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 .
Status of Claims
This action is in reply to the current action filed on October 8th, 2021.
Claims 1-24, 26-28, and 31-34 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 19th, 2022 was filed after the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 27 is objected to for reciting “the interface system is as a reference of known dimension”. Examiner recommends rewording this limitation to “the interface system is a reference of known dimension”.
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 1-24, 26-28, and 31-34 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1 and 34 are within the four statutory categories. Claims 1 and 34 are drawn to a method and system, respectively. Dependent Claims 2-24, 26-28 and 31-33 are further directed to a method and therefore also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A method of managing sleep therapy, comprising: inputting characterization data associated with each of a plurality of patients into a database system stored in a memory system;
inputting outcome data associated with each of the plurality of patients resulting from use of one of a plurality of interface systems worn during sleep therapy;
training at least one machine learning procedure of an algorithm stored in the memory system and executable via a processor system using a training set of the characterization data associated with each of a plurality of patients and the outcome data associated with each of the plurality of patients resulting from use of one of the plurality of interface systems to create at least one machine learning model,
determining a management option for sleep therapy for a person via execution of the algorithm based at least in part upon an optimization of at least one outcome parameter for the person predicted by the at least one machine learning model based at least in part upon characterization data of the person input into the database system,
wherein the management option comprises at least one of selection of an interface system from the plurality of interface systems for future use, fitting of a selected interface system for future use, and a change in use of a currently used interface system;
and communicating information regarding the management option to the person.
The limitations as shown in underline above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions- in this case, using a set of data associated with patients to determine a management option for sleep therapy for a patient and communicating that information to the patient), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-3, 5-8, 11-12, 14-15, 18-24, 26, 28, 31, and 33 include other limitations directed to the abstract idea. For example, Claim 2 recites the person is one of the plurality of patients, Claim 3 recites the management option for sleepy therapy determined via the predicted optimization of the outcome parameter based on characterization data and outcome data of the person, Claim 5 recites an outcome parameter is a function of outcome metrics including a number of apneas and usage time over a predetermined period of time, Claim 6 recites the outcome metrics comprise a level of drowsiness, Claim 7 recites the outcome data is determined from data from a PAP device, questionnaire, or observed patient behavior, Claim 8 recites patients are monitored over time, Claim 11 recites the management option comprising a change in a sleep therapy option and determining if a recommendation is to be communicated to the patient based on a threshold, Claim 12 recites the management option includes a recommendation for an appointment with a physician, change in lifestyle change in sleep behavior, education, or providing positive feedback, Claim 14 recites upon a triggering event, determining an updated management option for sleep therapy based in part on at least one outcome parameter, Claim 15 recites what the triggering event comprises, Claim 18 recites communicating a questionnaire to the person, Claim 19 recites situation sensitivity is determined from data from the person, Claim 20 recites data from the person is used to determine that the person is likely to have recently participated in a predetermined activity and the questionnaire includes at least one question of the level of drowsiness of the person, Claim 21 recites situation sensitivity is determined from data of motion, time or location data, Claim 22 recites the predetermined activity is driving, Claim 23 recites the characterization data of each patient comprises anatomical, sleep behavior, demographic, health, or sleep therapy data, Claim 24 recites obtaining a video or image of the person wearing a interface system, Claim 26 recites determining if the patient is using the system correctly, Claim 28 recites the anatomical data comprises at least one anatomical characteristic of the person’s head, Claim 31 recites the image is a two-dimensional image analyzed via an image characterization, Claim 33 recites determining a fitting for headgear. These limitations only server to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in the dependent claims not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 2-3, 5-8, 11-12, 14-15, 18-24, 26, 28, 31, and 33 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 34.
Step 2A Analysis – Prong Two:
Claims 1 and 34 are not integrated into practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the database system, memory system, interface systems, machine learning model, algorithm, and processor system of Claims 1 and 34) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that the processor may be associated with various other circuits that support operation of the processor, such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM) [0056]. Characterization data associated with each of a plurality of patients is stored in one or more databases of a database system which is stored in a memory system. Patient clinical outcome data from the sleep therapy associated with each of a plurality of patients is also stored in the one or more databases of the database system. Models from one or more machine learning algorithms, previously trained using patient characterization data metrics and patient outcome data metrics (which are determined, from previous sleep therapy treatment for the plurality or population of patients) in the database system hereof are used in determining/optimizing one or more predicted outcome parameters [0073]. In the case of a patient having an interface system (for example, a patient already in PAP therapy), a video or a photograph of the patient wearing their currently supplied interface system (for example, a mask system as illustrated in Figure 2) may be obtained and the interface system or a portion thereof may be used as a known dimensional reference. In that regard, the interface system data (type, brand and/or size) may be determined using, for example, computer vision software as known in the computer arts [0070]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the idea. Therefore, Claims 1 and 34 are directed to an abstract idea without practical application.
Dependent Claims 3-4, 8-10, 13-20, 24, 26-28, and 32-33 also recite additional elements. Claim 3 recites the previously recited additional element of the database system and specifies the characterization data of the person is input into the database system, Claim 4 narrows the previously recited algorithm and machine learning model and specifies the algorithm comprises a plurality of machine learning procedures or a plurality of machine learning models, Claim 8 recites the previously recited database system and specifies new characterization or new outcome data is input into the database system, Claim 9 recites the previously recited machine learning model and algorithm and specifies updating training of the algorithm based on the new data to create an updated machine learning model, Claim 10 narrows the previously recited machine learning model by specifying testing each of the machine learning model against a test dataset and determining which of the updated models has a better confidence interval, Claim 13 narrows the previously recited element of the interface system by specifying the management option comprises a selection of an interface system, Claim 14 recites the previously recited additional elements of the algorithm and machine learning model by specifying upon occurrence of a triggering event, determining an updated management option for sleep therapy through the algorithm based on an optimization of an outcome parameter by the machine learning model, Claim 15 recites the previously recited machine learning model and a new interface system by specifying the triggering event comprises initiation of use of at least one updated machine learning model, use of a new interface system by at least a portion of the patients, Claim 16 recites new additional limitations of a software application, device, and remote system, specifying the method comprises a software application on a patient device to facilitate communication of information between the patient and a remote system including the database system and the algorithm, Claim 17 recites a new additional element of a mobile personal communication device and specifies the device of Claim 16 is a mobile personal communication device, Claim 18 recites the previously recited elements of the software application and the database system and specifies the software application communicates a questionnaire to the person that is situation sensitive and inputting outcome data determined from a response of the person in the database system, Claim 19 recites the previously recited element of the device and specifies the situation sensitivity is determined from data from the device of the person and the timing of the questionnaire is based on data from the device, Claim 20 recites the previously recited device and specifies the device of the person is used to determine that the person has participated in an activity, Claim 24 recites the previously recited additional elements of the interface system and algorithm and specifies obtaining a video/image of the person who is wearing the interface system and the algorithm determines if the person is wearing the interface system correctly, Claim 26 recites the previously recited algorithm and interface system and specifies the algorithm comprises a computer vision procedure to determine if the interface system is used incorrectly/non-optimally, Claim 27 recites the previously recited additional element of the interface system and specifies a portion of the interface system is a reference of known dimension, Claim 28 recites a new additional element of a sleep therapy interface and specifies the determination of an anatomical characteristic based on a video/image and a known dimensional reference selected from an iris of the patient or a portion of a sleep therapy interface worn by the patient, Claim 32 recites the interface system and specifies the management option comprises the selection of an interface system and the fitting of a selected interface system, Claim 33 recites the interface system and specifies the management option comprises determining a fitting for headgear of the interface system. However, these additional elements are recited only at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B Analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the database system, memory system, interface systems, machine learning model, algorithm, and the processor system of Claims 1 and 34 amount to no more than mere instruction to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1 and 34 are not patent eligible.
Dependent Claims 2, 5-7, 11-12, 21-23, and 31 solely narrow the abstract idea and do not recite any additional elements. Claim 2 recites the person is one of the plurality of patients. Claim 5 recites an outcome parameter is a function of outcome metrics including a number of apneas and usage time over a predetermined period of time. Claim 6 recites the outcome metrics comprise a level of drowsiness. Claim 7 recites the outcome data is determined from data from a PAP device, questionnaire, or observed patient behavior. Claim 11 recites the management option comprising a change in a sleep therapy option and determining if a recommendation is to be communicated to the patient based on a threshold. Claim 12 recites the management option includes a recommendation for an appointment with a physician, change in lifestyle change in sleep behavior, education, or providing positive feedback, Claim 21 recites situation sensitivity is determined from data of motion, time or location data. Claim 22 recites the predetermined activity is driving. Claim 23 recites the characterization data of each patient comprises anatomical, sleep behavior, demographic, health, or sleep therapy data. Claim 31 recites the image is a two-dimensional image analyzed via an image characterization.
Dependent Claims 3-4, 8-10, 13-14 18-20, 24, 26-27, and 32-33 recite previously cited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 3 recites the previously recited additional element of the database system and specifies the characterization data of the person is input into the database system, Claim 4 narrows the previously recited algorithm and machine learning model and specifies the algorithm comprises a plurality of machine learning procedures or a plurality of machine learning models, Claim 8 recites the previously recited database system and specifies new characterization or new outcome data is input into the database system, Claim 9 recites the previously recited machine learning model and algorithm and specifies updating training of the algorithm based on the new data to create an updated machine learning model, Claim 10 narrows the previously recited machine learning model by specifying testing each of the machine learning model against a test dataset and determining which of the updated models has a better confidence interval, Claim 13 narrows the previously recited element of the interface system by specifying the management option comprises a selection of an interface system, Claim 14 recites the previously recited additional elements of the algorithm and machine learning model by specifying upon occurrence of a triggering event, determining an updated management option for sleep therapy through the algorithm based on an optimization of an outcome parameter by the machine learning model, Claim 18 recites the previously recited elements of the software application and the database system and specifies the software application communicates a questionnaire to the person that is situation sensitive and inputting outcome data determined from a response of the person in the database system, Claim 19 recites the previously recited element of the device and specifies the situation sensitivity is determined from data from the device of the person and the timing of the questionnaire is based on data from the device, Claim 20 recites the previously recited device and specifies the device of the person is used to determine that the person has participated in an activity, Claim 24 recites the previously recited additional elements of the interface system and algorithm and specifies obtaining a video/image of the person who is wearing the interface system and the algorithm determines if the person is wearing the interface system correctly, Claim 26 recites the previously recited algorithm and interface system and specifies the algorithm comprises a computer vision procedure to determine if the interface system is used incorrectly/non-optimally, Claim 27 recites the previously recited additional element of the interface system and specifies a portion of the interface system is a reference of known dimension, Claim 32 recites the interface system and specifies the management option comprises the selection of an interface system and the fitting of a selected interface system, Claim 33 recites the interface system and specifies the management option comprises determining a fitting for headgear of the interface system.
Dependent Claims 15-17 and 28 recite new additional elements with new limitations. Claim 15 recites the previously recited machine learning model and a new interface system and specifies the triggering event comprises initiation of use of at least one updated machine learning model, use of a new interface system by at least a portion of the patients, Claim 16 recites new additional limitations of a software application, device, and remote system, specifying the method comprises a software application on a patient device to facilitate communication of information between the patient and a remote system including the database system and the algorithm, Claim 17 recites a new additional element of a mobile personal communication device and specifies the device of Claim 16 is a mobile personal communication device, Claim 28 recites a new additional element of a sleep therapy interface and specifies the determination of an anatomical characteristic based on a video/image and a known dimensional reference selected from an iris of the patient or a portion of a sleep therapy interface worn by the patient.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide a conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-24, 26-28, and 31-34 are nonetheless rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
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.
Claims 1-9, 13-17, 23, and 34 are rejected under 35 USC 103 as being unpatentable over Apte et al. (US 20180286520 A1) in view of Richard et al. (US 20150306330 A1).
Regarding Claim 1, Apte discloses:
A method of managing sleep therapy, comprising: (Apte discloses as shown in FIGS. 1A-1B, embodiments of a method 100 for characterizing one or more sleep-related conditions can include:… and/or determining one or more therapies [0016].)
inputting characterization data associated with each of a plurality of patients into a database system stored in a memory system; (Apte discloses data from populations of subjects (e.g., associated with one or more sleep-related conditions; positively or negatively correlated with one or more sleep-related conditions; etc.) can be used to characterize subsequent users [0021].)
inputting outcome data associated with each of the plurality of patients resulting from use of one of a plurality of interface systems worn during sleep therapy; (Apte discloses supplementary data can include any one or more of: survey-derived data... site-specific data…Processing survey-derived data can include facilitating collection of survey-derived data, such as including providing one or more surveys to one or more users, subjects, and/or other suitable entities. Additionally or alternatively, processing supplementary data can include processing sensor data (e.g., sensors of sleep-related devices, wearable computing devices, mobile devices; biometric sensors associated with the user, such as biometric sensors of a user smart phone; etc.). Sensor data can include any one or more of: physical activity- and/or physical action-related data (e.g., …mobility sensor data from one or more devices such as a mobile device and/or wearable electronic device, etc.), sensor data describing environmental factors…, biometric sensor data (e.g., heart rate sensor data; fingerprint sensor data; optical sensor data such as facial images and/or video; data recorded through sensors of a mobile device; data recorded through a wearable or other peripheral device; etc.), and/or any other suitable data associated with sensors [0060-62]. The Examiner interprets the wearable device as the interface system work during therapy.)
training at least one machine learning procedure of an algorithm stored in the memory system and executable via a processor system (Apte discloses the method can include Block S120, which can function to process data for supplementing microorganism datasets, …such as for facilitating training, validating, generating, determining, applying and/or otherwise processing sleep-related characterization models, etc.) [0059]. The characterization process can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (e.g., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features [0076].)
using a training set of the characterization data associated with each of a plurality of patients and the outcome data associated with each of the plurality of patients resulting from use of one of the plurality of interface systems to create at least one machine learning model, (Apte discloses embodiments can function to generate models (e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.), such as models that can be used to characterize and/or diagnose users …and/or that can be used to select and/or provide therapies for subjects in relation to one or more sleep-related conditions [0020]. The method 100 can additionally or alternatively include Block S120, which can include processing… supplementary data (e.g., one or more supplementary datasets, etc.) associated with… one or more sleep-related conditions, one or more users, and/or other suitable entities. Block S120 can function to process data…and/or can function to supplement any suitable portion of the method 100 and/or system 200 (e.g., processing supplementary data for facilitating one or more characterization processes, such as in Block S130; such as for facilitating training, validating, generating, determining, applying and/or otherwise processing sleep-related characterization models, etc.) [0059].)
determining a management option for sleep therapy for a person via execution of the algorithm based at least in part upon an optimization of at least one outcome parameter for the person predicted by the at least one machine learning model based at least in part upon characterization data of the person input into the database system, (Apte discloses the therapy model is preferably based upon data from a large population of subjects… Such data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different sleep-related characterizations. In variations, support vector machines, as a supervised machine learning algorithm, can be used to generate the therapy provision model. However, any other suitable machine learning algorithm described above can facilitate generation of the therapy provision model [0095]. In examples, machine learning approaches…, parameter optimization approaches (e.g., Bayesian Parameter Optimization),… and/or other suitable analytical techniques (e.g., described herein) can be applied in determining site-related …characterizations…, therapies, and/or any other suitable outputs [0080].)
and communicating information regarding the management option to the person. (Apte discloses the therapy facilitation system 230 can include any one or more of: a communications system (e.g., to communicate therapy recommendations, selections, discouragements, and/or other suitable therapy-related information to a computing device (e.g., user device and/or care provider device; mobile device; smart phone; desktop computer; at a website, web application, and/or mobile application accessed by the computing device; etc.); [0043].)
Apte does not disclose the selection of an interface system which is met by Richard:
wherein the management option comprises at least one of selection of an interface system from the plurality of interface systems for future use, fitting of a selected interface system for future use, and a change in use of a currently used interface system; (Richard teaches the present invention provides an easy to use website or app (or application) that can assist the provider or physician and subject in making current or future mask selections that best match their facial profiles [0023]. Under an embodiment of the present system, a subject can easily take a photo of their face and then run that image through the mask database allowing them to see masks that can better fit their current needs, thus eliminating leaks and improving compliance with their therapy [0026]. By using these measurements, one or masks that actually fit the subject, based on the calculated measurements and the known sizes/tolerances of the masks, can be selected for the subject. Accordingly, mask(s) with the measurements that best fit the measurements of the subject taken from the photo are the mask(s) the system can select [0036].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for inputting characterization data and interface data for training a machine learning algorithm to determine a management option for sleep therapy and communicating that recommendation to the user as disclosed by Apte to incorporate the selection and fitting of an interface system for future use as taught by Richard. This modification would create a system and methods for ensuring CPAP users are wearing the proper device thus creating effective sleep therapy (see Richard, ¶ 0003).
Regarding Claim 34, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Apte further discloses:
A system of managing sleep therapy, comprising: a memory system; a processor system in operative connection with the memory system; (Apte discloses embodiments of the method 100 and/or system 200 can function to characterize (e.g., assess, evaluate, diagnose, describe, etc.) one or more sleep-related conditions [0018]. The system 200…can be embodied…as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with the system. 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 is preferably a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions [0124].)
Regarding Claim 2, Apte and Richard teach the limitations as seen in the rejection of Claim 1 above. Apte further discloses:
wherein the person is one of the plurality of patients or is a new patient additional to the plurality of patients. (Apte discloses specific examples of the technology can amount to an inventive distribution of functionality across a network including a sample handling system, a sleep-related characterization system, and a plurality of users, where the sample handling system can handle substantially concurrent processing of biological samples…from the plurality of users, which can be leveraged by the sleep-related characterization system in generating personalized characterizations and/or therapies (e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographic characteristics, other behaviors, preferences, etc.) for sleep-related conditions [0032]. )
Regarding Claim 3, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
wherein characterization data of the person and any available output data is input into the database system (Apte discloses Block S130 and/or other portions of the method 100 can include applying computer-implemented rules… to process population-level data, but can additionally…include applying computer-implemented rules to process…condition-specific basis (e.g., subgroups exhibiting a specific sleep-related condition, a combination of sleep-related conditions, triggers for the sleep-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis … and/or any other suitable basis. As such, Block S130 can include assigning users from the population of users to one or more subgroups; and applying different computer-implemented rules for determining features…for the different subgroups [0077].)
and the management option for sleep therapy determined via the predicted optimization of the at least one outcome parameter based at least in part upon characterization data and the outcome data of the person input into the database system. (Apte discloses the therapy model is …based upon data from a large population of subjects, which can include the population of subjects from which the…datasets are derived in Block S110, where…functional features or states of health…are well characterized. Such data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different sleep-related characterizations…a supervised machine learning algorithm, can be used to generate the therapy provision model. Upon identification of a subset of subjects of the population of subjects who are characterized to be in good health (e.g., using features of the characterization process), therapies that modulate microbiome compositions and/or functional features toward those of subjects in good health can be generated in Block S140. Block S140 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographic characteristics), and potential therapy formulations and therapy regimens…The therapy model can, however, be generated and/or refined in any other suitable manner [0095-96]. The Examiner interprets the refinement of the model based on outcome and characterization data as optimizing the model.)
Regarding Claim 4, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
wherein the algorithm comprises a plurality of machine learning procedures or a plurality of machine learning models (Apte discloses embodiments can function to generate models (e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.), such as models that can be used to characterize and/or diagnose users …and/or that can be used to select and/or provide therapies for subjects in relation to one or more sleep-related conditions [0020].)
Regarding Claim 5, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
wherein the at least one outcome parameter is a function of outcome metrics comprising a number of apneas over a predetermined period of time (Apte discloses the method 100 and/or system 200 can preferably determine and/or promote… characterizations and/or therapies for one or more sleep-related conditions, and/or any suitable portions of the method 100 and/or system 200 can be performed in relation to sleep-related conditions. Sleep-related conditions can include any one or more of: …sleep-related breathing disorders (e.g., sleep apnea, obstructive sleep apnea, snoring, central sleep apnea, child sleep apnea, infant sleep apnea, …etc.), …and/or any other suitable conditions associated with sleep [0022]. Data…can be associated with any suitable temporal indicators…including one or more: temporal indicators indicating when the data was collected…, determined, transmitted, received, and/or otherwise processed; temporal indicators providing context to content described by the data … [0025].)
and a usage time over the predetermined period of time. (Apte discloses the method 100 can… include one or more of: … determining, with one or more characterization processes, a sleep-related characterization for the user for one or more sleep-related conditions, based on a user microorganism dataset … associated with a biological sample of the user S160; facilitating therapeutic intervention for the one or more sleep-related conditions for the user … S170; monitoring effectiveness of one or more therapies and/or monitoring other suitable components … for the user…, over time (e.g., such as to assess user microbiome characteristics such as user microbiome composition features and/or functional features associated with the therapy, for the user over time, etc.) S180; [0017].)
Regarding Claim 7, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
wherein at least a portion of the outcome data is determined from at least one of data measured from a PAP device, questionnaire data or observed patient behavior. (Apte discloses data from populations of subjects (e.g., associated with one or more sleep-related conditions; positively or negatively correlated with one or more sleep-related conditions; etc.) can be used to characterize subsequent users, such as … to facilitate therapeutic intervention (e.g., promoting one or more therapies;…such as in relation to one of more sleep-related conditions [0021]. One or more sleep-related conditions can be characterized by and/or diagnosed by medical interview, medical history, survey, sensor data, medical exams, data activities including and/or requiring monitoring individuals as they sleep, other supplementary data, and/or through any suitable techniques [0023].)
Regarding Claim 8, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
wherein each of the plurality of patients is monitored over time and new data comprising at least one of new characterization data or new outcome data for each of the plurality of patients is input into the database system over time. (Apte discloses monitoring effectiveness of one or more therapies and/or monitoring other suitable components (e.g., microbiome characteristics, etc.) for the user (e.g., based upon processing a series of biological samples from the user), over time [0017]. Variations of the method 100 can further facilitate selection, monitoring … and/or adjusting of therapies provided to a user, such as through collection and analysis (e.g., with sleep-related characterization models) of additional samples from a subject over time [0021].)
Regarding Claim 9, Apte and Richard teach the limitations as seen in the rejection of Claim 8 above. Apte further discloses:
updating training of the algorithm based upon the new data for each of the plurality of patients to create at least one updated machine learning model. (Apte discloses updating (e.g., of characterization models and/or therapy models based on processed biological samples over time; etc.),…[0040]. Block S130 can include processing (e.g., generating, training, updating, executing, storing, etc.) one or more sleep-related characterization models (e.g., sleep-related condition models, therapy models, etc.) for one or more sleep-related conditions [0078].)
Regarding Claim 11, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
the management option comprises a change in a sleep therapy option, (Apte discloses other suitable data (e.g., supplementary data describing user behavior associated with one or more sleep-related conditions; supplementary data describing a sleep-related condition such as observed symptoms; etc.) can be used in determining a post-therapy characterization…, updated therapies (e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.) [0118]. The Examiner interprets updated therapies as a change in therapy.)
and the method further comprises determining if a recommendation to make the change in the sleep therapy option is to be communicated to the patient based upon a predetermined threshold in a change in the predicted optimization of the at least one output parameter. (Apte discloses the method can…include Block S180, which can include: monitoring effectiveness of one or more therapies. Monitoring of a user during the course of a therapy promoted by the therapy model…can thus be used to generate a therapy-effectiveness model for each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170. The method can include…promoting an updated therapy to the user for the sleep-related condition based on the post-therapy sleep-related characterization …and/or the user adherence to the therapy (e.g., modifying the therapy based on positive or negative results for the user microbiome in relation to the sleep-related condition; etc.). Additionally or alternatively, other suitable data… be used in determining a post-therapy characterization (e.g., degree of change from pre-to post-therapy in relation to the sleep-related condition; etc.), updated therapies (e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.) [0116-118].)
Regarding Claim 12, Apte and Richard teach the limitations as seen in the rejection of Claim 2 above. Apte further discloses:
the management option further includes at least one of a recommendation for an appointment with a physician, a recommendation for a change in lifestyle, a recommendation for a change in sleep behavior, providing education on sleep therapy, or providing positive feedback. (Apte discloses the therapy facilitation system 230 of the system 200 can function to facilitate therapeutic intervention …for one or more sleep-related conditions…the therapy facilitation system 230 can update and/or otherwise modify an application and/or other software of a device …to promote a therapy (e.g., promoting, at a to-do list application, lifestyle changes for improving a user state associated with one or more sleep-related conditions, etc.). However, the therapy facilitation system 230 can be configured in any other manner [0044]. Therapies (e.g., sleep-related therapies, etc.) can include any one or more of: consumables…device-related therapies…surgical operations…behavior modification therapies (e.g., physical activity recommendations such as increased exercise; dietary recommendations such as reducing sugar intake, increased vegetable intake, increased fish intake, decreased caffeine consumption, decreased alcohol consumption,…; weight-related recommendations; sleep habit recommendations; device recommendations…[0091].)
Regarding Claim 13, Apte and Richard teach the limitations as seen in the rejection of Claim 1 above. Apte does not disclose the following limitation met by Richard:
wherein the management option comprises a selection of an interface system from the plurality of interface systems for future use. (Richard teaches when the vector points and measurements are calculated, the measurements are compared to the known measurements for each of the masks in the database. By using these measurements, one or masks that actually fit the subject, based on the calculated measurements and the known sizes/tolerances of the masks, can be selected for the subject. Accordingly, mask(s) with the measurements that best fit the measurements of the subject taken from the photo are the mask(s) the system can select [0035-36].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for inputting characterization data and interface data for training a machine learning algorithm to determine a management option for sleep therapy and communicating that recommendation to the user as disclosed by Apte to incorporate the selection of an interface system from a plurality of options as taught by Richard. This modification would create a system and methods for ensuring CPAP users are wearing the proper device thus creating effective sleep therapy (see Richard, ¶ 0003).
Regarding Claim 14, Apte and Richard teach the limitations as seen in the rejection of Claim 1 above. Apte does not disclose the following limitation met by Richard:
upon occurrence of a triggering event, determining an updated management option for sleep therapy for at least one of the plurality of patients via execution of the algorithm based at least in part upon an optimization of the at least one outcome parameter for the at least one of the plurality of patients predicted by at least one machine learning model determined for use at the time of the triggering event and based at least in part upon the characterization data associated with the at least one of the plurality of patients and the outcome data associated with the at least one of the plurality of patients. (Apte discloses sleep-related conditions can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk…, associated severity [0023]. The method can additionally or alternatively include Block S180, which can include: monitoring effectiveness of one or more therapies. Monitoring of a user during the course of a therapy promoted by the therapy model …can be used to generate a therapy-effectiveness model for each characterization each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170. The method can include…promoting an updated therapy to the user for the sleep-related condition based on the post-therapy sleep-related characterization …and/or the user adherence to the therapy (e.g., modifying the therapy based on positive or negative results for the user microbiome in relation to the sleep-related condition; etc.)…other suitable data …can be used in determining a post-therapy characterization (e.g., degree of change from pre- to post-therapy in relation to the sleep-related condition; etc.), updated therapies (e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.) [0116-118].)
Regarding Claim 15, Apte and Richard teach the limitations as seen in the rejection of Claim 14 above. Apte further discloses:
wherein the triggering event comprises a passage of a predefined period of time, a request for the at least one of the plurality of patients, receipt of new characterization data associated with the at least one of the plurality of patients or new outcome data associated with the at least one of the plurality of patients, (Apte discloses sleep-related conditions can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), [0023]. Block S160, which can include determining…, a sleep-related characterization for the user,…in an example, Block S160 can include generating a sleep-related characterization for the user based on…a sleep-related condition model. Sleep-related characterizations can include one or more of: diagnoses…; risk,… comparisons…; therapy determinations; other suitable outputs associated with characterization processes [0105]. The method 100 can include receiving a post-therapy biological sample from the user; collecting a supplementary dataset from the user,…generating a post-therapy sleep-related characterization of the first user in relation to the sleep-related condition based on the sleep-related characterization model…; and promoting an updated therapy to the user for the sleep-related condition based on the post-therapy sleep-related characterization …and/or the user adherence to the therapy. Additionally or alternatively, other suitable data… can be used in determining a post-therapy characterization…, updated therapies (e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.) [0118]. The Examiner interprets these triggering events as a receipt of new characterization data.)
initiation of use of at least one updated machine learning model, (Apte discloses sleep-related characterization models, other models, other components of the system 200, and/or suitable portions of the method 100…can employ analytical techniques including… updating (e.g., of characterization models and/or therapy models based on processed biological samples over time; etc.) [0040].)
Apte does not disclose the use of a new interface system which is met by Richard:
use of a new interface system by at least a portion of the plurality of patients. (Richard teaches a system for selecting a mask comprising: a database storing information related to one or more masks;…determine one or more measurements of the at least the portion of the face of the subject based on the image; access information for at least one mask from the database based on the one or more measurements; generate a representation of the information on the display; and receive a selection of one of the at least one masks [0006]. The database can consist of 50-75 (or more or less) of the most popular masks used on the market and be updated regularly, such as every 3-4 months [0039].)
It would have been obvious