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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 2, 2026 has been entered.
Response to Arguments
I. Claim Rejections under 35 U.S.C. § 101
Applicant’s remarks concerning the previous § 101 rejections have been fully considered but are not persuasive.
Applicant’s first argument is that “a trained machine learning model cannot practically be performed in the human mind …” This argument is not persuasive because it mischaracterizes the rejection. The rejection does not assert that the human mind can literally perform a machine learning model; rather, the rejection asserts that the determinations being made in the claims here are ones that could be done mentally, but are implemented using a machine learning model and generic processor technology for the well understood benefits of those technologies, e.g. greatly increased processing speed. 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 the sensor system and the generation of a notification via a display device. The Examiner respectfully disagrees. As noted in the rejections, 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. As also noted in the rejections, 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 lastly argues that the claimed system as a whole “provides an improvement in the technical field of asthma management by delivering more accurate and reliable exacerbation risk information …” 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. 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).
II. Claim Rejections under 35 U.S.C. § 102
Applicant’s remarks concerning the previous § 102 rejections have been fully considered and are persuasive. The § 102 rejections are overcome and withdrawn.
III. Claim Rejections under 35 U.S.C. § 103
Applicant’s remarks concerning the previous § 103 rejections have been fully considered but are not persuasive.
Applicant first remarks that Barrett does “not disclose a sensor that is configured to measure a parameter relating to airflow of any type.” Applicant more specifically argues that detection of occurrences or numbers of inhalations is not equivalent to the detection of an “parameter relating to airflow.” The Examiner respectfully disagrees. An inhalation is an airflow event, and a number of inhalations is a parameter which relates to those airflow events, and thus is a parameter “relating to airflow.” However, this argument is ultimately moot in view of the claim amendments which specify that the parameter must be one of three particular types (which is addressed by the Ziegler reference). Applicant’s next argument concerning the differentiation of a use-detection system and a separate sensor system is similarly moot in view of the updated rejections.
Applicant’s next remarks concerning the “other analyses” in Para. 76 remain unpersuasive for the same reasons explained in the Response to Arguments section of the previous office action.
Finally, concerning the Ziegler reference, Applicant argues that Ziegler is “inherently backward-looking” and “not concerned with predicting future clinical events …” and thus one skilled in the art would not have found it obvious to use Ziegler’s airflow parameters in Barrett’s asthma probability analysis. The Examiner respectfully disagrees. Barrett alone already establishes that asthma predictions are made based on past/historical data analysis. Ziegler establishes that “user suffering from asthma may produce different inhalation flow features than a healthy user breathing normally” and that such airflow parameters are useful in monitoring “the long term development of a health condition and/or a disease” (see Para. 25 cited in the rejection). One skilled in the art looking at these references would appreciate that a user’s current and past asthma condition data, including the more specific types of airflow data seen in Ziegler, would be relevant and useful in making a more accurate future prediction or probability determination.
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
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