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 Applicant’s communication filed on June 9, 2026.
Claims 1, 3, 20, 22, and 36 have been amended and are hereby entered.
Claims 1-3, 6-8, 11, 20-22, 26-27, 30, 32 and 35-39 are currently pending and have been examined.
This action is made FINAL.
Priority
Acknowledgment is made of Applicant’s claim for priority under 35 U.S.C. § 371 of International Application No. PCT/US2020/057196, filed on October, 23, 2020, which claims priority from Provisional Application No. 62/928,937, filed on October, 31, 2019.
Claim Objections
Claims 3 and 22 are objected to because of the following informalities:
Claim 3 recites” optimized by the least one hyperparameter” in line 3. For purposes of examination and compact prosecution, the Examiner interprets this limitation to read “optimized by the at least one hyperparameter”.
Claim 22 recites “at least one hyperparameter” in line 3. However, independent claim 20 has been amended to recite “at least one hyperparameter” and thus it’s unclear whether this is the same hyperparameter or a different hyperparameter recited in dependent claim 22. For purposes of examination and compact prosecution, the Examiner interprets this limitation to be the same hyperparameter as recited in independent claim 20.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule” in claims 1, 20 and 36
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Applicant’s specification describes the training submodule to be a submodule of the regression module and is responsible for outputting variable coefficients where “The variable coefficients are the relative value for each of the environmental parameters for the specific primary patient that may trigger a respiratory event.” (see para 121). This is accomplished through performing regression analysis (see para 124).
Applicant’s specification describes the population tuning submodule to also be a submodule of the regression module and is responsible for identifying the optimal hyperparameters by performing, e.g., k-fold cross validation (see paras 122-123).
Further, Applicant’s specification states the following: “As used in this application, the terms "component," "module," "system," or the like, generally refer to a computer-related entity, either hardware (e.g., a circuit), a combination of hardware and software, software, or an entity related to an operational machine with one or more specific functionalities” (see para 141; emphasis added). Therefore, the claim limitations will be interpreted to be hardware AND software submodule(s) (see MPEP § 2181(II)(B) wherein when the supporting disclosure for a computer-implemented invention discusses the implementation of the functionality of the invention through hardware, software, or a combination of both, a question can arise as to which mode of implementation supports the means-plus-function limitation. The language of 35 U.S.C. 112(f) requires that the recited "means" for performing the specified function shall be construed to cover the corresponding "structure or material" described in the specification and equivalents thereof. Therefore, by choosing to use a means-plus-function limitation and invoke 35 U.S.C. 112(f) applicant limits that claim limitation to the disclosed structure, i.e., implementation by hardware or the combination of hardware and software, and equivalents thereof. Therefore, the examiner should not construe the limitation as covering pure software implementation).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 20-22, 26-27, 30, 32 and 35-39 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 recites the limitation "the collected activation data" in line 21. There is insufficient antecedent basis for this limitation in the claim. For purposes of examination and compact prosecution, Examiner interprets this limitation to be the collected usage data as previously recited in claim 20. Claims 21-22, 26-27, 30, 32 and 35 are further rejected as being dependent on a rejected base claim.
Claim 36 recites the limitations "the activation data" in line 3 and “the population of patients” in line 4. There is insufficient antecedent basis for these limitations in the claim. Claims 37-39 are further rejected as being dependent on a rejected base claim.
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 1-3, 6-8, 11, 20-22, 26-27, 30, 32 and 35-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claims 1 and 20 are each directed to a method and Claim 36 is directed to a system. Therefore, all claims fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent method and system claims 1 and 36, taking claim 1 as exemplary, recite the following limitations: A method for determining conditions that trigger a respiratory ailment, the method comprising: collecting data on activation of a plurality of respiration medicament devices, to deliver respiration medicament to patients in a population of patients, wherein activation is determined by a sensor in each respiration medicament device: storing the activation data and patient contextual parameter data related to each activation event for the population of patients; implementing at least one discretization protocol on the patient contextual parameter, wherein the patient contextual parameter data is grouped into defined ranges; pairing the sensor in each respiration medicament device to a client device associated with a patient in the plurality of patients using a passkey, wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device; accessing the activation data and the contextual parameter data grouped by the at least one discretization protocol for a primary patient in the population of patients over a certain period of time; determining the occurrence of rescue events based on the collected activation data; determining a coefficient for at least one contextual parameter as a triggering event for the primary patient based on the correlation of the contextual parameter and the rescue events; and providing a comparison of the coefficient for the at least one contextual parameter for the primary patient and distributions of coefficients of a triggering event for the population of patients based on the correlation of the contextual parameter and the rescue events for the population of patients; determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data optimized by at least one hyperparameter wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule; and alerting the primary patient if the coefficient is in a high percentile of a distribution of coefficients of the population of patients, wherein an alert includes providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers.
Further, the method of claim 20 recites the following limitations: A method to evaluate a triggering event for a respiratory ailment of a primary patient, the method comprising: collecting usage data from activation of a plurality of treatment devices from a population of patients from a communication interface, wherein activation is determined by a sensor in each treatment device; collecting contextual parameter data corresponding to the population of patients from the communication interface; storing the collected usage data and contextual parameter data in a storage device; determining a coefficient for a triggering event from the contextual parameter and usage data for each patient of the population of patients; pairing the sensor in each treatment device to a client device associated with a patient in the population of patients using a passkey, wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device; implementing at least one discretization protocol on the patient contextual parameter, wherein the patient contextual parameter data is grouped into defined ranges; determining a coefficient for the triggering event for the primary patient based on data relating to the contextual data grouped by the at least one discretization protocol and usage data for the primary patient; determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data optimized by at least one hyperparameter wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule; providing a comparison of the coefficient of the primary patient in relation to a distribution of the coefficients for the population of patients; and alerting the primary patient if the coefficient is in a high percentile of a distribution of coefficients of the population of patients, wherein an alert includes providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations above, 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 recitation of generic computer components. That is, other than reciting generic computer components, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the identified additional elements (see below), this claim encompasses a person determining whether or not to notify a patient, presumably to take some sort of action/response (which would be a way of managing their subsequent behavior) in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). 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” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1, 20 and 36 recite the following additional elements beyond the abstract idea:
Claims 1, 20 and 36 recite “activation of a plurality of respiration medicament (claims 1 and 36)/treatment (claim 20) devices”
Claims 1, 20 and 36 recite “wherein activation is determined by a sensor in each respiration medicament (claims 1 and 36)/treatment (claim 20) device”
Claims 1, 20 and 36 recite “pairing the sensor in each respiration medicament device (claims 1 and 20)/the sensor in each respiration medicament device is paired (claim 36) to a client device associated with a patient in the plurality of patients using a passkey, wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device”
Claims 1, 20 and 36 recite “providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers.”
Claims 1, 20 and 36 recite a training submodule and a population tuning submodule (see claim interpretation above)
Claims 20 further recites a storage device and a communication interface
Claim 36 further recites a storage device, one or more processors and instructions.
This judicial exception is not integrated into a practical application. In particular, the claims recite "wherein activation is determined by a sensor in each respiration medicament (claims 1 and 36)/treatment (claim 20) device” , "pairing the sensor in each respiration medicament device (claims 1 and 20)/the sensor in each respiration medicament device is paired (claim 36) to a client device associated with a patient in the plurality of patients using a passkey", a storage device, a communication interface, one or more processors, instructions, a training submodule and a population tuning submodule which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the communication interface displays data, allows users to input data/collects data from users, etc. (see Applicant’s specification paras 14, 16, 30, 44). The instructions are interpreted as purely software.
Further, the additional elements of (1) “activation of a plurality of respiration medicament (claims 1 and 36)/treatment (claim 20) devices”, (2) “wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device”, and (3) “providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers” are each being interpreted as insignificant extra-solution activity. The activation step (1) is recited at a high level of generality and amounts to mere data gathering, which is a form of extra-solution activity, and does not add a meaningful limitation to the claimed invention. The syncing and transmitting step (2) and the providing step (3) are recited at a high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, which is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
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. Therefore, Claims 1, 20 and 36 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
The claim does 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 using a sensor in each respiration medicament/treatment device to determine activation, pairing the sensor in each respiration medicament device to a client device using a passkey, a storage device, a communication interface, one or more processors, instructions, a training submodule, and a population tuning submodule to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of (1) “activation of a plurality of respiration medicament (claims 1 and 36)/treatment (claim 20) devices”, (2) “wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device”, and (3) “providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers” were considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field.
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. See MPEP §2106.05(d)(II).
This listing is not meant to imply that all computer functions are well‐understood, routine, conventional activities, or that a claim reciting a generic computer component performing a generic computer function is necessarily ineligible. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. See MPEP §2106.05(d)(II) – emphasis added.
Here, the steps are receiving or transmitting data over a network (MPEP 2106.05(d)(II)); storing and retrieving information in memory (MPEP 2106.05(d)(II)) – all of which have been recognized by the courts as well-understood, routine and conventional functions.
The claims are directed to an abstract idea with additional generic computer elements that do not add meaningful limitations to the abstract idea because they require no more than a generic computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry.
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well-understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of memories, processors, sensors, computing devices, networks, storage devices, I/O devices, Bluetooth connection, and operating systems, etc. (See Applicants specification paras pages 9-11, 13, 17-18, 22, 36-37).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
In summary, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because 1) mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”) and 2) well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). The claims do not provide an inventive concept significantly more than the abstract idea. 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: No, the claims do not provide significantly more).
Dependent Claims 2-3, 6-8, 11, 21-22, 26-27, 30, 32, 35, and 37-39 further define the abstract idea that is presented in the independent Claims and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 32 and 38-39 recite additional elements beyond the abstract idea. Claim 32 recites a display of a mobile device. Claim 38 recites an output application displaying selected trigger conditions. The output application is interpreted to be software. Claim 39 recites a display. These additional elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, the Applicant’s specification indicates the use of known computer components. 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. The claims do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-3, 6-8, 11, 20-22, 26-27, 30, 32 and 35-39 are 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-8, 11, 20-22, 26-27, 30, 32 and 35-39 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (US 20160314256) in view of Eidelberg et al. (US 5632276), further in view of Chen et al. (CN 109949930 A).
Regarding Claim 1, Su discloses the following limitations:
A method for determining conditions that trigger a respiratory ailment, the method comprising: (Su discloses systems and methods which identify (determining) environmental triggers because many respiratory disease patients have symptoms that are related to environmental triggers and factors such as air quality and weather (conditions that trigger a respiratory ailment). – paras 2, 46, 54)
collecting data, on activation of a plurality of respiration medicament devices to deliver respiration medicament to patients in a population of patients, (Su discloses an analytics system for monitoring (collecting) accurate, real-time medicament device usage data and rescue event data (data on activation of respiration medicament devices) and a platform for patients (patients in a population of patients) and healthcare providers to report data recorded by the sensors associated with their medicament devices including both rescue medication and controller medication events. – paras 3, 23, 41-47, 112) (The limitation “deliver respiration medicament to patients in a population of patients” is being interpreted as intended use and therefore has little patentable weight. However, Su discloses a medicament device used to deliver medication to the lungs of a user experiencing constricted respiratory airflow. – para 30)
wherein activation is determined by a sensor in each respiration medicament device: (Su discloses that a medicament device sensor (a sensor in each respiration medicament device) detects medication events (activation is determined by a sensor) and reports to the application server. – paras 18, 20, 22)
storing the activation data and patient contextual parameter data related to each activation event for the population of patients; (Su discloses that a database server stores processed rescue and controller medication event data (activation data) as well as non-patient surroundings data (patient contextual parameter data) such as regional data about a number of geographic regions such as public spaces in residential or commercial zones where patients are physically located and may be exposed to pollutants. Further, the regional data being stored includes information about the current weather conditions for the time and place of the rescue event (related to each activation even) such as temperature, humidity, air quality index (patient contextual parameter data). – paras 4-5, 26, 53-54)
pairing the sensor in each respiration medicament device to a client device associated with a patient in the plurality of patients using a passkey, (Su discloses in figure 1A that the sensor 120 is communicably connected to a client device 110 of patient/user 111 (pairing the sensor in each respiration medicament device to a client device associated with a patient). The sensor 120 and client device 110 are paired with each other using a BTLE passkey (using a passkey). – paras 18, 20, 22, 27, 33; FIG. 1A)
wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device; (Su discloses that the sensor automatically synchronizes and communicates information relating to medicament device usage with the client device. – paras 27, 35)
accessing the activation data and the contextual parameter data…for a primary patient in the population of patients over a certain period of time; (Su discloses a dashboard that is accessible (accessing) via a client device. The dashboard allows both patients and providers with the ability to monitor medication rescue events (the activation data). A healthcare provider who has access to individual or multiple patients has the ability to establish notification thresholds, set parameters for the notifications, and receive notifications when patients' event history matches certain conditions (e.g., a rescue event). FIG. 3A shows a display card displaying a patients surroundings data (the contextual parameter data). Further, input data (e.g., rescue event data and surroundings data) may be constant (i.e., not time dependent) or it may vary over time. As such the data itself may be indexed by time, for example separate data points may be available by time of day (including by minute or hour), or over longer periods such as by day, week, month, or season (over a certain period of time). The output may be generated based on individual estimates, aggregate estimates (e.g., an estimate applicable to groups of individuals with common characteristics such as geographical location), or both. – paras 6, 64-65, 73, 76-77; FIG. 3A)
determining the occurrence of rescue events based on the collected activation data; (Su discloses that the application server 130 generally receives an event (determining the occurrence of rescue events) anytime the patient uses their rescue medicament device (based on the collected activation data) 160, because the sensor may detect rescue medication events associated with the patients medicament device. The event data (the collected activation data) may include information that describes the time and date of associated with the event, the status or condition of the medicament device 160 (e.g., battery level), the number of doses of medication remaining (before or after the event), self-test results, and physiological data of a patient being treated with the medicament device 160 as measured by the sensor 120. – paras 65, 68-70; FIG. 1A; FIG. 4)
determining a coefficient for at least one contextual parameter as a triggering event for the primary patient based on the correlation of the contextual parameter and the rescue events; (Su discloses that the submodel training module 505 determines submodel coefficients (determining a coefficient) using training data and submodel functions. One type of submodel function (Equation 4) determines individual medicament device use estimates using surroundings data (contextual parameter) and individual data. For example, for active days on which a patient experiences a medicament device use event (the rescue events), the data management module 136 selects corresponding surroundings data (correlation of the contextual parameter) based on the time and location of the event. Further, Su discloses the submodel function (Equation 4) where Y.sub.ij is whether patient i experiences a medicament device use event (the rescue events) during the j-th (j=1, . . . , n.sub.i) time/day when the patient is active in the program and E.sub.ij is patient i's corresponding exposure during time/day j determined from surroundings data (contextual parameter)…The various β terms are the submodel coefficient vectors (coefficient). – paras 72, 98, 104-106, 112-114; FIG. 1B; FIGs. 5-6)
and providing a comparison of the coefficient for the at least one contextual parameter…and the…coefficients of the triggering event for the population of patients based on the correlation of the contextual parameter and the rescue events for the population of patients; (Su discloses receiving rescue medication event information (the triggering event) for multiple patients (for the population of patients) from the client devices and comparing (providing a comparison) their rescue medication use to other relevant populations – para 46) (While Su discloses comparing one population to other relevant populations, Su does not disclose comparing a primary patient to a distribution of a population of patients.)
determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data optimized by at least one hyperparameter (Su discloses that input data (e.g., surroundings data (contextual parameter)) is also used as training data along with usage event data (the collected activation data), exacerbation data, healthcare utilization data, and other patient data to determine the submodel coefficients, which may be regression coefficients (determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data) and that the sub-models are trained using the training data (optimized by at least one hyperparameter). – paras 72-74, 76-77, 95, 102-121)
wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule; (Su discloses implementing V-fold cross validation (wherein the hyperparameter is tuned) in order to minimizes the chance of over-fitting the model to the data. Su discusses using various model modules 134 and submodel modules 132 in order to predict respiratory disease risk, rescue medication usages, etc. Input data used to determine a particular submodel estimate 590 (based on a training submodule and a population tuning submodule) may include many different types of data received from different data sources. Submodel estimates 590 may be specific to an individual (a training submodule) (individual estimates) or may apply to groups of individuals with common characteristics such as geographical location or respiratory disease risk score (a population tuning submodule) (aggregate estimates). Models may use the individual estimates, aggregate estimates, or both to generate outputs. – abstract; paras 72-74, 76-77, 87, 95-100; FIG. 1B)
and alerting the primary patient if the coefficient is in a high percentile... (Su discloses that, responsive to any analyses performed, the application server 130 prepares and delivers push notifications to send to patients (alerting the primary patient) as well as generating risk notifications which are provided to the user through the client device. Further, a notification can be pushed to the patient based on the outcome of an analysis performed (if the coefficient is in a high percentile) by the server. – paras 20, 48-50, 65-67) (While Su discloses pushing notifications to patients based on the outcome of an analysis performed, Su does not disclose alerting based on the comparison of the coefficient to the distribution of coefficients of the population of patients)
wherein an alert includes providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers. (Su discloses that in response to any analyses performed, the application server 130 prepares and delivers push notifications (from a server and the server distributes the alert) to send to patients 111, authorized healthcare providers 112, and/or other users provided access to the patient's profile. Notifications may additionally comprise a distress or emergency signal (an alert includes providing a distress or an emergency signal to emergency assistance providers) that requests emergency assistance that are distributed to emergency assistance providers 112. – para 48)
Su does not disclose the following limitations met by Eidelberg:
providing a comparison… for the primary patient and distributions of… the population of patients… (Eidelberg teaches the step of determining the presence or severity of the given nervous system dysfunction is achieved by comparing (providing a comparison) the patient's score (the primary patient) with a distribution of patients' scores of a patient population (the distributions of… the population of patients) having the given nervous system dysfunction, a distribution of patients' scores of a patient population having a different nervous system dysfunction and a distribution of patients' scores of a patient population not having the given nervous system dysfunction. – page 8, col 3, lines 32-41; page 8, col 4, lines 16-32; page 10, col 7, lines 33-44; page 11, col 10, lines 1-7; FIG. 4 item 113)
alerting… if the coefficient is in a high percentile of a distribution of coefficients of the population of patients, (Eidelberg teaches that based upon the distributions (the distribution of coefficients of the population of patients), a likelihood ratio of having the given disease will be produced for a physician. – page 8, col 4, lines 16-32)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified comparing populations to other relevant populations as disclosed by Su to incorporate comparing a patients’ scores to a distribution of patients’ scores of a patient population and presenting a likelihood ratio based on the comparison as taught by Eidelberg in order to determine the presence or severity of nervous system dysfunction in a patient. (see Eidelberg page 7, col 2, lines 8-10; page 8, col 3, lines 42-52).
Su and Eidelberg do not teach the following limitations met by Chen:
implementing at least one discretization protocol on the patient contextual parameter, wherein the patient contextual parameter data is grouped into defined ranges; (Chen teaches discretizing environmental data. Ambient temperature and humidity data (the patient contextual parameter) are divided into discrete intervals (implementing at least one discretization protocol). For the ambient temperature, it is divided into 22 intervals according to the GB/T6529 standard, and the temperature range from low to high corresponds to an integer discrete value of 1 to 22 (grouped into defined ranges). For ambient humidity, it is divided into 3 intervals (grouped into defined ranges), the discrete value is 1 when the humidity is lower than 40%, the discrete value is 2 when the humidity is greater than 40% and less than 60%, and the discrete value is 3 when the humidity is greater than 60%. – see translated copy provided; abstract; claim 4; page 5, paras 16-17)
accessing…the contextual parameter data grouped by the at least one discretization protocol (Chen teaches using (accessing) the discrete data (the contextual parameter data grouped by the at least one discretization protocol) to diagnose animal epidemics Bayesian network for training. – see translated copy provided; abstract)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified collecting non-patient surroundings data as disclosed by Su to incorporate discretizing collected data as taught by Chen in order to perform remote, automatic and accurate diagnosis and early warning of an epidemic situation (see Chen abstract).
Regarding Claim 2, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
determining the coefficient partially based on the contextual parameter data and… (Su discloses that the submodel training module 505 determines submodel coefficients (determining the coefficient) using training data and submodel functions such as Equation 4 which utilizes surroundings data (based on the contextual parameter data). – paras 72, 97-98, 104-106, 112-114; FIGs. 5-6)
the occurrence of rescue events based on the collected activation data for at least one secondary patient similar to primary patient of the population of patients for the contextual parameter of the primary patient. (Su discloses that healthcare providers are able to monitor patients (the collected activation data) individually or in aggregate. A healthcare provider who has access to individual or multiple patients (at least one secondary patient) has the ability to establish notification thresholds, set parameters for the notifications, and receive notifications when patients' event history matches certain conditions such as a rescue event (the occurrence of rescue events). The sensors detect the actuation of medicament devices (the occurrence of rescue events) and store the event information and later transmits the event information to the client devices and application server. – paras 65, 68-70; FIG. 1A; FIG. 4)
Regarding Claim 3, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data optimized by the least one hyperparameter; (Su discloses that the sub-model coefficients (the coefficient) may, for example, be regression coefficients (based on regression analysis) and that the sub-models are trained using training data (optimized by the least one hyperparameter). – paras 72-74, 76-77, 95, 102-121)
and performing the regression analysis after a first predetermined period of time for collected activation data and contextual data. (Su discloses that the surroundings data (contextual data) and usage event data (collected activation data) are used as training to determine the model parameters such as regression coefficients (performing the regression analysis). Further, the input data may be constant (i.e., not time dependent) or it may vary over time. For example, one sub-model may estimate immediate medicament device usage probability for an individual while another estimates time-lagged medicament device usage probability for an individual (after a first predetermined period of time). – paras 76-77, 106)
Regarding Claim 6, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
tuning the at least one hyperparameter based on the collected contextual parameter data for the population of patients over a second predetermined period of time. (Su discloses input data that varies over time, such as meteorological data or air quality data (i.e., surroundings data) (the collected contextual parameter data), may be collected at regular intervals (over a second predetermined period of time) and may include a timestamp. The submodels may use timestamps to generate estimates that are time-dependent. Su further discloses implementing V-fold cross validation (tuning the at least one hyperparameter) to minimize the chance of over-fitting the model to the data. – paras 76-77, 87, 130, 136)
Regarding Claim 7, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
wherein the respiratory ailment is asthma. (Su discloses that the respiratory disease may be asthma. – paras 2, 20, 126; Table 3)
Regarding Claim 8, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
wherein the at least one contextual parameter includes one of air pollutant condition or weather condition. (Su discloses that the surroundings data includes regional data. Regional data examples include georeferenced weather data, such as temperature, wind patterns, humidity, the air quality index, and so on (weather condition). Another example is georeferenced pollution data, including particulate counts for various pollutants at an instance of time or measured empirically (air pollutant condition). – para 54)
Regarding Claim 11, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
determining multiple trigger conditions including the determined trigger condition; (Su discloses systems and methods which identify (determining) environmental triggers (multiple trigger conditions) because many respiratory disease patients have symptoms that are related to environmental triggers and factors such as air quality and weather (the determined trigger condition). – paras 2, 46, 54)
assigning a coefficient for each of the multiple trigger conditions; (Su discloses that the submodel functions are based on input data such as historical surroundings data (i.e., environmental triggers) (multiple trigger conditions) and the input data may be in raw form as actual values (a coefficient) or in a relative form as a value (a coefficient) relative to other data. – paras 78)
Eidelberg teaches the following limitations not met by Su:
and comparing the coefficients to a distribution of coefficients for each of the multiple trigger conditions from the population of patients. (Eidelberg teaches that the process of determining a patients marker score is repeated for each marker (for each of the multiple trigger conditions) (see FIG. 3). Then that each score is input for comparison (comparing the coefficients) to a distribution of patients’ scores of a patient population (to a distribution of coefficients) (see FIG. 4). – page 8, col 3, lines 32-41; page 10, col 7, lines 3-27; page 10, col 7, lines 33-44; page 11, col 10, lines 1-7; FIG. 3, items 105, 106; FIG. 4 items 110, 113)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified comparing populations to other relevant populations as disclosed by Su to incorporate comparing a patients’ scores to a distribution of patients’ scores of a patient population for each marker as taught by Eidelberg in order to determine the presence or severity of nervous system dysfunction in a patient. (see Eidelberg page 7, col 2, lines 8-10; page 8, col 3, lines 42-52).
Regarding Claim 20, Su discloses the following limitations:
A method to evaluate a triggering event for a respiratory ailment of a primary patient, the method comprising: (Su discloses systems and methods which identify and analyze (evaluate) environmental triggers because many respiratory disease patients have symptoms that are related to environmental triggers and factors such as air quality and weather (a triggering event for a respiratory ailment). – paras 2, 46, 54)
collecting usage data from activation of a plurality of treatment devices from a population of patients from a communication interface, (Su discloses an analytics system for monitoring (collecting) accurate, real-time medicament device usage data and rescue event data (usage data from activation of treatment devices from a population of patients) and a platform for patients (patients in a population of patients) and healthcare providers to report data recorded by the sensors associated with their medicament devices including both rescue medication and controller medication events. Further, application 115 provides a user interface (herein referred to as a “dashboard”) that is displayed on a screen of the client device 110 (a communication interface) and allows a user to input commands to control the operation of the application 115. – paras 3, 23, 25, 41-47, 62, 112)
wherein activation is determined by a sensor in each treatment device; (Su discloses that a medicament device sensor (a sensor in each respiration medicament device) detects medication events (activation is determined by a sensor) and reports to the application server. – paras 18, 20, 22)
collecting contextual parameter data corresponding to the population of patients from the communication interface; (Su discloses that surroundings data (contextual parameter data) may be received (collecting) for an individual or group of patients (the population of patients). Further, the client devices comprise a screen (the communication interface) and are suitable for user input of data and receipt, display, and interaction with notifications provided by the application server 130. – paras 25, 47, 62, 73)
storing the collected usage data and contextual parameter data in a storage device; (Su discloses that a database server (a storage device) stores processed rescue and controller medication event data (usage data) as well as non-patient surroundings data (contextual parameter data) such as regional data about a number of geographic regions such as public spaces in residential or commercial zones where patients are physically located and may be exposed to pollutants. – paras 4-5, 26, 52-56; FIG 1B, item 140)
determining a coefficient for a triggering event from the contextual parameter and usage data for each patient of the population of patients; (Su discloses that the submodel training module 505 determines submodel coefficients (determining a coefficient) using training data and submodel functions such as Equation 4 which utilizes individual patient (each patient of the population of patients) surroundings data (from the contextual parameter) and medicament device use event data (usage data). – paras 72, 97-98, 104-106, 112-114; FIGs. 5-6)
pairing the sensor in each treatment device to a client device associated with a patient in the population of patients using a passkey, (Su disclose in figure 1A that the sensor 120 is communicably connected to a client device 110 of patient/user 111 (pairing the sensor in each respiration medicament device to a client device associated with a patient). The sensor 120 and client device 110 are paired with each other using a BTLE passkey (using a passkey). – paras 18, 20, 22, 27, 33; FIG. 1A)
wherein the sensor is configured to automatically synchronize and transmit information relating to medicament device usage with the client device; (Su discloses that the sensor automatically synchronizes and communicates information relating to medicament device usage with the client device. – paras 27, 35)
determining a coefficient for the triggering event for the primary patient based on data relating to the contextual data…and usage data for the primary patient; (Su discloses that the submodel training module 505 determines submodel coefficients (determining a coefficient) using training data and submodel functions such as Equation 4. Equation 4 utilizes individual patient (the primary patient) surroundings data (the contextual parameter) and medicament device use event data (usage data). – paras 72, 97-98, 104-106, 112-114; FIGs. 5-6)
determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data optimized by at least one hyperparameter (Su discloses that input data (e.g., surroundings data (contextual parameter)) is also used as training data along with usage event data (the collected activation data), exacerbation data, healthcare utilization data, and other patient data to determine the submodel coefficients, which may be regression coefficients (determining the coefficient based on regression analysis of the contextual parameter in relation to the collected activation data) and that the sub-models are trained using the training data (optimized by at least one hyperparameter). – paras 72-74, 76-77, 95, 102-121)
wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule; (Su discloses implementing V-fold cross validation (wherein the hyperparameter is tuned) in order to minimizes the chance of over-fitting the model to the data. Su discusses using various model modules 134 and submodel modules 132 in order to predict respiratory disease risk, rescue medication usages, etc. Input data used to determine a particular submodel estimate 590 (based on a training submodule and a population tuning submodule) may include many different types of data received from different data sources. Submodel estimates 590 may be specific to an individual (a training submodule) (individual estimates) or may apply to groups of individuals with common characteristics such as geographical location or respiratory disease risk score (a population tuning submodule) (aggregate estimates). Models may use the individual estimates, aggregate estimates, or both to generate outputs. – abstract; paras 72-74, 76-77, 87, 95-100; FIG. 1B)
providing a comparison of the coefficient… (Su discloses receiving rescue medication event information for multiple patients (the population of patients) from the client devices and comparing (providing a comparison) their rescue medication use to other relevant populations – para 46) (While Su discloses comparing one population to other relevant populations, Su does not disclose comparing the primary patient coefficient to a distribution of coefficients of the population of patients)
and alerting the primary patient if the coefficient is in a high percentile… (Su discloses that, responsive to any analyses performed, the application server 130 prepares and delivers push notifications to send to patients (alerting the primary patient) as well as generating risk notifications which are provided to the user through the client device. Further, a notification can be pushed to the patient based on the outcome of an analysis performed (if the coefficient is in a high percentile) by the server. – paras 20, 48-50, 65-67) (While Su discloses pushing notifications to patients based on the outcome of an analysis performed, Su does not disclose alerting based on the comparison of the coefficient to the distribution of coefficients of the population of patients)
wherein an alert includes providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers. (Su discloses that in response to any analyses performed, the application server 130 prepares and delivers push notifications (from a server and the server distributes the alert) to send to patients 111, authorized healthcare providers 112, and/or other users provided access to the patient's profile. Notifications may additionally comprise a distress or emergency signal (an alert includes providing a distress or an emergency signal to emergency assistance providers) that requests emergency assistance that are distributed to emergency assistance providers 112. – para 48)
Su does not disclose the following limitations met by Eidelberg:
providing a comparison of the coefficient of the primary patient in relation to a distribution of the coefficients for the population of patients. (Eidelberg teaches determining the presence or severity of the given nervous system dysfunction is achieved by comparing (providing a comparison) the patient's marker score (the coefficient of the primary patient) with a distribution of patients' marker scores of a patient population (a distribution of the coefficients for the population of patients) having the given nervous system dysfunction, a distribution of patients' scores of a patient population having a different nervous system dysfunction and a distribution of patients' scores of a patient population not having the given nervous system dysfunction. – page 8, col 3, lines 32-41; page 8, col 4, lines 16-32; page 10, col 7, lines 33-44; page 11, col 10, lines 1-7; FIG. 4 item 113)
alerting… if the coefficient is in a high percentile of a distribution of coefficients of the population of patients, (Eidelberg teaches that based upon the distributions (the distribution of coefficients of the population of patients), a likelihood ratio of having the given disease will be produced for a physician (alerting). – page 8, col 4, lines 16-32)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified comparing populations to other relevant populations as disclosed by Su to incorporate comparing a patients score to a distribution of patients’ scores of a patient population and presenting a likelihood ratio based on the distributions as taught by Eidelberg in order to determine the presence or severity of nervous system dysfunction in a patient. (see Eidelberg page 7, col 2, lines 8-10; page 8, col 3, lines 42-52).
Su and Eidelberg do not teach the following limitations met by Chen:
implementing at least one discretization protocol on the patient contextual parameter, wherein the patient contextual parameter data is grouped into defined ranges; (Chen teaches discretizing environmental data. Ambient temperature and humidity data (the patient contextual parameter) are divided into discrete intervals (implementing at least one discretization protocol). For the ambient temperature, it is divided into 22 intervals according to the GB/T6529 standard, and the temperature range from low to high corresponds to an integer discrete value of 1 to 22 (grouped into defined ranges). For ambient humidity, it is divided into 3 intervals (grouped into defined ranges), the discrete value is 1 when the humidity is lower than 40%, the discrete value is 2 when the humidity is greater than 40% and less than 60%, and the discrete value is 3 when the humidity is greater than 60%. – see translated copy provided; abstract; claim 4; page 5, paras 16-17)
accessing…the contextual parameter data grouped by the at least one discretization protocol (Chen teaches using (accessing) the discrete data (the contextual parameter data grouped by the at least one discretization protocol) to diagnose animal epidemics Bayesian network for training. – see translated copy provided; abstract)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified collecting non-patient surroundings data as disclosed by Su to incorporate discretizing collected data as taught by Chen in order to perform remote, automatic and accurate diagnosis and early warning of an epidemic situation (see Chen abstract).
Regarding Claim 21, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
further comprising collecting activation data for at least one secondary patient similar to primary patient of the population of patients for the contextual parameter of the primary patient, (Su discloses an analytics system for monitoring (collecting) accurate, real-time medicament device usage data and rescue event data and a platform for patients and healthcare providers to report data recorded by the sensors associated with their medicament devices including both rescue medication and controller medication events. The medication event information (activation data) is sent to the application server 130 for…aggregate analyses of event data across multiple patients (data for at least one secondary patient). – paras 3, 23, 25, 35, 41-47, 62, 112)
wherein the coefficient is determined partially based on the contextual parameter data and the occurrence of rescue events based on the at least one secondary patient. (Su discloses that the submodel training module 505 determines submodel coefficients (the coefficient) using training data and submodel functions such as Equation 4 which utilizes surroundings data (based on the contextual parameter data) and medicament device use event data (occurrence of rescue events). The analysis on input data may be done for an individual or an aggregate analysis of event data across multiple patients (the at least one secondary patient) may be done. – paras 35, 65, 68-70, 72, 97-98, 104-106, 112-114; FIGs. 1A, 4-6)
Regarding Claim 22, this claim recites substantially similar limitations to those recited in claims 3 and 6 above; thus, the same rejection applies. Further, Su, Eidelberg and Chen disclose the following limitations:
wherein the coefficients of the population of patients are selected based on regression analysis performed after the first predetermined period of time for each patient in the population of patients, (For example, a notification can be pushed to the patient based on the outcome of an analysis performed by server 130 (based on regression analysis performed). The dashboard 300 will process the notification and determine (selected) which card/s to use to present the information (the coefficients) in the notification. FIG. 3A shows that the air quality is “bad” for patient “Bobby” based on the outcome of the analysis performed. – paras 66-67)
and wherein at least one hyperparameter is tuned based on the collected contextual parameter data for the population of patients over a second predetermined period of time. (Su discloses input data that varies over time, such as meteorological data or air quality data (i.e., surroundings data) (the collected contextual parameter data), may be collected at regular intervals (over a second predetermined period of time) and may include a timestamp. The submodels may use timestamps to generate estimates that are time-dependent. Su further discloses implementing V-fold cross validation (at least one hyperparameter is tuned) to minimize the chance of over-fitting the model to the data. – paras 76-77, 87, 130, 136)
Regarding Claim 26, this claim recites substantially similar limitations to those recited in claim 7 above; thus, the same rejection applies.
Regarding Claim 27, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
wherein the at least one contextual parameter includes one of air pollutant condition or weather condition, (Su discloses that the surroundings data includes regional data. Regional data examples include georeferenced weather data (weather condition) and georeferenced pollution data (air pollutant condition). – paras 49, 54)
wherein the pollutant condition is one of air quality index, ozone molecules (O3), nitrogen dioxide molecules (NO2), sulfur dioxide molecules (SO2), particulate matter, 2.5 micrometers or less (PM2.5), particulate matter, 10 micrometers or less (PM10), (Su discloses that regional data examples include the air quality index. Further Table 4 shows a list of example input data types for air pollution such as AQI (air quality index), ozone molecules (O3), nitrogen dioxide molecules (NO2), sulfur dioxide molecules (SO2), particulate matter, 2.5 micrometers or less (PM2.5), particulate matter, and 10 micrometers or less (PM10). – paras 49, 54, 81-82, 99-100; TABLE 4)
and wherein the weather condition is one of temperature, humidity, wind speed, wind direction, station pressure, and visibility. (Su discloses that the georeferenced weather data includes, for example, temperature, wind patterns (wind speed, wind direction), humidity, the air quality index, and so on.– paras 54)
Regarding Claim 30, this claim recites substantially similar limitations to those recited in claim 11 above; thus, the same rejection applies. Further, Su, Eidelberg and Chen disclose the following limitations:
and displaying selected trigger conditions from the multiple trigger conditions based on whether the coefficient for the primary patient is in a certain percentile… (Su discloses that, responsive to any analyses performed, the application server 130 prepares and delivers push notifications to send to patients as well as generating risk notifications which are provided to the user through the client device (displaying). Further, a notification can be pushed to the patient based on the outcome of an analysis performed (based on whether the coefficient for the primary patient is in a certain percentile) by the server. – paras 20, 48-50, 65-67) (While Su discloses pushing notifications to patients based on the outcome of an analysis performed, Su does not disclose displaying based on the comparison of the coefficient to the distribution of coefficients of the population of patients)
Eidelberg teaches the following limitations not met by Su:
displaying… based on whether the coefficient for the primary patient is in a certain percentile of the distribution of the coefficients for the population of patients. (Eidelberg teaches that based upon the distributions (the distribution of the coefficients for the population of patients), a likelihood ratio of having the given disease will be produced for a physician (the coefficient for the primary patient is in a certain percentile). – page 8, col 4, lines 16-32)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified pushing notifications to patients based on the outcome of an analysis performed as disclosed by Su to incorporate presenting a likelihood ratio based on the comparison of a patients’ scores to a distribution of patients’ scores of a patient population as taught by Eidelberg in order to determine the presence or severity of nervous system dysfunction in a patient. (see Eidelberg page 7, col 2, lines 8-10; page 8, col 3, lines 42-52).
Regarding Claim 32, Su, Eidelberg and Chen disclose all the limitations above and further disclose the following limitations:
assigning a descriptive label correlated with an overall risk factor for the primary patient; (Su discloses generating disease risk scores for individuals (an overall risk factor for the primary patient). The respiratory disease risk scores are assigned to groups (assigning a descriptive label) for different ranges (e.g., high, medium, low), and may have color coding. – paras 20, 24, 47, 73, 140-144; FIG. 13)
and displaying the descriptive label and trigger conditions to the primary patient on a display of a mobile device. (Su discloses generating risk notifications (the descriptive label and trigger conditions) which are provided to the user through the client device along with predicted weather and air pollution levels in the following 1-7 days (trigger conditions). – paras 20, 24, 47, 65-67, 73, 140-144; FIG. 3; FIG. 13, item 1335)
Regarding Claim 35, this claim depends on claim 20, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 15 above; thus, the same rejection applies.
Regarding Claim 36, this claim recites substantially similar limitations to those recited in claims 1 and 20 above; thus, the same rejection applies. Further, Su, Eidelberg and Chen disclose the following limitations:
a storage device to store the activation data and patient contextual parameter data related to each activation event for the population of patients; (Su discloses that a database server (a storage device) stores processed rescue and controller medication event data (activation data) as well as non-patient surroundings data (contextual parameter data) such as regional data about a number of geographic regions such as public spaces in residential or commercial zones where patients are physically located and may be exposed to pollutants. – paras 4-5, 26, 52-56; FIG 1B, item 140)
and one or more processors configured to execute instructions to cause the system to: (Su discloses the use of at least one processor (one or more processors) for executing computer program instructions (configured to execute instructions). – paras 40, 59-60, 63; claim 9)
Regarding Claim 37, this claim recites substantially similar limitations to those recited in claim 11 above; thus, the same rejection applies.
Regarding Claim 38, this claim recites substantially similar limitations to those recited in claim 30 above; thus, the same rejection applies. Su, Eidelberg and Chen disclose the following limitations:
further comprising an output application, the output application displaying selected trigger conditions (Su discloses that the application server identifies triggers (spatial, temporal, environmental) and has the ability to generate estimates and send the estimates to a client device for display to a user. FIG. 3 shows a dashboard displaying triggers such as air quality, humidity, and temperature (an output application displaying selected trigger conditions). – abstract; paras 20, 46)
Regarding Claim 39, this claim recites substantially similar limitations to those recited in claim 32 above; thus, the same rejection applies.
Response to Arguments
Regarding the claim objection to claim 1, Applicant’s amendments have rendered the previous claim objection moot. Thus, it has been withdrawn.
Regarding rejections under 35 USC § 101 to Claims 1-3, 6-8, 11, 15, 20-22, 26-27, 30, 32 and 35-39, Applicant’s arguments have been fully considered, and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) The implementation of the notification is in direct response to an analysis, wherein the system exhibits a practical application by incorporating limitations imposed by the communication to auxiliary emergency systems via the notification. (p. 12).
Regarding (a), Examiner respectfully disagrees. MPEP 2106.04(d)(I) indicates that insignificant extra-solution activity cannot integrate a judicial exception into a practical application. Amended claim 1 recites the following limitations: “wherein an alert includes providing a distress or an emergency signal from a server and the server distributes the alert to emergency assistance providers.” Examiner interprets these limitations to be additional elements, however, it is recited at a high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, which is a form of extra-solution activity. Thus, a practical application is not present.
(b) In a further aspect, the claims have been amended to disclose a functional operation of the system to improve the operation of the system. "The population tuning submodule 664 may be run at a cadence significantly less than the training submodule 662 to conserve computational resources. For example, the training submodule 662 may be run every 30 days for the data collected from each primary patient, but the population tuning submodule 664 may be run over the data from the entire population of patients once every 90 days to optimize the hyperparameters incorporating the new data added." Here, the manner in which the parameters are optimized in the system that causes the system to operate more efficiently. This increase in computational efficiency improves the operation of the overall system. (p. 12).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer or where the claimed invention provides a technical solution to a technical problem. See also MPEP 2106.05(a). Here, while Applicant's argued problem is a technical problem, there is no nexus between the argued problem and the argued solution because there is no indication that the claimed invention actually solves this problem. The Applicant has identified problems in computational efficiency; however, there is no indication that the claim actually solves this problem. The Applicant further points to their specification to show that, to paraphrase, a batch processing method is used to incorporate data over a 90 data period and update the hyperparameters. First, the Examiner notes that this feature is not recited in the claim, and second the Examiner notes that batch processing of data does not provide an improvement to the computers efficiency because the computer is not processing data more efficiently; rather the computer is being used every 90 days instead of, for example, once a week. There is no indication that the computer is made to improve computational efficiency. Because there is no improvement to the functioning of the computer, a practical application is not present.
Regarding rejections under 35 USC § 103 to Claims 1-3, 6-8, 11, 15, 20-22, 26-27, 30, 32, and 35-39, Applicant’s arguments have been fully considered and are not persuasive. Applicant argues:
(c) Per the Office Action, Reference Chen appears to teach or suggest tuning a parameter based on a V-fold cross validation; however Chen does not appear to teach or suggest a tuning the hyperparameter using a training submodule and population tuning submodule. The cited portions and proposed combination of the applied references are not understood to disclose or teach each and every feature of independent claim 1, wherein claims 1, 20, and 36 recite similar recitations. As for the other applied references, the Office Action does not identify aspects of the other references that remedy the above-noted deficiencies of the cited portions of independent claims 1, 20, and 36. (p. 13).
Regarding (c), Examiner respectfully disagrees. Examiner notes that Su was previously and currently is being relied upon to teach limitations regarding parameter tuning. The Chen refence was not relied upon for these limitations. Therefore, Applicant’s argument is moot. However, in the interest of compact prosecution, Examiner will respond to Applicant’s argument in regards to the Su reference.
Regarding the submodules, amended claim 1 recites “wherein the hyperparameter is tuned based on a training submodule and a population tuning submodule;”. Su discloses implementing V-fold cross validation (wherein the hyperparameter is tuned) in order to minimize the chance of over-fitting the model to the data. Various model modules and submodel modules are used in the process. Input data (e.g., surroundings data and usage event data) are used to determine a particular submodel estimate (based on a training submodule and a population tuning submodule) where each submodel estimate may be specific to an individual (a training submodule) or may apply to groups of individuals with common characteristics (a population tuning submodule). Models may use the individual estimates, aggregate estimates, or both to generate outputs. See also updated rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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