DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/26/2025 has been entered.
Claims 1-5, 8-9 and 11 remain pending in this application.
The 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejections have been withdrawn in light of the amendments made to the claims and the specification.
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 1-5, 8-9 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5, 8-9 and 11 are drawn to a system which is within the four statutory categories (i.e. machine).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“An exacerbation risk prediction system that predicts an exacerbation risk of a patient suffering from a respiratory disease, the exacerbation risk prediction system comprising:
an oxygen concentrator that supplies oxygen to the patient through a cannula;
an acquisition unit that acquires first information including biological information of the patient wearing the cannula on exertion and at rest and patient information that is information of the patient regarding a disease state;
a storage that stores the first information;
a prediction unit that predicts the exacerbation risk based on the first information; and
a learning unit that learns the first information and an evaluation regarding exacerbation in association with each other;
a presentation unit that presents a guide regarding the exacerbation risk based on the exacerbation risk predicted by the prediction unit being a first predetermined threshold or larger, a first guide being presented when the predicted exacerbation risk is a first predetermined threshold and a second guide being presented when the predicted exacerbation risk is a second predetermined threshold, the second predetermined threshold and the second guide being different from the first predetermined threshold and the first guide, respectively; and
an operation unit that transmits control information to an air conditioner to operate the air conditioner installed in a building in which the patient is present so as to improve the exacerbation risk based on the exacerbation risk predicted by the prediction unit being the predetermined threshold or larger, the air conditioner being configured to control at least one of a temperature and a humidity of a target space in which the patient is present to reduce the predicted exacerbation risk so that it becomes smaller than the predetermined threshold,
the biological information being at least one of a respiratory rate, a respiratory wave form, and an exhaled gas component amount,
the acquisition unit acquiring the at least one of the respiratory rate, the respiratory waveform, and the exhaled gas component amount from the oxygen concentrator, and
the prediction unit predicting the exacerbation risk by inputting the first information to a learning model created by the learning unit.”
The limitations of “…acquire first information including biological information of the patient wearing the cannula on exertion and at rest and patient information that is information of the patient regarding a disease state;…predict the exacerbation risk based on the first information;…acquiring the at least one of the respiratory rate, a respiratory waveform and the exhaled gas component amount from the oxygen concentrator; and …predict the exacerbation risk…”, which correspond to “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic storage and generic processor devices (the processor device comprises prediction/learning/acquisition units) does not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
Claim 1 also recites “a prediction unit that predicts the exacerbation risk based on the first information”, “a learning unit that learns the first information and an evaluation regarding exacerbation in association with each other”, ”the prediction unit predicting the exacerbation risk by inputting the first information to a learning model created by the learning unit”, and the learning model is described in the current specification as a fully connected neural network, or other learning models, such as SVM, random forest and XGBoost may be used. Therefore, these limitations correspond to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Claims 2-5, 8-9 and 11 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2-5, 8-9 and 11 recite the same abstract idea. Claims 2-5, 8-9 and 11 describe a further limitation regarding the basis for predicting an exacerbation risk for the patient. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “an oxygen concentrator”, “an acquisition unit that acquires”, “a storage that stores the information”, “a prediction unit that predicts”, “a learning unit that learns”, “an operation unit that transmits control information to an air conditioner to operate the air conditioner”, which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims also recite other additional limitations beyond abstract idea, including functions such as “presenting a guide (data)”, “the air conditioner installed in a building in which the patient is present so as to improve the exacerbation risk based on the exacerbation risk predicted by the prediction unit” are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
Accordingly, these additional elements 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 are directed to an abstract idea.
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 element of using a processor (prediction unit) to perform the predicting steps and the operational unit that transmits control information to the air conditioner 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.
The claims are not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-5, 8-9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Navarro et al. (hereinafter Navarro) (WO 2021/220249 A1) in view of Naumov et al. (hereinafter Naumov) (WO 2022084408 A1).
Claim 1 has been amended to recite an exacerbation risk prediction system that predicts an exacerbation risk of a patient suffering from a respiratory disease, the exacerbation risk prediction system comprising:
an oxygen concentrator that supplies oxygen to the patient through a cannula (Navarro discloses “oxygen concentrator” in abstract, “The system has an oxygen concentrator configured to generate and deliver oxygen enriched air to the patient according to a selected dosage.” in [0014], and “Examples of airway delivery devices include, but are not limited to: nasal masks, nasal pillows, nasal prongs, nasal cannulas, and mouthpieces.” in [0104]);
an acquisition unit that acquires first information including biological information of the patient wearing the cannula on exertion and at rest and patient information that is information of the patient regarding a disease state (Navarro discloses “The health monitoring device includes one or more sensors configured to collect the physiological data. Another implementation is where the one or more sensors of the health monitoring device are selected from one of the group of: an audio sensor, a heart rate sensor, a respiratory sensor,” in [0015]);
a storage that stores the first information (Navarro discloses “The data analysis engine 472 then analyzes the collected data to determine a health condition (722). The health data analysis may include determining the severity of the triggering event. The collected analysis is then stored (724) for further analysis and other purposes such as building on a training set for the machine-learning engine 480.” in [0191]);
a prediction unit that predicts the exacerbation risk based on the first information (Navarro discloses “the health data analysis engine is operable to predict respiratory exacerbations based on the operational data and the physiological data.” in [0015]; and
a learning unit that learns the first information and an evaluation regarding exacerbation in association with each other (Navarro discloses “Data from the databases 484, health conditions from the health data analysis engine 472 and data from individual POC user systems such as the user system 490 may be further correlated by the machine-learning engine 480. The machine-learning engine 480 may implement machine-learning structures such as a neural network, decision tree ensemble, support vector machine, Bayesian network, or gradient boosting machine.” in [0184]);
a presentation unit that presents a guide regarding the exacerbation risk to the patient based on the exacerbation risk predicted by the prediction unit being a predetermined threshold or larger (Navarro discloses “…The data analysis engine 472 then analyzes the collected data to determine a health condition (722). The health data analysis may include determining the severity of the triggering event. The collected analysis is then stored (724) for further analysis and other purposes such as building on a training set for the machine-learning engine 480. The health data analysis engine 472 then determines and implements a response to the detected triggering event (726), possibly based on the determined severity. For example, this may involve notifications to the patient in less severe triggering events or notifications to a health care professional in more severe triggering events….” in [0191]); and
the biological information being at least one of a respiratory rate, a respiratory waveform, and an exhaled gas component amount (Navarro discloses “…The health monitoring device includes one or more sensors configured to collect the physiological data. Another implementation is where the one or more sensors of the health monitoring device are selected from one of the group of: an audio sensor, a heart rate sensor, a respiratory sensor, a ECG sensor,…” in [0015]),
the acquisition unit acquiring the at least one of the respiratory rate, the respiratory waveform, and the exhaled gas component amount from the oxygen concentrator (Navarro discloses “…The health monitoring device includes one or more sensors configured to collect the physiological data. Another implementation is where the one or more sensors of the health monitoring device are selected from one of the group of: an audio sensor, a heart rate sensor, a respiratory sensor, a ECG sensor,…” in [0015]), and
the prediction unit predicting the exacerbation risk by inputting the first information to a learning model created by the learning unit (Navarro discloses “Data from the databases 484, health conditions from the health data analysis engine 472 and data from individual POC user systems such as the user system 490 may be further correlated by the machine-learning engine 480. The machine-learning engine 480 may implement machine-learning structures such as a neural network, decision tree ensemble, support vector machine, Bayesian network, or gradient boosting machine.” in [0184]).
Navarro teaches “notifications to the patient in less severe triggering events or notifications to a health care professional in more severe triggering events….” in [0191], but fails to expressly teach “a presentation unit that presents a guide…a first guide being presented when the predicted exacerbation risk is a first predetermined threshold and a second guide being presented when the predicted exacerbation risk is a second predetermined threshold, the second predetermined threshold and the second guide being different from the first predetermined threshold and the first guide, respectively”. However, this feature is well known in the art, as evidenced by Naumov.
In particular, Naumov discloses “…The DHP 406 may also aggregate and analyze the data to determine one or more metrics associated with the End-User or Patient, such as an individualized compliance score, an individualized future compliance score, and/or an individualized risk score. For example, the DHP 406 may send an alert to and/or send a notification for display on the user device associated with a user when their compliance score is below a threshold, future compliance score is below a threshold, and/or risk score exceeds a threshold. The threshold value for the compliance score, future compliance score, and/or risk score may be a predefined numerical value or percentage that is indicative of an increased risk of an exacerbation event (e.g., an asthma, COPD, or other respiratory exacerbation event). The alert may indicate that the user should maintain their rescue inhaler nearby. Additionally or alternatively, the alert/notification may indicate how the user can improve compliance with the prescribed treatment.” in [0189], and “The DHP 406 may send an alert to a user device associated with the user upon determining that the user is likely to experience an exacerbation, such as when the user’s individualized risk score is above a threshold (e.g., individualized risk scores greater than 7) that indicates that the user is at a high likelihood of a respiratory exacerbation. The user device may generate the alert and/or send a message to one or more of the user’s inhalers that cause the inhalers to generate the alert for the user. The alert could be any one or more of an audible noise generated by a speaker, an illuminate light generated by a light source, a GUI presented on the user device, etc. The alert may indicate that the user should keep their rescue inhaler nearby. Further, the threshold may, for example, be a predetermined value that is compared to an output by the machine learning algorithm, and that signifies a high probability that the user will have difficulty breathing and/or feel the need to use their respiratory rescue medication in the immediate future (e.g., within the next X hours, such as 12 hours, or days, such as 1 day).” in [0216]. Therefore, the first threshold (risk score being greater than 7), the guide is notifying the user to keep their rescue inhaler nearby, and the second threshold (a predetermined value that is compared to an output by the machine learning algorithm, and that signifies a high probability that the user will have difficulty breathing), then the guide (notification) would be notifying the user to use their respiratory rescue medication in the immediate future.
It would have been obvious to one of ordinary skill in the art to include in the managing respiratory condition system of Navarro the ability to provide notifications based on risk score thresholds as taught by Naumov since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Navarro teaches “home smart management system” in [0164], but fails to expressly teach “an operation unit that transmits control information to an air conditioner to operate the air conditioner installed in a building in which the patient is present so as to improve the exacerbation risk based on the exacerbation risk predicted by the prediction unit being the predetermined threshold or larger, the air conditioner being configured to control at least one of a temperature and a humidity of a target space in which the patient is present to reduce the predicted exacerbation risk so that it becomes smaller than the predetermined threshold”. However, this feature is well known in the art, as evidenced by Naumov.
In particular, Naumov discloses “The DHP 406 is also configured to provide the analyzed and manipulated data (e.g., the weather enrichment, a compliance score, a future compliance score, a risk score, c/c.) back to the user devices 402a, 402b, or to the computer 408 that includes the HCP facing processing module associated with a health care provider. Also, or alternatively, the DHP 406 and/or the user devices 402a, 402b may perform control of one or more smart devices installer at the End -User or Patient’s residence. For example, the DHP 406 and/or the user devices 402a, 402b may perform control of a smart thermostat or HVAC system installed at the End-User or Patient’s residence, for example, as described in more detail herein.” in [0184] and “…control and/or adjustment of smart devices or appliances that contribute to a patient’s likelihood of experiencing an exacerbation may also consider the location of the user and/or past, current, or future weather conditions at that location.” in [0221] and “…based on the individualized score for the user, the user’s location, and/or past, current, or future weather conditions at that location, the DHP 460 and/or the user device 402a, 402b may control or adjust to set humidity and/or temperature of the HVAC system to decrease the likelihood that the patient experiences an exacerbation event…” in [0222].
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Naumov with the motivation of to decrease the likelihood that the patient experiences an exacerbation event (Naumov; [0222].
Claim 2 recites the exacerbation risk prediction system according to claim 1, wherein the first information further includes environment information of at least one of an indoor and an outdoor of the building in which the patient is present (Navarro discloses “The database may also store relevant external data from other sources such as environmental data, scientific data, and demographic data.” in [0121]).
Claim 3 recites the exacerbation risk prediction system according to claim 2, wherein the biological information is at least one of a blood oxygen concentration, a heart rate, a height, and a weight (Navarro; [0015]).
Claim 4 recites the exacerbation risk prediction system according to claim 2, wherein the patient information is at least one of a degree of progress of the disease state, a forced expiratory volume versus standard, a prescribed flow rate, a medical history, a type of medicine being taken, a degree of cough, a degree of sputum, a color of sputum, a degree of shortness of breath, a degree of sleep, and a physical condition (Navarro discloses “oxygen sensor senses and collects physiological data of the patient” in abstract]).
Claim 5 recites the exacerbation risk prediction system according to claim 2, wherein the environment information of the indoor is at least one of temperature, humidity, carbon dioxide concentration, carbon monoxide concentration, ozone concentration, SO3 concentration, temperature difference from the outdoor, dust amount, PM2.5 amount, yellow sand amount, mold amount, virus amount, VOC amount, pollen amount, allergic substance amount, bacteria amount, oxygen concentration, airflow, and atmospheric pressure, and the environment information of the outdoor is at least one of temperature, humidity, weather, atmospheric pressure, dust amount, PM2.5 amount, and yellow sand amount (Navarro; [0124]).
Claim 8 recites the exacerbation risk prediction system according to claim 1, wherein the biological information is at least one of a blood oxygen concentration, a heart rate, a height, and a weight (Navarro; [0208]).
Claim 9 recites the exacerbation risk prediction system according to claim 8, wherein the patient information is at least one of a degree of progress of the disease state, a forced expiratory volume versus standard, a prescribed flow rate, a medical history, a type of medicine being taken, a degree of cough, a degree of sputum, a color of sputum, a degree of shortness of breath, a degree of sleep, and a physical condition (Navarro; [0173]).
Claim 11 recites the exacerbation risk prediction system according to claim 1, wherein the patient information is at least one of a degree of progress of the disease state, a forced expiratory volume versus standard, a prescribed flow rate, a medical history, a type of medicine being taken, a degree of cough, a degree of sputum, a color of sputum, a degree of shortness of breath, a degree of sleep, and a physical condition (Navarro; [0173]).
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in order in which they appear.
Arguments about 35 USC 101 rejection:
Argument 1: Applicant argues that presenting different guides at different predetermined thresholds integrates the alleged judicial exception into a practical application.
In response, Examiner submits that providing a guide to the user based on the predetermined thresholds, such as providing a guide to “take rest” when the exacerbation risk is at a predetermined threshold of 5 and providing a guide to “consult a medical institution” correspond to insignificant extra-solution activities, which do not provide a practical application for the abstract idea. This feature amounts to data outputting, and also is not significant, since it does not impose meaningful limits on the claim such that it nominally or tangentially related to the invention (presenting data) (see MPEP2106.05 (g)).
Argument 2: Applicant argues that claim 1 recites “an operation unit that transmits control information to an air conditioner to operate the air conditioner installed in a building in which the patient is present so as to improve the exacerbation risk based on the exacerbation risk predicted by the prediction unit being a second predetermined threshold or larger, the air conditioner being configured to control at least one of a temperature and a humidity of a target space in which the patient is present to reduce the predicted exacerbation risk to a third predetermined threshold or lower” and the specific control step of the exacerbation risk prediction system integrate the judicial exception into a practical application, that is similar to claim 2 of Example 45 in the Guidance.
In response, Examiner submits that claim 2 of Example 45 provides a technological improvement to the technological problems associated with under cure and overcure, which would otherwise negatively affect the cured polyurethane’s strength and wear performance.
The current claims recite a feature of controlling and/or adjusting an air conditioner for exacerbation purposes, which is well understood, routine and conventional activity as evidenced by the applied art Naumov. In particular, Naumov discloses “The DHP 406 is also configured to provide the analyzed and manipulated data (e.g., the weather enrichment, a compliance score, a future compliance score, a risk score, c/c.) back to the user devices 402a, 402b, or to the computer 408 that includes the HCP facing processing module associated with a health care provider. Also, or alternatively, the DHP 406 and/or the user devices 402a, 402b may perform control of one or more smart devices installer at the End -User or Patient’s residence. For example, the DHP 406 and/or the user devices 402a, 402b may perform control of a smart thermostat or HVAC system installed at the End-User or Patient’s residence, for example, as described in more detail herein.” in [0184] and “…control and/or adjustment of smart devices or appliances that contribute to a patient’s likelihood of experiencing an exacerbation may also consider the location of the user and/or past, current, or future weather conditions at that location.” in [0221].
Therefore, the limitation of “an operation unit that transmits control information to an air conditioner to operate the air conditioner installed in a building in which the patient is present so as to improve the exacerbation risk based on the exacerbation risk predicted by the prediction unit being a second predetermined threshold or larger, the air conditioner being configured to control at least one of a temperature and a humidity of a target space in which the patient is present to reduce the predicted exacerbation risk to a third predetermined threshold or lower” corresponds to mere instruction to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Argument 3: Applicant argues that claim 1 is similar to claim 2 of Example 43 of the PTO 101 Guidelines and administers a treatment to patient by controlling operation of an air conditioner.
In response, Examiner submits that claim 1 recites “the air conditioner being configured to control at least one of a temperature and humidity of a target space…” and this feature is directed to providing a suitable temperature and humidity (environment) to the user, which may mostly similar to “administering a suitable medication to a patient”, which is not directed to a “particular treatment” (see MPEP 2106.04(d)(2)).
Additionally, Examiner submits that claim 2 of Example 43 is directed to patient being administered a treatment that is particular to the identified phenotype, where the limitation is not directed to a well-understood, routine and conventional activity. The current claim 1 recites a feature of controlling and/or adjusting an air conditioner for exacerbation purposes, which is well understood, routine and conventional activity as evidenced by the applied art Naumov (Naumov discloses “The DHP 406 is also configured to provide the analyzed and manipulated data (e.g., the weather enrichment, a compliance score, a future compliance score, a risk score, c/c.) back to the user devices 402a, 402b, or to the computer 408 that includes the HCP facing processing module associated with a health care provider. Also, or alternatively, the DHP 406 and/or the user devices 402a, 402b may perform control of one or more smart devices installer at the End -User or Patient’s residence. For example, the DHP 406 and/or the user devices 402a, 402b may perform control of a smart thermostat or HVAC system installed at the End-User or Patient’s residence, for example, as described in more detail herein.” in [0184] and “…control and/or adjustment of smart devices or appliances that contribute to a patient’s likelihood of experiencing an exacerbation may also consider the location of the user and/or past, current, or future weather conditions at that location.” in [0221].).
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 103 rejection:
Applicant argues that Navarro does not teach presenting a first guide for a first predetermined threshold, and presenting a second guide for a second predetermined threshold in which the first guide and the first predetermined threshold are different from the second guide and the second predetermined threshold, respectively.
In response, Examiner submits that Naumov discloses “…The DHP 406 may also aggregate and analyze the data to determine one or more metrics associated with the End-User or Patient, such as an individualized compliance score, an individualized future compliance score, and/or an individualized risk score. For example, the DHP 406 may send an alert to and/or send a notification for display on the user device associated with a user when their compliance score is below a threshold, future compliance score is below a threshold, and/or risk score exceeds a threshold. The threshold value for the compliance score, future compliance score, and/or risk score may be a predefined numerical value or percentage that is indicative of an increased risk of an exacerbation event (e.g., an asthma, COPD, or other respiratory exacerbation event). The alert may indicate that the user should maintain their rescue inhaler nearby. Additionally or alternatively, the alert/notification may indicate how the user can improve compliance with the prescribed treatment.” in [0189], and “The DHP 406 may send an alert to a user device associated with the user upon determining that the user is likely to experience an exacerbation, such as when the user’s individualized risk score is above a threshold (e.g., individualized risk scores greater than 7) that indicates that the user is at a high likelihood of a respiratory exacerbation. The user device may generate the alert and/or send a message to one or more of the user’s inhalers that cause the inhalers to generate the alert for the user. The alert could be any one or more of an audible noise generated by a speaker, an illuminate light generated by a light source, a GUI presented on the user device, etc. The alert may indicate that the user should keep their rescue inhaler nearby. Further, the threshold may, for example, be a predetermined value that is compared to an output by the machine learning algorithm, and that signifies a high probability that the user will have difficulty breathing and/or feel the need to use their respiratory rescue medication in the immediate future (e.g., within the next X hours, such as 12 hours, or days, such as 1 day).” in [0216]. Therefore, the first threshold (risk score being greater than 7), the guide is notifying the user to keep their rescue inhaler nearby, and the second threshold (a predetermined value that is compared to an output by the machine learning algorithm, and that signifies a high probability that the user will have difficulty breathing), then the guide (notification) would be notifying the user to use their respiratory rescue medication in the immediate future.
Applicant argues that Naumov does not teach the HVAC is controlled when the patient’s risk score is a predetermined threshold or larger.
In response, Examiner submits that Naumov discloses “…based on the individualized score for the user, the user’s location, and/or past, current, or future weather conditions at that location, the DHP 460 and/or the user device 402a, 402b may control or adjust to set humidity and/or temperature of the HVAC system to decrease the likelihood that the patient experiences an exacerbation event…” in [0222].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687