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 .
Response to Arguments
I. Rejections under 35 U.S.C. § 103
Applicant’s remarks concerning the previous § 103 rejections have been fully considered but are not persuasive. Applicant argues that while Barrett teaches that rescue inhalations may be used in the predictive analysis, Barrett “does not establish that asthma predictions may be made based on measured characteristics of individual inhalation
events, such as peak inhalation flow, inhalation volume, or inhalation duration.” As such, Applicant argues “Barrett does not provide a generic invitation to use any past or historical
inhaler-related data for determining an asthma exacerbation probability.” The Examiner respectfully disagrees. As noted in previous actions, Barrett teaches that various historical data, including historical rescue inhaler usage, is relevant to predicting future asthmatic events (see e.g. Paras. 9, 11, 22, 100, 134 and claims 1-2). In other words, evidence related to historical asthma events are relevant to the prediction of future asthma events.
Meanwhile, the Ziegler reference demonstrates in e.g. Para. 25 that the claimed airflow parameters are relevant to monitoring the “long term development” of asthma since “a user suffering from asthma may produce different inhalation flow features than a healthy user breathing normally …” Even if Ziegler is interpreted as failing to envision that such flow features are useful in a predictive analysis, as argued by Applicant, such a use would still be obvious in view of the combination because Barrett already demonstrates that it was known that prior asthmatic events are relevant to predicting future ones. As such, one skilled in the art would understand that current inhalation flow features would have value in predicting future asthmatic exacerbations for the same or similar reason that current rescue inhaler usage has that value in Barrett, i.e. since both are indicative of a current asthmatic event which Barrett teaches will be useful in predicting future ones.
II. Rejections under 35 U.S.C. § 101
Applicant’s remarks concerning the previous § 101 rejections have been fully considered but are not persuasive.
Applicant first argues “The application of such a model to determine exacerbation probability based on multiple input parameters-including a peak inhalation flow, an inhalation volume, or an inhalation duration, plus rescue inhalation counts and trends-cannot practically be performed in the human mind, with or without pen and paper. The human mind is not equipped to execute the inference operations of a trained machine learning model on physiological airflow parameters to generate a probability assessment.” The Examiner respectfully disagrees. As noted in the previous action, the human mind is fully capable of determining/calculating probabilities of future events based on present and/or past data, and can become better (trained) at making those determinations more accurately and/or faster by learning over time. The claims here merely implement that mental process in a computing environment, e.g. using a “trained machine learning model” and “a processor” etc.
Applicant next argues that the claims as a whole integrate the mental process into a practical application due to the recitation of (1) the sensor system, (2) the first inhaler, and (3) the generation of a notification via a display device. The Examiner respectfully disagrees.
Concerning (1), the collection of data with a sensor system is considered merely insignificant pre-solution activity of mere data gathering, since it collects the data necessary to carry out the mental process. Applicant argues “this is not mere data gathering-it specifies the particular physiological parameters that must be measured by the sensor, tying the system to a concrete respiratory monitoring application.” This is not a distinction; mere data gathering includes gathering specific types of data.
Concerning (2), as noted in the rejection, the limitations concerning a rescue and/or maintenance medicament is also considered insignificant pre-solution activity since it is only nominally or tangentially related to the invention being claimed (i.e. because the claimed invention involves simply gathering data during a user’s typical use of those medications). In other words, the claims do not ultimately require any treatment or impact the treatment device itself; rather, they simply perform a mental process alongside an otherwise ongoing and existing treatment.
Concerning (3), the step of generating a notification via a display device is considered insignificant post-solution activity since it merely outputs the result of the mental process. Applicant’s argument that “This is not merely outputting data; it generates clinically actionable information to manage a medical condition.” This is not a distinction. The § 101 analysis doesn’t look to whether output data is “actionable” to evaluate whether it qualifies as insignificant post-solution activity. Moreover, all output data is “actionable” in the sense that any output data can be used for further analyses or actions.
Applicant lastly argues that the claimed system as a whole “provides an improvement in the technical field of asthma management … [and] produces a real-world effect-enabling proactive asthma exacerbation risk management-that extends beyond the computer itself.” The Examiner respectfully disagrees. The claims here do not improve the actual technology being used. For instance, the claims here do not set forth an improved type of sensor(s), or an improvement in processor or machine learning technology, or an improved type of output. Rather, the alleged improvement is provided by the steps that could be carried out mentally, but are instead implemented using generic computing technology to provide the predictable and well-known benefits of modern computing technology (e.g. speed and convenience).
Claim Rejections - 35 USC § 101
Claims 1, 3-6, 9-12, 14 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Step 1: All of claims 1, 3-6, 9-12, 14 and 16-20 are directed either to a method or a system.
Step 2A, Prong One: The claims recite a mental process including various “determine …” or “determining …” (see e.g. claims 1, 9, 14 and 19) and “providing … and demarcating …” (see claim 19) which could be performed by the human mind and/or by a human with a physical aid such as pen and paper.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application because the claims merely implement the mental process using generic processing technology and add insignificant extra-solution activity. Specifically: the steps of measuring the rescue inhalation and the parameter related to airflow is considered insignificant pre-solution activity of mere data gathering, since it merely collects the data necessary to carry out the mental process; the limitations concerning a rescue and/or maintenance medicament (see e.g. claims 5-6) is also considered insignificant pre-solution activity since it is only nominally or tangentially related to the invention being claimed (i.e. because the claimed invention involves simply gathering data during a user’s typical use of those medications). Furthermore, merely carrying out mental steps using generic computing technology such as “processor” or “a trained machine learning model” is well established to not amount to an integration into a practical application under the § 101 analysis. See, e.g., MPEP §§ 2106.04(a)(2)(III)(C) and 2106.04(d)(I) and 2106.05(f). Finally, the step of generating a notification via a display device is merely insignificant post-solution activity since it merely outputs the result of the mental process using a generic output modality.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements recited in the claims are generic processing/computing components and generic data collection components including a generic rescue and/or maintenance inhaler and a generic display device. The Examiner previously took official notice that these are basic, generic components which are well-understood, routine and conventional in the medical diagnostic arts, and the claims here merely use them for their well-understood, routine and conventional functions. Applicant' s subsequent reply did not traverse the Examiner's assertion of official notice; therefore, the facts under official notice are now taken to be admitted prior art. See MPEP § 2144.03(C) (“If applicant does not traverse the examiner' s assertion of official notice or applicant' s traverse is not adequate, the examiner should clearly indicate in the next Office action that the common knowledge or well-known in the art statement is taken to be admitted prior art because applicant either failed to traverse the examiner' s assertion of official notice or that the traverse was inadequate.”). Additionally, numerous of the prior art references cited with this action demonstrate that these additional elements were well-understood, routine and conventional. As such, those additional elements cannot be considered “significantly more” than the judicial exception in Step 2B of the § 101 analysis.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-6, 9-12, 14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0102522 A1 to Barrett et al. (hereinafter “Barrett”) in view of US 2019/0030262 A1 to Ziegler et al. (hereinafter “Ziegler”).
Regarding Claims 1, 9, 14 and 20, Barrett teaches a system for determining a probability of an asthma exacerbation in a subject (see e.g. the abstract: “a basis to determine a patient's risk score. This data is analyzed to determine the severity of the patient's risk for an asthma event and is used to send notifications accordingly), the system comprising:
a first inhaler (160; see e.g. Para. 59 explaining that this can be a metered dose inhaler) for delivering a rescue medicament to the subject (see e.g. Para. 59: “Examples of rescue medications that are dispensed by a rescue medicament device 160 include albuterol, salbutamol, levalbuterol, metaproterenol, and terbutaline” and Para. 60: “a rescue medicament device 160 that dispenses rescue medication), the first inhaler comprising a use-detection system (120) configured to determine a rescue inhalation performed by the subject using the first inhaler (see e.g. Para. 61: “a sensor 120 is a physical device that monitors the usage of the medicament dispenser 160. The sensor 120 is either removably attachable to the medicament dispenser without impeding the operation of the medication dispenser, or the sensor 120 is an integrated component that is a native part of the medicament dispenser 160); and
a processor (client device 110 and/or application server 130; see e.g. Para. 87 explaining that either can include a processor 205 and other various computer components) configured to:
determine a number of said rescue inhalations during a first time period (see e.g. Para. 23: “number of rescue puffs taken”; Para. 28: “the number of rescue inhaler usage events for a given day”; Para. 100: “a total number of uses for the time period and a number of uses for each day”; Para. 135: “the number of rescue puffs taken”; Para. 143: “the number of rescue inhaler usage events”); and
determine, using a trained machine learning model (see e.g. Para. 7: “The relationship between these parameters and risk assessment generated for the patient is embodied in a machine learned model. The model, and system more generally, is capable of receiving input values for the parameters and categorizing a patient's risk score to provide a risk assessment with accurate and medically relevant treatment options to mitigate the risk” and Para. 130: “the model 640 is trained using a machine learning technique, examples of which include but are not limited to linear, logistic, and other forms of regression (e.g., elastic net), decision trees (e.g., random forest, gradient boosting), support vector machines, classifiers (e.g. Naïve Bayes classifier), fuzzy matching”), said probability of the asthma exacerbation based on said number of rescue inhalations (see e.g. Para. 129: “the number of rescue usage events for that data” and Para. 138: “calculates the baseline risk threshold based on the total number of usage events over a specified prior time period preceding either the current day during which the risk is being calculated (for either labeling during training or during model use), or more generally during a time period preceding the time of a current/most recent rescue usage event.), wherein the model uses the absolute number of rescue inhalations during the first time period (see e.g. Para. 100: “a total number of uses for the time period and a number of uses for each day” and Para. 138: “calculates the baseline risk threshold based on the total number of usage events over a specified prior time period preceding either the current day during which the risk is being calculated (for either labeling during training or during model use), or more generally during a time period preceding the time of a current/most recent rescue usage event”; also see e.g. Para. 149: “any current rescue event data 605, historical rescue event data 635”) and one or more trends based on the number of rescue inhalations (see e.g. Para. 76: “rescue use trends over time”; also see e.g. Paras. 100-101).
generate a notification, via a display device, indicating the probability of the asthma exacerbation for the user (see e.g. the abstract: “used to send notifications accordingly” and Paras. 94-101 discusses various types of notification displays).
Barrett also teaches a sensor 120 which can measure various usage aspects of the inhaler including inhalation by the user (see Para. 66 of Barrett). Barrett fails to specifically teach one of the specific airflow parameters of “at least one of a peak inhalation flow, an inhalation volume, or an inhalation duration.” Another reference, Ziegler, teaches an analogous invention for measuring various flow parameters of an inhaler using a sensor (16) built into the inhaler, including e.g. peak inhalation flow and inhalation volume and duration (see e.g. Para. 16: “The at least one inhalation flow feature may comprise at least one of a peak inhalation flow, an inhalation flow duration and an inhalation flow volume”; also see Para. 25) which Ziegler teaches is useful for evaluating asthma (see e.g. Para. 25: “by means of the peak inhalation flow a user is able to perform, by means of the total inhalation flow volume and its duration, a health condition of the user may be monitored. E.g., a user suffering from asthma may produce different inhalation flow features than a healthy user breathing normally. By means of this use, e.g., the long term development of a health condition and/or a disease may be monitored”). It would have been obvious to one of ordinary skill in the art as of Applicant's effective filing date to modify Barrett to measure one or more of peak inhalation flow and inhalation volume and duration, as taught by Ziegler, because these parameters are known to be advantageous for monitoring asthma as taught by Ziegler, and because Barrett teaches that a variety of sensor types are broadly envisioned.
Regarding Claim 3, see e.g. Para. 64: “each sensor 120 captures the time and geographical location of the rescue medication event, that is, usages of the rescue medicament device 160, by the patient 111.” Also see the incorporation of Ziegler above.
Regarding Claims 4 and 16, see e.g. Para. 60: “Each patient may be associated with more than one medicament device 160. For example, the patient may have a rescue medicament device 160 that dispenses rescue medication, and a controller medicament device 160 that dispenses controller medication. Similarly, each patient may be associated with more than one sensor 120, each chosen to operate with one of the patient's medicament devices 160.”
Regarding Claims 5-6 and 17-18, see e.g. Para. 59: “Examples of controller medications that are dispensed by a controller medicament device 160 include beclomethasone, budesonide, and fluticasone as well as combinations of those medications with a long-acting bronchodilator such as salmeterol or formoterol. Examples of rescue medications that are dispensed by a rescue medicament device 160 include albuterol, salbutamol, levalbuterol, metaproterenol, and terbutaline.”
Regarding Claim 10, see e.g. Para. 22: “a patient history of events occurring at night”, Para. 134: “a record of rescue events occurring at night” and claim 13.
Regarding Claim 11, see e.g. Para. 127: “This determination for the label of high or low risk is determined based on whether the events associated with the day exceed the baseline threshold for that day”; also see e.g. Paras. 100-101 and 148-150.
Regarding Claim 12, see e.g. claim 18: “wherein the parameters include a number of days where rescue inhaler usage events are monitored by a computing system external to the rescue inhaler unit” and Para. 100: “rescue device usage for the previous week including a total number of uses for the time period and a number of uses for each day”; also see claim 7 and Para. 124: “triggering conditions include … a conclusion of a time interval.”
Regarding Claim 19, see e.g. Para. 76: “a risk analysis may be performed on rescue and controller medication use for multiple patients to identify based on spatial/temporal clusters (or outbreaks) of medication use based on historically significant permutations from individual, geographic, clinical, epidemiologic, demographic, or spatial or temporal baselines or predicted or expected values … rescue use comparisons to other relevant populations.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN R DOWNEY whose telephone number is (571)270-7247. The examiner can normally be reached Monday-Friday 8:30am-5:00pm ET.
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/JOHN R DOWNEY/Primary Examiner, Art Unit 3792
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