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 .
DETAILED ACTION
Response to Amendment
The present Office Action is in response to the Request for Continued Examination dated 02/17/2026.
In the amendment dated 02/17/2026, the following occurred: Claim 1 was amended. Claims 23-96 were canceled.
Claims 1-22 are currently pending.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/17/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/17/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a system for assessment of users health, which are within a statutory category.
Step 2A1
Regarding claim 1, the limitation of receive a plurality of usage events associated with a plurality of different users, wherein each usage event is associated with an inhaler, a medicament type, and a user of the plurality of different users, and wherein each usage event comprises a time associated with the usage event and one or more inhalation parameters of the usage event, wherein the one or more inhalation parameters comprise a peak inhalation flow (PIF) for the usage event or an inhalation volume for the usage event; train […] using training data via an unsupervised learning method, wherein the training data comprises the time and the one or more inhalation parameters associated with each of the plurality of usage events; determine a compliance score for a user; cause to generate a notification indicating the compliance score for the user when the compliance score is below a threshold as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to receive a plurality of usage events, train […] using training data via an unsupervised learning method, determine a compliance score and cause to generate a notification indicating the compliance score when the compliance score is below a threshold in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receive…train…determine…and cause to generate a compliance score” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a processor and a memory, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Note that the broadest reasonable interpretation of “train a machine learning algorithm using training data via an unsupervised learning method” and in light of the disclosure, amounts to the performance of mathematical calculations and discernment of mathematical relationships. Evidence for this determination can be found in paragraphs 196-198, where several well-known machine learning algorithms (e.g., k-Nearest Neighbor (kNN), Learning Vector Quantization (LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL), Support Vector Machines (SVM), a linear regression algorithm, a logistic regression algorithm, a Naive Bayes classifier, and/or a weighted average algorithm), are optionally employed to train the analytical subsystem. The aforementioned algorithms all execute a multitude of mathematical calculations and operations to ascertain relationships among training datasets and predictive outcomes. Similarly, the supervised machine learning algorithms (e.g., XGBoost) articulated in the disclosure amount to mathematical algorithms that use complex loss functions to tune the model(s). Consequently, the training of said algorithms is reasonably understood to amount to the performance and utilization of mathematical concepts in order to achieve reliable predictions regarding a given dataset. Thus given the broadest reasonable interpretation, the Examiner interprets the training to be implemented using existing, known mathematical techniques. As such, training a machine learning algorithm using training data via an unsupervised learning method is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a processor and a memory. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for performing generic computer functions, see Specification at para. 0097 and 0326) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 further recites the additional element of a machine learning algorithm. This additional element is interpreted to be the application of mathematical relationships and equates to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, this additional elements does not integrate the abstract idea into a practical application.
Claim 1 further recites the additional element of a display device to generate a notification. This additional element is recited at a high level of generality (i.e. a general means to output information) and amount to extra solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, this additional elements does not integrate the abstract idea into a practical application.
Step 2B
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 integration of the abstract idea into a practical application, the additional elements of a processor and memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional element of a machine learning algorithm was determined to be “apply it” to the abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional element of a display device to generate a notification was considered extra-solution activity. Extra-solution activity cannot provide significantly more than the judicial exception if it amounts to well-understood, routine, and conventional activity. Per MPEP § 2106.05(d)(1)(2), when making a determination as to the well-understood, routine, and conventional nature of an additional element, examiners may cite a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). In light of Finitsis et al. (Finitsis DJ, Pellowski JA, Johnson BT (2014) Text Message Intervention Designs to Promote Adherence to Antiretroviral Therapy (ART): A Meta-Analysis of Randomized Controlled Trials. PLOS ONE 9(2): e88166), hereinafter Finitsis, which describes in paragraph three of the Introduction that text-message based reminder systems have been widely studied to increase ART adherence among patients, the generating of a notification that contains compliance information and/or user attributes is demonstrably well-understood, routine, and conventional activity. Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-22 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 further merely describe(s) the one or more inhalation parameters comprises both the PIF for the usage event and the inhalation volume for the usage event. Claim(s) 3 further merely describe(s) at least a subset of the plurality of usage events are maintenance usage events that are associated with a maintenance medicament type and a dosing schedule for the maintenance medicament type; and wherein the training data further comprises an adherence ratio that indicates the user's adherence to the dosing schedule for the maintenance medicament type for each user of the plurality of users that is associated with at least one maintenance usage event. Claim(s) 4 further merely describe(s) the adherence is determined based on a comparison between a number of maintenance usage events of the user over a predetermined period of time and a number of maintenance usage events indicated by the dosing schedule for the predetermined period of time. Claim(s) 5 further merely describe(s) at least a subset of the plurality of usage events are rescue usage events that are associated with a rescue medicament type; and wherein the training data further comprises a user's frequency of rescue usage events for each user of the plurality of users that is associated with at least one rescue usage event. Claim(s) 6 further merely describe(s) the user's frequency of rescue usage events comprises a comparison between a number of rescue usage events of the user over a predetermined period of time and a baseline number of rescue usage events of the user. Claim(s) 7 further merely describe(s) the user's frequency of rescue usage events comprises an average number of daily rescue usage events for the user for a predetermined period of time. Claim(s) 8 further merely describe(s) the user's frequency of rescue usage events comprises an absolute number of rescue usage events for the user for a predetermined period of time. Claim(s) 9 further merely describe(s) first subset of the plurality of usage events are maintenance usage events that are associated with a maintenance medicament type and a dosing schedule for the maintenance medicament type, and a second subset of the plurality of usage events are rescue usage events that are associated with a rescue medicament type; and wherein the training data further comprises an adherence ratio that indicates the user's adherence to the dosing schedule for the maintenance medicament type for each user of the plurality of users that is associated with at least one maintenance usage event, and a user's frequency of rescue usage events for each user of the plurality of users that is associated with at least one rescue usage event. Claim(s) 10 further merely describe(s) the training data comprises one or more of: a number of usage events of a rescue medicament type for a user of the plurality of users in a last predetermined number of days; or a number of missed usage events of a maintenance medicament type for a user of the plurality of users over the last predetermined number of days. Claim(s) 11 further merely describe(s) the training data comprises any combination of: a percent change in inhalation peak flow for a previous number of usage events for a user of the plurality of users compared to an average inhalation peak flow of the user; or a percent change in inhalation volume for a previous number of usage events for a user of the plurality of users compared to an average inhalation volume of the user. Claim(s) 12 further merely describe(s) wherein the time associated with each of the plurality of usage events is an indication of whether the usage event occurred during the daytime or nighttime. Claim(s) 13 further merely describe(s) determine an environmental condition for each of the plurality of usage events using a respective time and geographic location associated with the usage event; and wherein the training data further comprises the environmental condition for each of the plurality of usage events. Claim(s) 14 further merely describe(s) the environmental condition comprises any combination of temperature, humidity, outdoor air pollutants, particulate matter of 2.5 microns or smaller (PM2.5), particulate matter of 10 microns or smaller (PM10), ozone, nitrogen dioxide (N02), or sulfur dioxide (SO2). Claim(s) 15 further merely describe(s) the unsupervised learning method comprises a clustering method. Claim(s) 16 further merely describe(s) the unsupervised learning method comprises a k-means or c-means clustering method. Claim(s) 17 further merely describe(s) the display device is associated with the user or a health care provider of the user. Claim(s) 18 further merely describe(s) the compliance score indicates how compliant the user has been during usage events in a last predetermined number of days. Claim(s) 19 further merely describe(s) the compliance score further indicates how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament. Claim(s) 20 further merely describe(s) determine an attribute that the user should improve upon to improve their compliance score; and cause the display device to generate an attribute notification indicating the attribute for the user. Claim(s) 21 further merely describe(s) the attribute comprises one or more of the following: taking a maintenance medicament at a different time of day, increasing the PIF of future usage events, or increasing inhaled volume of future usage events. Claim(s) 22 further merely describe(s) determine a significance factor for each of a plurality of attributes; and determine the attribute that the user should improve upon to improve their compliance score based on the significance factor for each of the plurality of attributes. Claims 2-22 further define the abstract idea and are rejected for the same reason presented above with respect to claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-11, 15-18 and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Haldar et al. (Haldar et al. Cluster Analysis and Clinical Asthma Phenotypes. American Journal of Respiratory and Critical Care Medicine 178(3) pp 217-224. (2008)), hereinafter Haldar, in view of Gondalia (WO 2020/168138), hereinafter Gondalia
REGARDING CLAIM 1
Haldar discloses a system for personalized assessment of a user's respiratory health (Haldar at pg. 218, para. 1 and 3 that teaches a cluster analysis used to classify clinical phenotypes of asthma patients (interpreted by Examiner as assessment of user’s respiratory health)), receive a plurality of usage events associated with a plurality of different users (Haldar at pg. 219, Subjects, para. 2-3, that teach the collection of patient data during the study of three discrete patient populations afflicted with asthma, some of whom were treated with steroid therapy via an inhaler (interpreted by Examiner as means to receive a plurality of usage events associated with a plurality of different users)), wherein each usage event is associated with an inhaler, a medicament type, and a user of the plurality of different users (Haldar at pg. 219, Results; Tables 1-2, that teach the analysis of patients' use of inhaled corticosteroids, methacholine, and/or bronchodilators), and wherein each usage event comprises a time associated with the usage event and one or more inhalation parameters of the usage event, wherein the one or more inhalation parameters comprise a peak inhalation flow (PIF) for the usage event or an inhalation volume for the usage event (Haldar at Tables 1-4, that teach the recording of inhaled corticosteroid per day and the dosage being represented in a ratio of dose per micrograms per day (interpreted by Examiner as a time associated with the usage event and one or more inhalation parameters)); train a machine learning algorithm using training data via an unsupervised learning method, wherein the training data comprises the time and the one or more inhalation parameters associated with each of the plurality of usage events (Haldar at pg. 218, paragraph 3; pg. 219, Cluster Analysis Methodology; Tables 1-3 that teach the use a k-means clustering algorithm to separate the patient populations into clinically significant clusters, wherein the tables list the input variables upon which the clustering model is trained, and the dosage amount constitutes an inhalation volume (interpreted by Examiner as train a machine learning algorithm using training data via an unsupervised learning method, wherein the training data comprises the time and the one or more inhalation parameters associated with each of the plurality of usage events)); determine, using the trained machine learning algorithm, a compliance score for a user (Haldar at Tables 1-3, teaches an asthma control score (interpreted by Examiner as the compliance score of Gondalia below) and the computation of inhaled corticosteroid and long-acting bronchodilator use, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as the trained machine learning algorithm));
Haldar does not explicitly disclose the system comprising: a memory comprising compute-executable instructions; and a processor coupled to the memory, wherein the processor is operative to: cause a display device to generate a notification indicating the compliance score for the user when the compliance score is below a threshold, however Gondalia discloses:
the system comprising: a memory comprising compute-executable instructions; and a processor coupled to the memory, wherein the processor is operative to (Gondalia at [00119]-[00120], [00124] and Fig. 6): cause a display device to generate a notification indicating the compliance score for the user when the compliance score is below a threshold (Gondalia at [0018], [0021], [0025], [0033], [0073], [0088], and [00112] that teach generating notifications to a client device based on the usage technique record, calculating a user asthma control score and a score representing the quality of individual usage events sent to user client devices (Interpreted by Examiner as generating a compliance score of a user on a display device) Moreover, [00134] teaches that notifications may be sent to the provider for patients exhibiting consistent improper inhaler use, which may be triggered by a particular threshold of consistent improper use (interpreted by Examiner as when the compliance score is below a threshold, which Examiner interprets would indicate improper use)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the functionalities of Haldar to incorporate causing a display device to generate a notification indicating the compliance score for the user when the compliance score is below a threshold as taught by Gondalia, with the motivation of ensuring proper usage technique. (Gondalia at [0013]).
REGARDING CLAIM 2
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the one or more inhalation parameters comprises both the PIF for the usage event and the inhalation volume for the usage event (Haldar at tables 1-3 teach the peak flow variability (interpreted by Examiner as the PIF for the usage event) and the dose of inhaled corticosteroid (interpreted by Examiner as the inhalation volume of the usage event) being included in the patient data analysis).
REGARDING CLAIM 3
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein at least a subset of the plurality of usage events are maintenance usage events that are associated with a maintenance medicament type and a dosing schedule for the maintenance medicament type (Haldar at pg. 219 teaches a cluster analysis methodology and results, para. 2-3 and table 3 that teach the measurement of maintenance corticosteroid/methacholine use by a patient (interpreted by Examiner as the plurality of usage events are maintenance usage events)); and wherein the training data further comprises an adherence ratio that indicates the user's adherence to the dosing schedule for the maintenance medicament type for each user of the plurality of users that is associated with at least one maintenance usage event (Haldar at table 3 teaches the measurement of maintenance corticosteroid/methacholine use as a ratio of total anticipated usage, wherein the tables list the input variables (per their labeling) upon which the clustering model is trained. Moreover, see Halder, Clustering Methodology, which teaches that the variables included in tables 1-3 were chosen for cluster modeling (interpreted by Examiner as the training data further comprises an adherence ratio that indicates the user's adherence to the dosing schedule for the maintenance medicament type)).
REGARDING CLAIM 4
Haldar and Gondalia disclose the limitation of claim 3.
Haldar further discloses:
The system of claim 3, wherein the adherence is determined based on a comparison between a number of maintenance usage events of the user over a predetermined period of time and a number of maintenance usage events indicated by the dosing schedule for the predetermined period of time (Haldar at Table 3 teaches the measurement of maintenance corticosteroid use as a ratio of total anticipated usage, which implies a preset schedule of usage for a patient over the course of a year (interpreted by Examiner as a comparison between a number of maintenance usage events of the user over a predetermined period of time, indicated by the dosing schedule)).
REGARDING CLAIM 5
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein at least a subset of the plurality of usage events are rescue usage events that are associated with a rescue medicament type; and wherein the training data further comprises a user's frequency of rescue usage events for each user of the plurality of users that is associated with at least one rescue usage event (Haldar at pg. 219, Statistical Methods; Tables 1-3 that teach the analysis of the rate of rescue corticosteroid events and long-term bronchodilator uses by a patient, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as the frequency of rescue usage events)).
REGARDING CLAIM 6
Haldar and Gondalia disclose the limitation of claim 5.
Haldar further discloses:
The system of claim 5, wherein the user's frequency of rescue usage events comprises a comparison between a number of rescue usage events of the user over a predetermined period of time and a baseline number of rescue usage events of the user (Haldar at pg. 219, Results, paragraphs 1, 3-4; pg. 220, 2-4; Tables 1-3 which teach the frequency of rescue corticosteroid and long-term bronchodilator uses and the comparison in rescue corticosteroid uses between different patient clusters (interpreted by Examiner as the user's frequency of rescue usage events comprising a comparison between a number of rescue usage events of the user over a predetermined period of time and a baseline number of rescue usage events of the user)).
REGARDING CLAIM 7
Haldar and Gondalia disclose the limitation of claim 5.
Haldar further discloses:
The system of claim 5, wherein the user's frequency of rescue usage events comprises an average number of daily rescue usage events for the user for a predetermined period of time (Haldar at Tables 2- 3 which teach the rate of severe asthma exacerbations in which oral corticosteroids were administered over the course of a year with respect to a given diagnostic cluster (interpreted by Examiner as an average number of daily rescue usage events for the user for a predetermined period of time)).
REGARDING CLAIM 8
Haldar and Gondalia disclose the limitation of claim 5.
Haldar further discloses:
The system of claim 5, wherein the user's frequency of rescue usage events comprises an absolute number of rescue usage events for the user for a predetermined period of time (Haldar at Tables 2-3 which teach the rate of severe asthma exacerbations in which oral corticosteroids were administered being calculated on a "per patient" basis, implying that a total number of occurrences had to be quantified prior to obtaining a "per patient" rate (interpreted by Examiner as an absolute number of rescue usage events for the user for a predetermined period of time)).
REGARDING CLAIM 9
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein a first subset of the plurality of usage events are maintenance usage events that are associated with a maintenance medicament type and a dosing schedule for the maintenance medicament type, and a second subset of the plurality of usage events are rescue usage events that are associated with a rescue medicament type (Haldar at pg. 219, Results, paragraphs 1, 3-4; pg. 220, 2-4; Tables 1-3 which teach the frequency of methacholine uses (maintenance events) and corticosteroids/long-acting bronchodilator uses (rescue uses) by a patient (interpreted by Examiner as the first and second subsets)); and wherein the training data further comprises an adherence ratio that indicates the user's adherence to the dosing schedule for the maintenance medicament type for each user of the plurality of users that is associated with at least one maintenance usage event (Haldar at Tables 2-3 which teaches the measurement of maintenance corticosteroid use as a ratio of total anticipated usage, which implies a preset schedule of usage for a patient over the course of a year, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as an indication to the user's adherence to the dosing schedule)), and a user's frequency of rescue usage events for each user of the plurality of users that is associated with at least one rescue usage event (Haldar at pg. 219, Results, paragraphs 1, 3-4; pg. 220, 2-4; Tables 1-3 which teach the rate of severe asthma exacerbations in which oral corticosteroids were administered over the course of a year with respect to a given diagnostic cluster, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as a user's frequency of rescue usage events)).
REGARDING CLAIM 10
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the training data comprises one or more of: a number of usage events of a rescue medicament type for a user of the plurality of users in a last predetermined number of days; or a number of missed usage events of a maintenance medicament type for a user of the plurality of users over the last predetermined number of days (Haldar, pg. 219, Results, paragraphs 1, 3-4; pg. 220, 2-4; Tables 1-3 which teach the frequency of methacholine uses (maintenance events) and corticosteroids/long-acting bronchodilator uses (rescue uses) by a patient; and is met by the measurement of maintenance corticosteroid use as a ratio of total anticipated usage, which implies a preset schedule of usage for a patient over the course of a year, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as a number of usage events of a rescue medicament type for a user of the plurality of users in a last predetermined number of days)).
REGARDING CLAIM 11
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the training data comprises any combination of: a percent change in inhalation peak flow for a previous number of usage events for a user of the plurality of users compared to an average inhalation peak flow of the user; or a percent change in inhalation volume for a previous number of usage events for a user of the plurality of users compared to an average inhalation volume of the user (Haldar at Tables 1-3 which teach the measurement of the peak flow variability with respect to the mean for patient clusters, wherein the tables list the input variables upon which the clustering model is trained (interpreted by Examiner as a percent change in inhalation peak flow for a previous number of usage events for a user of the plurality of users compared to an average inhalation peak flow of the user)).
REGARDING CLAIM 15
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the unsupervised learning method comprises a clustering method (Haldar at pg. 218, paragraph 3; pg. 219, Cluster Analysis Methodology which teach the use of a k-means clustering machine learning algorithm).
REGARDING CLAIM 16
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the unsupervised learning method comprises a k-means or c-means clustering method (Haldar at pg. 218, paragraph 3; pg. 219, Cluster Analysis Methodology which teach the use of a k-means clustering machine learning algorithm).
REGARDING CLAIM 17
Haldar and Gondalia disclose the limitation of claim 1.
Haldar does not explicitly disclose wherein the display device is associated with the user or a health care provider of the user, however Gondalia further discloses:
The system of claim 1, wherein the display device is associated with the user or a health care provider of the user (Gondalia at [0018], [0021], [0025], [0033], [0048] and [00114] teach the allocation of a client device to a user in order to communicate adherence information to said user (interpreted by Examiner as the display device is associated with the user or a health care provider of the user)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the functionalities of Haldar to incorporate the display device the is associated with the user or a health care provider of the user as taught by Gondalia, with the motivation of ensuring proper usage technique. (Gondalia at [0013]).
REGARDING CLAIM 18
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the compliance score indicates how compliant the user has been during usage events in a last predetermined number of days (Haldar at Tables 1-3 teaches the individual clusters being assigned poor, moderate, or good compliance based on a number of attributes, including patient use of corticosteroids and other asthma medications over the course of a year (12 month) period (interpreted by examiner as the compliance score indicates how compliant the user has been during usage events in a last predetermined number of days)).
REGARDING CLAIM 20
Haldar and Gondalia disclose the limitation of claim 1.
Haldar further discloses:
The system of claim 1, wherein the processor is operative to: determine, using the trained machine learning algorithm, an attribute that the user should improve upon to improve their compliance score (Haldar at Tables 1-3 teach virtue of the provided p-values, which demonstrate the significance of each patient attribute; for example, certain attributes have p-values less than 0.001, indicating that those attributes should be modified to improve compliance and thus phenotypic outcomes (interpreted by Examiner as determine, using the trained machine learning algorithm, an attribute that the user should improve upon));
Haldar does not explicitly disclose cause the display device to generate an attribute notification indicating the attribute for the user, however Gondalia further discloses:
and cause the display device to generate an attribute notification indicating the attribute for the user (Gondalia at [0038], [0044], [0091], [0093], [0095], [00104]-[00105] and [00108] teach the transmission of technique and adherence notifications and recommendations to a user via the client device (interpreted by Examiner as generate an attribute notification indicating the attribute for the user)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the functionalities of Haldar to incorporate cause the display device to generate an attribute notification indicating the attribute for the user as taught by Gondalia, with the motivation of ensuring proper usage technique. (Gondalia at [0013]).
REGARDING CLAIM 21
Haldar and Gondalia disclose the limitation of claim 20.
Haldar does not explicitly disclose wherein the attribute comprises one or more of the following: taking a maintenance medicament at a different time of day, increasing the PIF of future usage events, or increasing inhaled volume of future usage events, however Gondalia further discloses:
The system of claim 20, wherein the attribute comprises one or more of the following: taking a maintenance medicament at a different time of day, increasing the PIF of future usage events, or increasing inhaled volume of future usage events (Gondalia at [00106], [00108], [00110], [00130] and [00138] teach recommendations provided to a patient regarding the timing of inhaler devices to enhance user techniques and, thus, clinical outcomes (interpreted by Examiner as taking a maintenance medicament at a different time of day)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the attributes of Haldar to incorporate the attribute comprising taking a maintenance medicament at a different time of day as taught by Gondalia, with the motivation of ensuring proper usage technique. (Gondalia at [0013]).
REGARDING CLAIM 22
Haldar and Gondalia disclose the limitation of claim 20.
Haldar further discloses:
The system of claim 20, wherein the processor is operative to: determine, using the trained machine learning algorithm, a significance factor for each of a plurality of attributes (Haldar at Tables 1-3 teach by the determination of significance values (p values) for each of the patient data attributes in determining their cluster assignment and ultimate adherence (interpreted by Examiner as a significance factor for each of a plurality of attributes)); and determine, using the trained machine learning algorithm, the attribute that the user should improve upon to improve their compliance score based on the significance factor for each of the plurality of attributes (Haldar, Tables 1-3, is met by virtue of the provided p-values, which demonstrate the significance of each patient attribute; for example, certain attributes have p-values less than 0.001, likely indicating significance (interpreted by Examiner as determining the attribute that the user should improve upon to improve their compliance score based on the significance factor for each of the plurality of attributes)).
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Haldar et al. (Haldar et al. Cluster Analysis and Clinical Asthma Phenotypes. American Journal of Respiratory and Critical Care Medicine 178(3) pp 217-224. (2008)), hereinafter Haldar, in view of Gondalia (WO 2020/168138), hereinafter Gondalia, in further view of Tibble et al. (Tibble, H., Chan, A., Mitchell, E.A. et al. A data-driven typology of asthma medication
adherence using cluster analysis. Sci Rep 10, 14999 (2020)), hereinafter Tibble.
REGARDING CLAIM 12
Haldar and Gondalia disclose the limitation of claim 1.
Haldar and Gondalia do not explicitly disclose wherein the time associated with each of the plurality of usage events is an indication of whether the usage event occurred during the daytime or nighttime, however Tibble further discloses:
The system of claim 1, wherein the time associated with each of the plurality of usage events is an indication of whether the usage event occurred during the daytime or nighttime (Tibble at pg. 2, Measures of Adherence; pg. 4, Data cleaning and missing data teach the recording of patient dosages on a twice daily regimen (interpreted by Examiner as an indication that the usage event occurred during the daytime)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the time related information associated with inhaler usage of Haldar and Gondalia to incorporate an indication that the usage event occurred during the daytime as taught by Tibble, with the motivation of measuring patient adherence to a regular medication schedule (Tibble at pg. 2, Measures of Adherence).
REGARDING CLAIM 19
Haldar and Gondalia disclose the limitation of claim 18.
Haldar and Gondalia do not explicitly disclose wherein the compliance score further indicates how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament, however Tibble further discloses:
The system of claim 18, wherein the compliance score further indicates how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament (Tibble at Discussion, paragraph 1, fig. 2 teach the individual clusters having poor, moderate, or good compliance based on a number of patient attributes, including frequency of taking methacholine and corticosteroids (interpreted by Examiner as wherein the compliance score further indicates how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the compliance score of Haldar and Gondalia to incorporate the compliance score further indicating how adherent the user has been with respect to a dosing schedule associated with a maintenance medicament as taught by Tibble, with the motivation of measuring patient adherence to a regular medication schedule (Tibble at pg. 2, Measures of Adherence).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Haldar et al. (Haldar et al. Cluster Analysis and Clinical Asthma Phenotypes. American Journal of Respiratory and Critical Care Medicine 178(3) pp 217-224. (2008)), hereinafter Haldar, in view of Gondalia (WO 2020/168138), hereinafter Gondalia, in further view of Son et al. (Son et al. A Framework for Smart Asthma Management. MIS Quarterly 44(1) pp 285-303. March 2020), hereinafter Son.
REGARDING CLAIM 13
Haldar and Gondalia disclose the limitation of claim 1.
Haldar and Gondalia do not explicitly disclose determine an environmental condition for each of the plurality of usage events using a respective time and geographic location associated with the usage event; and wherein the training data further comprises the environmental condition for each of the plurality of usage events, however Son further discloses:
The system of claim 1, wherein the processor is operative to: determine an environmental condition for each of the plurality of usage events using a respective time and geographic location associated with the usage event (Son at Table 2 (pg. 291) teaches the recording of environmental factors (pollution level, humidity, temperature, etc.) and timestamp associated with patients in the smart asthma management system (SAM) data based on geographic location (interpreted by Examiner as determine an environmental condition for each of the plurality of usage events using a respective time and geographic location associated with the usage event)); and wherein the training data further comprises the environmental condition for each of the plurality of usage events (Son at pg. 297 teaches the training of the various models (SVM, regression, CG-HMM models) using the SAM data (interpreted by Examiner as the training data further comprises the environmental condition for each of the plurality of usage events)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the functionalities of Haldar and Gondalia to incorporate determining an environmental condition for each of the plurality of usage events using a respective time and geographic location associated with the usage event, wherein the training data further comprises the environmental condition for each of the plurality of usage events as taught by Son, with the motivation better identifying unusual inhaler usage (Son at pg. 289, Model Development).
REGARDING CLAIM 14
Haldar and Gondalia disclose the limitation of claim 1.
Haldar, Gondalia and Son disclose the limitation of claim 13.
Haldar and Gondalia do not explicitly disclose wherein the environmental condition comprises any combination of temperature, humidity, outdoor air pollutants, particulate matter of 2.5 microns or smaller (PM2.5), particulate matter of 10 microns or smaller (PM10), ozone, nitrogen dioxide (N02), or sulfur dioxide (SO2), however Son further discloses:
The system of claim 13, wherein the environmental condition comprises any combination of temperature, humidity, outdoor air pollutants, particulate matter of 2.5 microns or smaller (PM2.5), particulate matter of 10 microns or smaller (PM10), ozone, nitrogen dioxide (N02), or sulfur dioxide (SO2) (Son at pg. 286, paragraph 4; pg. 291; Table 2 teach the environmental conditions including humidity, temperature, and pollutants level in terms of PM2.5 measures of particulate matter).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the environmental condition of Haldar, Gondalia and Son to incorporate temperature, humidity, outdoor air pollutants as taught by Son, with the motivation of measuring the effect of environmental factors on inhaler usage or asthma frequency, as such factors are triggers of asthma usage (Son at pg. 291).
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Lou (US 2008/0126131) discloses predictive modeling and risk stratification of a medication therapy regimen. Sezan (US 2016/0106935) discloses breathprint sensor systems, smart inhalers and methods for personal identification.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
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