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 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 27, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: “feedback component” in claims 17 and 20.
For the purpose of examination, Examiner interprets the “feedback component” to be a software component enacted by the processor to perform the outlined functions, which aligns with [0071] of Applicant specification, which recites: “embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and/or any combination thereof. When implemented in software, firmware, middleware, scripting language, and/or microcode, the program code or code segments to perform the necessary tasks can be stored in a machine- readable medium such as a storage medium. A code segment or machine- executable instruction can represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and/or program statements.”
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-20). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis and is similar to system claim 14.
Claim 1:
A method for generating a clinical parameter for a user, the method comprising:
performing the following until a set of baseline user data is generated:
monitoring a first plurality of wellness-relevant parameters representing the user at a physiological sensing device over a defined period;
obtaining a second plurality of wellness-relevant parameters representing the user via a portable computing device;
retrieving a third plurality of wellness-relevant parameters representing the user from an electronic health records (EHR) system, the first plurality of wellness-relevant parameters, the second plurality of wellness-relevant parameters, and the third plurality of wellness-relevant parameters collectively forming a set of wellness-relevant parameters;
generating a set of aggregate parameters from the set of wellness- relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters; and
assigning a clinical parameter to the user via a predictive model according to a subset of the set of aggregate parameters;
determining a threshold value associated with the clinical parameter from the set of baseline user data;
obtaining a novel set of wellness-relevant parameters;
generating a novel set of aggregate parameters from the novel set of wellness-relevant parameters;
generating a novel clinical parameter via the predictive model, representing a current state of the user, from a subset of the novel set of aggregate parameters; and
providing an intervention to the user if the novel clinical parameter meets the determined threshold.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards generating a patient data baseline by obtaining patient wellness-relevant parameters, aggregating the obtained data, and assigning clinical parameters to the user and providing an intervention when analysis of new sets of data and parameters meet a determined threshold, which are typical human activities performed by medical providers monitoring their patients’ health over time.
The claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass “monitoring a first plurality of wellness-relevant parameters”, “obtaining a second plurality of wellness-relevant parameters”, “retrieving a third plurality of wellness-relevant parameters”, “generating a set of aggregate parameters”, “assigning a clinical parameter to the user”, “determining a threshold value”, “obtaining a novel set of wellness-relevant parameters”, “generating a novel set of aggregate parameters”, “generating a novel clinical parameter”, and “providing an intervention to the user”.
Accordingly, the claim recites at least one abstract idea.
Claim 14 is abstract for the same reasons as above.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
physiological sensing device (1,14); portable computing device (1,6,14,16); electronic health records (EHR) system (1,14); predictive model (1,7-9,11-12,14-15,20); network interface (4,14); anomaly detection model (7); training the predictive model (8); retraining the predictive model (8-9,20); reinforcement learning process (9); feature aggregator (14,19); intervention selector (14); recurrent neural network (15); psychosocial assessment application (7-8,16-17,20); feedback component (17,20); feature extractor (19)
Any computing devices that would be able to perform the method (portable computing device) and their associated software elements (feature aggregator, intervention selector, psychosocial assessment application, feedback component, feature extractor) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0023] of Applicant specification recites: “A "portable computing device," as used herein, is a computing device that can carried by the user, such as a smartphone, smart watch, tablet, notebook, and laptop, that can measure a wellness-relevant parameter either through sensors on the device or via interaction with the user.” No specific, technical improvements are being made to computing devices as a generic computing device is applied to perform the abstract idea of monitoring patient health and an insignificant extra-solution activity of gathering data; MPEP 2106.05(g).
Physiological sensing devices are also taught at a high level of generality. [0022] recites: “An "physiological sensing device," as used herein, is a device to measure one or more physiological parameters and/or biological rhythms. A physiological sensing device is often implanted, ingested, or wearable, although in some instances an off-body device can be used to capture physiological parameters.” No specific, technical improvements are being made to physiological sensing devices as any generic sensor device is applied to perform an insignificant extra-solution activity of gathering data.
Electronic health records (EHR) systems are also taught at a high level of generality. [0026] recites: “Additional parameters can be either retrieved from an electronic health records (EHR) interface and/or other available databases via a network interface 121 associated with the server 120.” No specific, technical improvements are being made to EHR systems as they are only applied to perform an insignificant extra-solution activity of gathering data.
Machine learning (predictive model, anomaly detection model, recurrent neural network) and its training (training the predictive model, retraining the predictive model, reinforcement learning process) are also taught at a high level of generality. [0024] recites: “As used herein, a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured.” [0045] further recites: “The predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical clinical parameter to the user. In one example, the predictive model 124 can assign a continuous parameter that corresponds to a likelihood that that user has or is about to contract a specific disease or disorder, a likelihood that the user is experiencing the effects of aging, a likelihood that the user is experiencing an onset of dementia, a likelihood that the user has or will develop a neurodegenerative disorder, a likelihood that the user will experience an intensifying of symptoms, or "flare-up," of a chronic condition, a likelihood that the user will use an addictive substance during rehabilitation or treatment, a current or predicted level of pain for the user, an expected performance level of the user associated with a current or future time for a particular activity or occupation, a change in symptoms associated with a disease or disorder, a current or predicted response to treatment, a likelihood that the user has experienced an increase in stress, or an overall wellness level of the user.” [0050] further recites: “Recurrent neural networks are a class of neural networks in which connections between nodes form a directed graph along a temporal sequence. Unlike a feedforward network, recurrent neural networks can incorporate feedback from states caused by earlier inputs, such that an output of the recurrent neural network for a given input can be a function of not only the input but one or more previous inputs. As an example, Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory.” [0058] further recites: “a reinforcement learning approach can be used to adjust the model parameters based on the accuracy of either predicted future values of wellness-relevant parameters at intermediate stages of the predictive model 124 or the output of the predictive model. For example, a decision threshold used to generate a categorical output from a continuous index produced by the predictive model 124 can be set at an initial value based on feedback from a plurality of models from previous users and adjusted via the reinforcement model to generate a decision threshold specific to the user.” No specific, technical improvements are being made to the field of machine learning as known machine learning methods are applied to perform the abstract idea of determining patient health. Examiner notes the fact pattern of the instant application matches claim 2 of Example 47.
Network interfaces are also taught at a high level of generality. [0038] recites: “The remote server 120 analyzes the data collected by the portable monitoring devices 102 and 110 and any clinical data received from the EHR system at the network interface 121. The remote server 120 can be implemented as a dedicated physical server or as part of a cloud server arrangement. In addition to the remote server, data can be analyzed, in whole or in part, on the local device itself and/or in a federated learning mechanism, in which case, any data from the EHR can be provided to the local device via an appropriate network interface.” No specific technical improvements are being made to networking technologies as any appropriate network is applied to perform an insignificant extra-solution activity of transmitting data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites wherein providing an intervention to the user comprises providing one of the novel set of wellness-relevant parameters, the novel set of aggregate parameters, and the novel clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach; which teaches an abstract idea of certain methods of organizing human activities under managing personal behaviors by communicating with a healthcare or wellness professional.
Claim 3: This claim recites wherein the clinical parameter is a value representing an overall wellness of the user, and the subset of the set of aggregate parameters comprises the entire set of aggregate parameters; which only serves to limit the clinical parameter and the subset of the set of aggregate parameters.
Claim 4: This claim recites wherein the providing the intervention to the user comprises reporting the clinical parameter to one of a health care provider, an insurance company, a care team, a research team, a coach of the user, and a workplace of the user via a network interface; which teaches an abstract idea of certain methods of organizing human activities under managing personal behaviors by communicating with a healthcare or wellness professional. The network interface is taught at a high level of generality such that it is only applied to perform an insignificant extra-solution activity of transmitting data.
Claim 5: This claim recites wherein the providing the intervention to the user comprises transmitting a message to the user suggesting a that guides the user through a stress reduction technique; which teaches an abstract idea of providing guidance/instructions for the patient to follow.
Claim 6: This claim recites wherein the providing the intervention to the user comprises providing the intervention to the user via portable computer device; which teaches the portable computer device at a high level of generality, such that it performs an insignificant extra-solution of outputting data.
Claim 7: This claim recites wherein the user is a first user of a plurality of users, and the predictive model is an anomaly detection model trained on data collected from the plurality of users; which only serves to limit the user and the type of predictive model.
Claim 8: This claim recites wherein the user is a first user of a plurality of users, the method further comprising: training the predictive model on data collected from the plurality of users; and collecting values for the set of wellness-relevant parameters from the first user over a period of time; and retraining the predictive model on the collected values for the set of wellness- relevant parameters; which teaches training and retraining at a high level of generality such that it is only applied to improve upon the abstract idea of determining patient health.
Claim 9: This claim recites wherein retraining the predictive model on the subset of wellness-relevant parameters comprises retraining the predictive model via a reinforcement learning process; which only serves to limit the type of retraining.
Claim 10: This claim recites wherein the set of aggregate parameters comprises a first aggregate parameter representing autonomic function of the user, a second aggregate parameter representing a cognitive function of the user, and a third aggregate parameter representing a motor and musculoskeletal health of the user; which only serves to further limit the types of parameters.
Claims 11 and 19: These claims recite wherein generating the set of aggregate parameters from the set of wellness-relevant parameters comprises generating a time series for one of the set of aggregate parameters and assigning the clinical parameter to the user via the predictive model comprises performing a wavelet decomposition on the time series of the one of the set of aggregate parameters to provide a set of wavelet coefficients, and assigning the value according to at least the set of wavelet coefficients and the subset of the set of aggregate parameters; which teaches an abstract idea of mathematical concepts as it recites utilizing wavelet decomposition to process input data.
Claim 12: This claim recites wherein assigning the clinical parameter to the user via the predictive model comprises: assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters; which teaches an abstract idea of assigning clinical parameters to a user according to predicted and determined values, which further describes the abstract idea of determining the patient’s health.
Claim 13: This claim recites wherein the set of aggregate parameters includes at least a first aggregate parameter representing sleep and circadian rhythms of the user, a second aggregate parameter representing a sociobehavioral function of the user, and a third aggregate parameter representing a biomarkers and genomics of the user which only serves to further limit the types of parameters.
Claim 15: This claim recites wherein the predictive model is a recurrent neural network; which only serves to narrow the type of predictive model.
Claim 16: This claim recites wherein at least one of the second plurality of wellness-relevant parameters is derived from psychosocial assessment data provided by the user, the portable computing device comprising a user interface that allows the user to interact with a psychosocial assessment application; which teaches an abstract idea of certain methods of organizing human activity under managing personal behaviors by claiming the interaction between a user and the device via a user interface. The claim further limits a type of parameter.
Claim 17: This claim recites the system further comprising a feedback component that collects values for the set of wellness-relevant parameters from the user over a period of time and adjusts the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters; which teaches an abstract idea of adjusting a threshold value, which is an activity that a doctor would perform when monitoring a patient’s long term health and adjusting thresholds to match new baselines. This claim further teaches the feedback component at a high level of generality such that it is applied to perform an insignificant extra-solution activity of gathering data
Claim 18: This claim is rejected for the same reasons as claims 10 and 13.
Claim 20: This claim recites the system further comprising a feedback component that collects values for the set of wellness-relevant parameters from the user over a period of time, collecting values representing an outcome for the user, and retraining the predictive model on the collected values for the set of wellness-relevant parameters and the values representing the outcome for the user; which teaches retraining at a high level of generality such that no improvements made to how training machine learning models is performed. This claim further teaches the feedback component at a high level of generality such that it is applied to perform an insignificant extra-solution activity of gathering data.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 and 15-20 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-13 and 15-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-13 and 15-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 7, 10-12, 14-16, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rezai (US 20210162216).
Regarding claim 1, Rezai teaches a method for generating a clinical parameter for a user, the method comprising:
performing the following until a set of baseline user data is generated ([0007], “A method comprises obtaining a measurement of baseline values of one or more combinations of a physiological, a cognitive, a psychosocial, and a behavioral parameter of the patient.”):
monitoring a first plurality of wellness-relevant parameters representing the user at a physiological sensing device over a defined period ([0], “System 100 can include a plurality of portable monitoring devices 102 and 110 that includes sensors for monitoring systems tracking the craving and other addiction parameters of the patient… By using portable monitoring devices 102 and 110, measurements can be made continuous from non-clinical settings.” [0070], “FIG. 4 is a schematic example 150 of the system of FIG. 1 using a plurality of portable monitoring devices 152, 154, and 160. In the illustrated implementation, the first and second portable monitoring devices 152 and 154 are wearable devices, worn on the wrist and finger, respectively. Craving-relevant parameters monitored by the first and second portable monitoring devices 152 and 154 can include, for example, heart rate, heart rate variability, metrics of sleep quality, biological rhythm variations, metrics of sleep quantity, physical activity of the user, body orientation, movement, arterial blood pressure, respiratory rate, peripheral arterial oxyhemoglobin saturation, as measured by pulse oximetry, maximum oxygen consumption, temperature, and temperature variation.”);
obtaining a second plurality of wellness-relevant parameters representing the user via a portable computing device ([0033], “a smart watch can be used to measure the patient's heart rate, heart rate variability, body temperature, blood oxygen saturation, movement, and sleep.”);
retrieving a third plurality of wellness-relevant parameters representing the user from an electronic health records (EHR) system ([0040], “In addition to one or more combinations of physiological, cognitive, psychosocial and behavioral parameters, clinical data can also be part of the multi-dimensional feedback approach to improving addiction. Such clinical data can include, for example, the patient's clinical state, the patient's medical history (including family history), treatment history, and prescription intake and history.” [0060], “The predictive model 124 can also utilize patient data 126 stored at the remote server 120, including, for example, employment information (e.g., title, department, shift), …, genomic data, nutritional information, medication intake, …, and relevant medical history.”). Examiner interprets patient medical information derived from a remote server to encompass data from an EHR system.
the first plurality of wellness-relevant parameters, the second plurality of wellness-relevant parameters, and the third plurality of wellness-relevant parameters collectively forming a set of wellness-relevant parameters ([0057], “The remote server can analyze the data collected by the portable monitoring devices 102 and 110.”). Examiner interprets the several types of data collected to encompass a set of data.
generating a set of aggregate parameters from the set of wellness- relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters ([0057], “Information received from the portable monitoring devices 102 and 110 can be provided to a feature extractor 122 that extracts a plurality of features for use as a predictive model 124. The feature extractor 122 can determine categorical and continuous parameters representing the craving relevant parameters. In one example, the parameters can include descriptive statistics, such as measures of central tendency (e.g., median, mode, arithmetic mean, or geometric mean) and measures of deviation (e.g., range, interquartile range, variance, standard deviation, etc.) of time series of the monitored parameters, as well as the time series themselves.” [0061], “The predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the patient.”);and
assigning a clinical parameter to the user via a predictive model according to a subset of the set of aggregate parameters ([0061], “The predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the patient.”);
determining a threshold value associated with the clinical parameter from the set of baseline user data ([0043], “The method further comprises determining threshold values for the one or more combinations of the physiological, cognitive, psychosocial, and behavioral parameters.”);
obtaining a novel set of wellness-relevant parameters; generating a novel set of aggregate parameters from the novel set of wellness-relevant parameters; generating a novel clinical parameter via the predictive model, representing a current state of the user, from a subset of the novel set of aggregate parameters ([0007], “A method comprises obtaining a measurement of baseline values of one or more combinations of a physiological, a cognitive, a psychosocial, and a behavioral parameter of the patient. The method further includes applying an initial focused ultrasound signal, an initial deep brain stimulation signal and/or an initial transcranial magnetic stimulation signal to a neural target site of the patient and obtaining a subsequent measurement of resultant values of the one or more combinations of the physiological, the cognitive, the psychosocial, and the behavioral parameter of the patient during or after application of the initial focused ultrasound signal, the initial deep brain stimulation signal and/or the initial transcranial magnetic stimulation signal. The method further includes obtaining a comparison of the resultant values to the baseline values to determine if the patient's addiction has improved”); and
providing an intervention to the user if the novel clinical parameter meets the determined threshold ([0013], “The method further includes selecting a patient for therapy upon a determination that the baseline values are different from the threshold values. Once the patient is selected for the therapy, the method can further include applying an initial focused ultrasound signal, an initial deep brain stimulation signal, and/or an initial transcranial magnetic stimulation signal to the patient to improve the patient's addiction. Patients are selected that have quantifiable measurements of physiological, cognitive, psychosocial, and behavioral values at baseline to properly measure threshold differences.”).
Regarding claim 3, Rezai teaches the method of claim 1. Rezai further teaches wherein the clinical parameter is a value representing an overall wellness of the user ([0061], “In another example, the predictive model 124 can be used to provide an index representing an internal marker of brain body balance, homeostasis, resilience and wellness.”), and
the subset of the set of aggregate parameters comprises the entire set of aggregate parameters ([0007], “obtaining a subsequent measurement of resultant values of the one or more combinations of the physiological, the cognitive, the psychosocial, and the behavioral parameter of the patient during or after application of the initial focused ultrasound signal”).
Regarding claim 7, Rezai teaches the method of claim 1. Rezai further teaches wherein the user is a first user of a plurality of users, and the predictive model is an anomaly detection model trained on data collected from the plurality of users ([0061], “It will be appreciated that, in one example, the comparison can be performed using a machine learning system trained on data representing previous cases, such that a clinical parameter representing a risk level of relapsing for the patient can be determined according to a deviation of the patient from a baseline value.”). Examiner notes that previous cases indicates multiple cases in the past, which represents a plurality of users.
Regarding claim 10, Rezai teaches the method of claim 1. Rezai further wherein the set of aggregate parameters comprises a first aggregate parameter representing autonomic function of the user, a second aggregate parameter representing a cognitive function of the user, and a third aggregate parameter representing a motor and musculoskeletal health of the user ([0023], “one or more combinations of the patient's resultant physiological, cognitive, psychosocial, behavioral and responses as well as sensory responses (e.g. smell, touch, taste, vestibular, interoception, proprioception, vision, and hearing), motor, and autonomic responses.”).
Regarding claim 11, Rezai teaches the method of claim 1. Rezai further teaches wherein generating the set of aggregate parameters from the set of wellness-relevant parameters comprises generating a time series for one of the set of aggregate parameters and assigning the clinical parameter to the user via the predictive model comprises performing a wavelet decomposition on the time series of the one of the set of aggregate parameters to provide a set of wavelet coefficients and assigning the value according to at least the set of wavelet coefficients and the subset of the set of aggregate parameters (eq. 3, [0057], “In one implementation, the feature extractor 124 can perform a wavelet transform on the time series of values for one or more parameters to provide a set of wavelet coefficients. It will be appreciated that the wavelet transform used herein is two-dimensional, such that the coefficients can be envisioned as a two-dimensional array across time and either frequency or scale.” [0058], “For a given time series of parameters, xi, the wavelet coefficients, Wa(n), produced in a wavelet decomposition can be defined as:”),
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Regarding claim 12, Rezai teaches the method of claim 1. Rezai further teaches wherein assigning the clinical parameter to the user via the predictive model comprises:
assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters ([0068], “In one implementation, the predictive model 124 can include a constituent model that predicts future values for craving-related parameters, such as a convolutional neural network that is provided with one or more two-dimensional arrays of wavelet transform coefficients as an input... In one implementation, the craving-related parameters predicted by the constituent models can include measured parameters such as heart rate, temperature, and heart rate variability as well as self-report questions such as encountering trigger, feeling depressed, or increased life stress.” [0069], “a given model can predict a physiological or behavior state for a patient at a future time based on received data from the feature extractor 122 and stored user data 126. These predicted values can be provided to a patient/user or utilized as inputs to additional models to predict a status of the patient at the future time.”). Examiner interprets predicting parameter values for the state based on the input parameters for the user to encompass assigning the predicted parameter to the user.
Regarding claim 14, this claim is rejected for the same reasons as claim 1, as described above. Rezai further teaches a system that includes a network interface that retrieves parameters ([0072], “The mobile device 160 can further comprise a network transceiver 168 via which the system 150 communicates with a remote server 170 via a local area network or Internet connection.” [0060], “The predictive model 124 can also utilize patient data 126 stored at the remote server 120”),
a feature aggregator ([0071], “The mobile device 160 can also include a graphical user interface 164 that allows a patient/user to interact with one or more data gathering applications 166 stored at the base unit.” [0079], “these techniques, blocks, steps and means can be implemented in hardware, software, or a combination thereof.”), and
an intervention selector ([0043], “The method further includes selecting a patient for therapy upon a determination that the baseline values are different from the threshold values. Once the patient is selected for the therapy, the method can further include applying an initial focused ultrasound signal, an initial deep brain stimulation signal, and/or an initial transcranial magnetic stimulation signal to the patient to improve the patient's addiction.” [0079], “these techniques, blocks, steps and means can be implemented in hardware, software, or a combination thereof.”).
Regarding claim 15, Rezai teaches the system of claim 14. Rezai further teaches wherein the predictive model is a recurrent neural network ([0072], “the remote server 170 includes a predictive model implemented as a recurrent neural network, specifically a network with a long short-term memory architecture.”).
Regarding claim 16, Rezai teaches the system of claim 14. Rezai further teaches wherein at least one of the second plurality of wellness-relevant parameters is derived from psychosocial assessment data provided by the user, the portable computing device comprising a user interface that allows the user to interact with a psychosocial assessment application ([0071], “The mobile device 160 can also include a graphical user interface 164 that allows a patient/user to interact with one or more data gathering applications 166 stored at the base unit… In general, the data gathering applications 166 can be selected and configured to monitor each parameters as listed in Tables I. II and/or III.” [0036], “Table III provides non-limiting examples of psychosocial and behavioral parameters that can be measured and exemplary tests, devices, and methods, to measure the behavioral parameters.”).
Regarding claim 19, this claims is rejected for the same reasons as claim 11, as described above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Rezai (US 20210162216) in view of Jain (US 20120290266).
Regarding claim 2, Rezai teaches the method of claim 1. Although Rezai contemplates notifying a third party of a patient’s condition ([0046], “notifying the patient or third party upon the determination that the patient's addiction has not improved. The third party can be, for example but not limited to, a physician, a therapist, a nurse, a family member of the patient, a counselor, a case manager, a peer recovery coach, or a 12-step sponsor.”), Rezai does not explicitly teach wherein providing an intervention to the user comprises providing one of the novel set of wellness-relevant parameters, the novel set of aggregate parameters, and the novel clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach.
However, Jain does teach wherein providing an intervention to the user comprises providing one of the novel set of wellness-relevant parameters, the novel set of aggregate parameters, and the novel clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach ([0392], “display system 190 may send or message some or all of the analysis output to one or more third-parties. In one embodiment, display system 190 may automatically send the analysis output to one or more healthcare providers. As an example and not by way of limitation, a subject wearing a portable blood glucose monitor may have all of the data from that sensor transmitted to his doctor. In another embodiment, display system 190 will only send the analysis output to a healthcare provider when one or more threshold criteria are met.”).
Rezai in view of Jain are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Jain for the advantage of “automatically send[ing] the analysis output to one or more healthcare providers” (Jain; [0392]).
Regarding claim 4, Rezai teaches the method of claim 1. Rezai further teaches wherein the providing the intervention to the user comprises reporting the clinical parameter to one of a health care provider, an insurance company, a care team, a research team, a coach of the user, and a workplace of the user via a network interface ([0392], “display system 190 may send or message some or all of the analysis output to one or more third-parties. In one embodiment, display system 190 may automatically send the analysis output to one or more healthcare providers. As an example and not by way of limitation, a subject wearing a portable blood glucose monitor may have all of the data from that sensor transmitted to his doctor. In another embodiment, display system 190 will only send the analysis output to a healthcare provider when one or more threshold criteria are met.”).
Rezai in view of Jain are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Jain for the advantage of “automatically send[ing] the analysis output to one or more healthcare providers” (Jain; [0392]).
Regarding claim 5, Rezai teaches the method of claim 1. Rezai does not teach wherein the providing the intervention to the user comprises transmitting a message to the user suggesting a that guides the user through a stress reduction technique.
However, Jain does teach wherein the providing the intervention to the user comprises transmitting a message to the user suggesting a that guides the user through a stress reduction technique ([0382], “Display system 190 may render, visualize, display, message, and publish to one or more users based on the one or more analysis outputs from analysis system 180.” [0304], “A person may receive a stress-related therapy in a variety of ways, such as, for example, from a third-person (e.g., a physician or therapist), from himself (e.g., self-relaxation techniques), from display system 190 (e.g., displaying a stress-related therapy), or from another suitable source.” [0393], “In particular embodiments, display system 190 may display one or more therapies to a user based on analysis output from analysis system 180. A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user. Display system 190 may deliver a variety of therapies, such as interventions, biofeedback, breathing exercises, progressive muscle relaxation exercises, presentation of personal media (e.g., music, personal pictures, etc.), offering an exit strategy (e.g., calling the user so he has an excuse to leave a stressful situation), references to a range of psychotherapeutic techniques, and graphical representations of trends (e.g., illustrations of health metrics over time), cognitive reframing therapy, and other therapeutic feedbacks.”).
Rezai in view of Jain are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Jain for the advantage of “helping with stress management” (Jain; [0279]).
Regarding claim 6, Rezai teaches the method of claim 1. Rezai does not teach wherein the providing the intervention to the user comprises providing the intervention to the user via portable computer device.
However, Jain does teach wherein the providing the intervention to the user comprises providing the intervention to the user via portable computer device ([0393], “In particular embodiments, display system 190 may display one or more therapies to a user based on analysis output from analysis system 180. A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user.” [0395], “computer system 1600 may be an embedded computer system, …, a laptop or notebook computer system, …, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these.”).
Rezai in view of Jain are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Jain for the advantage of providing a “Display system 190 [that] may have subcomponents that are … remote 140” (Jain; [0029]).
Claims 8-9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rezai (US 20210162216) in view of Quan (US 20210280078).
Regarding claim 8, Rezai teaches the method of claim 1. Rezai further teaches wherein the user is a first user of a plurality of users, the method further comprising:
training the predictive model on data collected from the plurality of users ([0061], “It will be appreciated that, in one example, the comparison can be performed using a machine learning system trained on data representing previous cases, such that a clinical parameter representing a risk level of relapsing for the patient can be determined according to a deviation of the patient from a baseline value.”); and
collecting values for the set of wellness-relevant parameters from the first user over a period of time ([0012], “The method further comprises obtaining a subsequent measurement of resultant values of the physiological, cognitive, psychosocial, or behavioral characteristic of the patient during or after application of the initial focused ultrasound signal, the initial deep brain stimulation signal and/or the initial transcranial magnetic stimulation signal.” [0061], “The predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the patient.”).
Rezai does not teach retraining the predictive model on the collected values for the set of wellness- relevant parameters.
However, Quan does teach retraining the predictive model on the collected values for the set of wellness- relevant parameters ([0165], “updating one or more models (e.g., as described above), such as based on an input received in S270 and/or based on any other processes and/or information in the method, which functions to dynamically adapt the set of models (and/or algorithms, decision trees, etc.) associated with the method 200 based on new information, which can subsequently function to make the models more robust.” [0080], “The health and/or fitness regimens can include any or all of: the collection and analysis (e.g., tracking) of information associated with the participants (…a mood or emotion of a participant, a stress level of a participant, a sleep parameter of a participant, etc.);”).
Rezai in view of Quan are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Quan for the advantage of “mak[ing] the models more robust” (Quan; [0165]).
Regarding claim 9, Rezai in view of Quan teaches the method of claims 1 and 8. Rezai does not teach wherein retraining the predictive model on the subset of wellness-relevant parameters comprises retraining the predictive model via a reinforcement learning process.
However, Quan does teach wherein retraining the predictive model on the subset of wellness-relevant parameters comprises retraining the predictive model via a reinforcement learning process ([0076], “the set of models can include any or all of: other predictive and/or regression models, topic models, reinforcement learning models, inverse reinforcement learning models, linear models (e.g., generalized linear models), nonlinear models, and/or any other suitable models.” [0166], “S290 can optionally include tracking one or more outputs, which preferably include a behavior change (e.g., increase in weight loss, increase in exercise, decrease in severity of a medical problem, etc.) and/or a set of outcome key drivers, such as weight loss, and correlating (e.g., through regression modeling) the interactions with the coach to the outcomes. If a particular conversation topic and/or other interaction with a participant leads to a positive behavior change, for instance, a model can be updated to reinforce the conversation topic/action that led to this, such that the same response might be tried with a similar participant.”).
Rezai in view of Quan are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Quan for the advantage of utilizing “reinforcement learning models” (Quan; [0076]).
Regarding claim 20, this claim is rejected for the same reasons as claim 8, as described above. Examiner interprets the collected parameters are values that represent the outcome of the user, as they are used to by the predictive model to output the user’s condition ([0061], “The predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the patient.”).
Claims 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rezai (US 20210162216) in view of Bahrami (US 20170308671).
Regarding claim 13, Rezai teaches the method of claim 1. Rezai further teaches wherein the set of aggregate parameters includes at least a first aggregate parameter representing sleep and circadian rhythms of the user and a second aggregate parameter representing a sociobehavioral function of the user ([0070], “Craving-relevant parameters monitored by the first and second portable monitoring devices 152 and 154 can include, for example, heart rate, heart rate variability, metrics of sleep quality, biological rhythm variations, metrics of sleep quantity, physical activity of the user,...” [0036], “Table III provides non-limiting examples of psychosocial and behavioral parameters that can be measured and exemplary tests, devices, and methods, to measure the behavioral parameters.”).
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Rezai does not teach a third aggregate parameter representing a biomarkers and genomics of the user.
However, Bahrami does teach a third aggregate parameter representing a biomarkers and genomics of the user ([0030], “the dimensions for the multi-dimensional model may include (1) clinical data 110, such as data captured by an Electronic Health Record (EHR); (2) a Personalized Health Risk Assessment Profile (PHRAP) 112 based on one or more genetic, physical, behavioral, psychosocial and diseases profiles; (3) Personalized Biomarkers with Adaptable Normal Value (PBNV) 118 based on longitudinal measurement of biomarkers in bodily fluids for an individual;”).
Rezai in view of Bahrami are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Bahrami for the advantage of obtaining “Comprehensive patient data” (Bahrami; [0090]).
Regarding claim 18, this claims is rejected for the same reasons as claims 10 and 13, as described above. Rezai further teaches a sixth aggregate parameter representing sensory function and changes in function for the user ([0040], “in addition to one or more combinations of physiological, cognitive, psychosocial and behavioral parameters, sensory response (e.g. smell, touch, taste, vestibular, introception, vision, hearing, proprioception), motor, and autonomic responses can also be part of a multi-dimensional feedback approach to improve addiction.”).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Rezai (US 20210162216) in view of Saliman (US 20170228517).
Regarding claim 17, Rezai teaches the system of claim 14. Rezai teaches the system further comprising a feedback component that collects values for the set of wellness-relevant parameters from the user over a period of time ([0012], “The method further comprises obtaining a subsequent measurement of resultant values of the physiological, cognitive, psychosocial, or behavioral characteristic of the patient during or after application of the initial focused ultrasound signal, the initial deep brain stimulation signal and/or the initial transcranial magnetic stimulation signal.” [0061], “at least some of the parameters can be measured periodically through the course of several days or weeks as to establish a time series of measurements representing a biological rhythm of the patient.”).
Rezai does not teach wherein the component adjusts the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters.
However, Saliman does teach wherein the component adjusts the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters ([0107], “These threshold values for a particular patient may be adjusted based on, for example, a subset of rules that are appropriate for the patient or other considerations (e.g., comorbidities, patient- or doctor-specific goals, etc.). Information about other considerations may be stored in, for example, other considerations element 132. Examples of patient-facing interfaces that provide various wellness scores are provided by interfaces 1800-1813 of FIGS. 18A-18N.” [0096], “an OMD may be designed to gather information regarding a patient's condition prior to a patient receiving a treatment or procedure so as to, for example, establish a baseline degree of wellness for the patient” [0193], “the pre-treatment wellness score, post-treatment wellness score, and/or improvement score may be compared to a minimum threshold. If one or more of the scores are below the minimum threshold, a notification may be generated and provision of the notification to a treatment provider, treatment facility, and/or treatment facility administrator, alerting the treatment provider, treatment facility, and/or treatment facility administrator to the abnormally low one or more scores may be facilitated (step 1085).”).
Rezai in view of Saliman are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rezai with Saliman for the advantage of utilizing “a subset of rules that are appropriate for the patient” (Saliman; [0107]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pain Management Based On Emotional Expression Measurements (US 20230120858) teaches systems and methods for managing pain in a subject. A system may include one or more sensors configured to sense from the subject information corresponding to emotional reaction to pain, such as emotional expression. The emotional expression includes facial or vocal expression. A pain analyzer circuit may generate a pain score using signal metrics of facial or vocal expression extracted from the sensed information. The pain score may be output to a user or a process. The system may additionally include a neurostimulator that can adaptively control the delivery of pain therapy by automatically adjusting stimulation parameters based on the pain score.
Method And System For Modeling Behavior And Heart Disease State (US 20170000422), which teaches a method for evaluating cardiovascular health of a patient includes receiving a log of use dataset associated with patient digital communication behavior at a mobile computing device, wherein the log of use dataset is associated with a time period, receiving a supplementary dataset associated with the time period, generating a survey dataset based on a patient response to a survey, generating a cardiovascular health predictive model based upon at least one of the log of use dataset, the supplementary dataset, and the survey dataset, 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; providing a cardiovascular-related notification to the patient; and automatically providing a cardiovascular therapeutic intervention at a cardiovascular device for the patient.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID CHOI whose telephone number is (571)272-3931. The examiner can normally be reached M-Th: 8:30-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on (571)270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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