related data pertaining to the patient before, during, and/or after (or in the absence of) communication with another individual (e.g., a phone call) and/or computer network (e.g., a social networking application), as described above in relation to Block S110. Block S120 can include receiving one or more of: location information, movement information (e.g., related to physical isolation, related to lethargy), device usage information (e.g., screen usage information related to disturbed sleep, restlessness, and/or interest in mobile device activities), and any other suitable information. In variations, Block S120 can include receiving location information of the patient by way of one or more of: receiving a GPS location of the individual (e.g., from a GPS sensor within the mobile communication device of the patient), estimating the location of the patient through triangulation (e.g., triangulation of local cellular towers in communication with the mobile communication device), identifying a geo-located local Wi-Fi hotspot during a phone call, and in any other suitable manner. In applications, data received in Block S110 and S120 can be processed to track behavior characteristics of the patient, such as mobility, periods of isolation, quality of life (e.g., work-life balance based on time spent at specific locations), and any other location-derived behavior information).
Regarding claim 4, Moturu discloses the method of claim 1 as described above. Moturu further discloses wherein the user device comprises a wrist-adapter to reversibly attach to the wrist of the target user, (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.) ).
Regarding claim 6, Moturu discloses the method of claim 5 as described above. Moturu further discloses wherein the machine learning model comprises one or more of a decision tree algorithm or a random forest, (para. 64, generating a cardiovascular health metric includes generating a decision tree model with internal nodes and branches selected based on correlations between cardiovascular health and digital communication behavior, supplementary data, and/or survey data, where generating the cardiovascular health metric is based on the correlations.).
Regarding claim 7, Moturu discloses the method of claim 1 as described above. Moturu further discloses wherein the physical statistics of the user are obtained via a smartwatch worn by the user, (para. 19, Such digital communication data can be collected from a patient's mobile phone, smart watch, laptop, and/or other suitable patient device. Additionally or alternatively, the technology can augment the cardiovascular health monitoring process with passively collected supplementary data (e.g., location data, cardiovascular device data and/or actively collected data (e.g., responses to digital surveys).).
Claim 5 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 2017/0235912, Moturu, et al., hereinafter Moturu(2).
Regarding claim 5, Moturu in view of discloses a method for training a machine learning prediction system, comprising:
(a) accessing, by the machine learning prediction system, a set of training data for a plurality of users of a population, the training data representative of physical statistics and symptoms for the plurality of users for each of a plurality of time periods, the training data collected by a wearable sensor, (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);
(b) training, by the machine learning prediction system, a machine learning model using the accessed set of training data, the machine learning model configured to predict, for a first acute health condition, acute health condition onset for a user based on physical statistics of the user, the physical statistics comprising data corresponding to (i) a weather condition corresponding to the user, (ii) a planned event corresponding to the user, and a (iii) a geographical location corresponding to the user, (para. 36, local environmental data (e.g., climate data, temperature data, light parameter data, etc.), para. 59, physical activity level can be inferred from motion sensor data (e.g., steps taken, distance traveled, etc.), location data (e.g., GPS tracking, etc.), and/or other suitable mobility behavior data, para. 61, event-related data can be used in any manner for determining a cardiovascular health metric, para. 62, a set of photos captured by the user during a time period can be analyzed to determine the scenes (e.g., nature, inside a building, outside a building, daytime, nighttime, etc.) in which the photos were taken. The presence or frequency of particular scene types can be correlated with cardiovascular health. In a specific example, a high proportion of nature photographs can indicate a calming experience involving physical activity, and consequently can be correlated with an improvement in cardiovascular health. However, media data can be used in any manner for determining a cardiovascular health metric, 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).
Moturu does not explicitly disclose the following:
(c) in response to determining, via the machine learning model, a probability of acute health condition onset for a user exceeds a threshold, performing one or more intervention actions on behalf of the target user.
However, Moturu(2) teaches (c) in response to determining, via the machine learning model, a probability of acute health condition onset for a user exceeds a threshold, performing one or more intervention actions on behalf of the target user, (para. 19, computational modeling of diagnosis and treatment determination, para. 55, A diagnosis can include one or more of: a risk and/or severity value indicating a probability of a patient being afflicted with a given condition, general mental or physiological health indicators, para. 62, Block S140 can include automatically promoting medical status analyses based on satisfaction of a threshold condition (e.g., a risk of depression exceeding a risk threshold). Threshold conditions can be based on diagnoses (e.g., risk thresholds, severity thresholds, type of user condition thresholds, etc.), therapeutic interventions (e.g., type of therapeutic intervention, urgency associated with a patient needing to start a medication regimen, etc.), data collected in Blocks S110-S125, and/or any other suitable information, and para. 66, a treatment system 220 operable to automatically promote a medical status analysis (e.g., to a care provider) associated with the condition and/or user and para. 68, the treatment system 220 can be operable to generate, transmit, and/or apply control instructions for operating a supplemental device 230 and/or other suitable device (e.g., a medical device of the treatment system 220) to promote the medical status analyses (e.g., controlling a user device administer a guided meditation to the user, etc.).)
One having ordinary skill in the art at the time the invention was filed would combine the therapeutic intervention system of Moturu with the method and system for improving care determination of Moturu(2) with the motivation of diagnosing patient conditions and prescribing effective treatments (Moturu(2) para. 18).
Claims 16 and 26 are 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 claims 16 and 26, Moturu discloses the method of claim 8 and system of claim 18 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SMART DEVICE (US 20180001184 A1) teaches recommending lifestyle modification for a subject by using a DNA sequencer to generate genetic information; aggregating genetic information, environmental information, treatment data, and treatment response from a patient population.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682