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
Claims 1-2, 9-10, and 17-18 are currently amended.
Response to Arguments
Applicant's arguments, see pages 9-13, filed 3/11/2026, have been fully considered but they are
not persuasive.
35 U.S.C. 101:
Regarding claim 1, applicant argues that the claims that the claimed invention is not directed to
an abstract idea without significantly more, especially in view of Ex Parte Desjardins (appeal Review Panel, Sept. 26, 2025; designated presidential Nov. 4, 2025) and the December 5, 2025 memorandum integrating Desjardin into the MPEP, which instructs examiners to analyze AI/ML inventions under Enfish/McRO when the claims recite improvements to the functioning of a computer or other technology. After further consideration, the examiner argues that the claims only recites “a machine learning algorithm” configured to perform the abstract idea of comparing monitored heart rate signal and respiratory rate signal. The claim does not recite how the machine learning algorithm is used to make this comparison and additional structure within the machine learning algorithm, such as rules and mathematical logics. Therefore, the rejection is maintained.
Furthermore, applicant argues that the claims are directed to a specific technical architecture
for contactless health monitoring using a moveable sensor assembly, not mere mental observations. After further consideration, the examiner argues that the claim does not recite any structure other than a sensor assembly, a first sensor, and a second sensor configured to perform pre-solution activity to the step of data gathering. The claim is recited at a high level of generality to receive positional data and using said data to move a sensor assembly. The applicant’s specifications [56-60] recites an impulse Radio Ultrawide Band (IR-UWB) sensor and a Passive Infrared (PIR) sensor used to track a user’s positional data. Additionally, the sensor assembly may also comprise a processor and one or more gears coupled to a rotatory motor to provide pivotal and rotational movement. The examiner suggest providing these additional elements to overcome the 35 U.S.C. 101 rejection.
Applicant argues that “predicting by a machine learning algorithm” is not a generic invocation of
machine learning, but rather a specific application of ML to process filtered sensor data. The examiner respectfully disagrees and argues that the machine learning algorithm is recited too generically and is interpreted as generic computer implementation to perform the abstract idea of comparing two signals.
Additionally, the applicant argues that the claim is focused on a specific improvement to health
monitoring system operation and prediction precision. After further consideration, the examiner argues that the improvement towards prediction precision is an improvement towards the abstract idea and not the device itself. The examiner suggests amending the claims to focus on the additional elements of the specified two sensors in combination with the sensor assembly gears and motors to argue for a technological improvement.
Applicant is reminded that abstract ideas cannot provide a practical application or significantly
more (e.g., an improvement). Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). See Genetic Technologies Limited v. Merial LLC (Fed Cir 2016). Here, the additional elements of claims 1, 9, and 17 are merely generically recited computer elements used as tools for executing the abstract ideas or insignificant extra-solution activity.
Applicant’s arguments, see pages 13-15, filed 3/11/2026, with respect to the rejection(s) of
claim(s) 1, 3-9, 11, 13-17, 19, and 20 under 35 U.S.C. 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Bloem.
35 U.S.C. 102 and 103:
Regarding claim 1, applicant argues that Cuddihy, alone or in combination with the prior art,
does not teach “wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor.” After further search and consideration, the examiner will now rely on Bloem to teach this limitation (col. 8, lines 21-41). It is disclosed “the sensor signal generator can be an actuator (i.e., a linear actuator, rotational actuator, or the like) coupled to a motion target. The actuator can be activated to initiate a motion that is known. The known motion can provide a known response in a motion sensor given information regarding the relative position and orientation of the two.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the sensor assembly of Cuddihy to add the actuator from Bloem for the benefit of initiating a rotational motion using positional information given by the sensors from Cuddihy.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 9 and 17 recite a method, an apparatus, and a non-transitory computer with instructions for performing operations of the device comprising:
receiving health data of the user in the facility;
filtering the health data of the user to determine heart rate signal and respiratory rate signal;
monitoring the heart rate signal and the respiratory rate signal over a pre-defined time period;
comparing the monitored heart rate signal and respiratory rate signal with one or more pre- defined thresholds;
predicting, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal;
and generating one or more alerts in response to predicting the possible health issue for the user.
To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is
evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.05. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a method, claim 9 is directed to an apparatus and claim 17 is directed to a
computer readable storage medium storing instructions to perform the steps of the method and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Claims 1, 9 and 17 recite a method, apparatus, and a non-transitory computer with instructions
for performing operations of the device comprising:
receiving health data of the user in the facility;
filtering the health data of the user to determine heart rate signal and respiratory rate signal;
monitoring the heart rate signal and the respiratory rate signal over a pre-defined time period;
comparing the monitored heart rate signal and respiratory rate signal with one or more pre- defined thresholds;
predicting, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal;
and generating one or more alerts in response to predicting the possible health issue for the user.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1, 9, and 17 recite an abstract idea of a mental process.
Claims 1, 9 and 17 recite the abstract idea of a mental process. The limitations as drafted in the
claims, under its broadest reasonable interpretation, covers performance of the claimed steps in the mind, but for the recitation of a generic processor. Other than reciting a generic processing system and memory, nothing in the elements of the claims precludes the step from practically being performed in the mind or manually by a clinician. For example:
“Receiving health data of the user in the facility.” A physician may manually gather health data in a hospital for a patient.
“Filtering the health data of the user to determine heart rate signal and respiratory rate signal.” A physician may manually filter out data that is not needed for analysis using equations.
“Monitoring the heart rate signal and the respiratory rate signal over a pre-defined time period.” A physician may monitor the health of the patient over time.
“Comparing the monitored heart rate signal and respiratory rate signal with one or more pre- defined thresholds.” A comparison step may be manually done by a physician.
“Predicting, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal.” Based on a comparison, the physician may make an educated guess on the patient’s health.
“And generating one or more alerts in response to predicting the possible health issue for the user.” A physician may alert the patient verbally or visually if the patient has a possible health issue.
Furthermore claims 4-6, 8, 12-14, 16 and 18 recite additional steps that can be manually performed by the clinician.
“wherein segregating the heart rate signal and the respiratory rate signal comprises: applying a transformation to the filtered health data to obtain transformed health data, wherein the transformation transforms the filtered health data from time domain to frequency domain; classifying one or more peaks in the transformed health data for a pre-defined range as the respiratory rate signal; isolating the respiratory signal from the transformed health data; identifying a frequency of at least one peak from amongst the one or more peaks as a highest frequency; determining a location of the at least one peak in the transformed health data; and selecting one or more values at the location of the at least one peak in the transformed health data as heart rate signal.” The physician may segregate the heart rate and respiratory signals and transform the data using equations in a frequency domain. A physician may spot and classify peaks in the frequency domain, isolate and classify the highest peak.
“wherein monitoring the heart rate signal and the respiratory rate signal comprises: defining one or more patterns for the heart rate signal and the respiratory rate signal for the pre-defined time period based on the received health data.” A physician may monitor and identify one or more patterns.
“wherein comparing the monitored heart rate signal and respiratory rate signal with one or more pre-defined thresholds comprises: defining the one or more pre-defined thresholds based at least in part on electrocardiogram (ECG) data associated with people of one or more age groups and people suffering from one or more health issues associated with heart and respiratory system; and determining if the monitored heart rate signal and respiratory rate signal meets the one or more pre-defined thresholds.” A physician may take into account age and historical diseases of a patient while making the comparison.
“wherein generating one or more alerts comprises at least one of: triggering one or more alert signals based on the possible health issue predicted for the user; and notifying at least one of a medical personnel or an emergency contact of the user based on the possible health issue predicted for the user.” A physician may make a diagnosis and provide a visual of auditory alert to the patient and other faculty members.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial
exception into a practical application?
Claims 1, 7, 9, and 17 recite the additional elements of a “sensors”, “a processor” and a “a memory”, which are being interpreted as a processor of a data gathering device.
“receiving telemetry data from one or more sensors in the facility, wherein at least a portion of the telemetry data comprises health data of the user in the facility.” The use of sensor to perform this step is pre-solution activity to the step of data gathering.
“predicting by a machine learning algorithm.” This is mere computer implementation to perform an abstract idea.
“first sensor of one or more sensors placed in a sensor assembly in the facility.” A first sensor is recited as pre-solution activity to gather telemetry data.
“wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor;” A second sensor is recited as pre-solution activity to gather positional data.
However, these elements are recited at a high level of generality performing the function of generic data processing such that they amount to no more than mere instructions to simply implement the abstract idea using generic computer components. See MPEP 2106.05(b) and (f).
Accordingly, the additional elements do not integrate the abstract idea into a practical
application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
The additional elements when considered individually and in combination are not enough to
qualify as significantly more than the abstract idea.
“receiving telemetry data from one or more sensors in the facility, wherein at least a portion of the telemetry data comprises health data of the user in the facility.” The use of sensor to perform this step is pre-solution activity to the step of data gathering.
“predicting by a machine learning algorithm.” This is mere computer implementation to perform an abstract idea.
“first sensor of one or more sensors placed in a sensor assembly in the facility.” A first sensor is recited as pre-solution activity to gather telemetry data.
“wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor;” A second sensor is recited as pre-solution activity to gather positional data.
As discussed above with respect to integration of the abstract idea into a practical application, “sensors”, “a processor” and a “a memory”, which are being interpreted as a processor of a data gathering device as recited to perform the steps of:
receiving health data of the user in the facility;
filtering the health data of the user to determine heart rate signal and respiratory rate signal;
monitoring the heart rate signal and the respiratory rate signal over a pre-defined time period;
comparing the monitored heart rate signal and respiratory rate signal with one or more pre- defined thresholds;
predicting, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal;
and generating one or more alerts in response to predicting the possible health issue for the user.
amount to no more than mere instructions to apply the exception using generic computer
components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. These additional elements are well‐understood, routine (For example Cuddihy et al US Pub.: US 20130053653 A1, hereinafter Cuddihy) teaches a data gathering device with a processor and memory, and conventional limitations that amount to mere instructions or elements to implement the abstract idea. In addition, the end result of the system/method, the essence of the whole, is a patent-ineligible concept. Therefore, the claims are not patent eligible.
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.
Claims 1, 4-9, 11, 13-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable
by Cuddihy et al US Pub.: US 20130053653 A1, hereinafter Cuddihy in view of Bloem et al. US Pat.: US 11125907 B2.
Regarding claim 1, Cuddihy teaches a method for monitoring health of a user in a facility, the
method comprising (fig. 1-3; paragraph 18):
receiving telemetry data from a first sensor of one or more sensors placed in a sensor assembly in the facility, wherein at least a portion of the telemetry data comprises health data of the user in the facility (fig. 1-3; paragraph 21 and 24-26); The system 100 further includes one or more sensors 114 for measuring additional parameters that may affect the state of the subject.
filtering the health data of the user to determine heart rate signal and respiratory rate signal (fig. 1-3; paragraph 25 and 41). The filter element 216 generates motion frames 218, heartbeat frames 220 and respiration frames 222 from the received radar signals based on corresponding frequency band characteristics.
monitoring the heart rate signal and the respiratory rate signal over a pre-defined time period (fig. 1-3; paragraph 38-41); The processing unit 116 extracts characteristics and patterns from the gross motion, heartbeat and respiration data captured by monitoring the subject 102 over a period of time.
comparing the monitored heart rate signal and respiratory rate signal with one or more pre-defined thresholds (fig. 1-3; paragraph 28, 40, 48, and 64); The processing unit 116 compares the measured heartbeat and respiration data with corresponding baseline values for early detection of changes in cardiac function that indicate an increased risk of heart disease.
predicting by a machine learning algorithm, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal (fig. 1-3; paragraph 29, 39, 47, 57, and 63); The processing unit 116 evaluates the changes for tracking recovery, medication effects, or predicting increased risk of impending health impairment. The designated limits may be pre-programmed into the system, input by a user or learned by the system over a period of time. Therefore, a machine learning algorithm is disclosed.
and generating one or more alerts in response to predicting the possible health issue for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
However, Cuddihy does not explicitly teach wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor.
Bloem, in the same field of endeavor, teaches wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor (col. 8, lines 21-41). It is disclosed “the sensor signal generator can be an actuator (i.e., a linear actuator, rotational actuator, or the like) coupled to a motion target. The actuator can be activated to initiate a motion that is known. The known motion can provide a known response in a motion sensor given information regarding the relative position and orientation of the two.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the sensor assembly of Cuddihy to add the actuator from Bloem for the benefit of initiating a rotational motion using positional information given by the sensors from Cuddihy.
Regarding claim 3, 11, and 19, Cuddihy teaches wherein filtering the health data of the user
comprises:
applying a filter to the health data to remove one or more noise signals from the health data; and segregating the heart rate signal and the respiratory rate signal of the user from the filtered health data (fig. 1-3; paragraph 25 and 41). The filter element 216 generates motion frames 218, heartbeat frames 220 and respiration frames 222 from the received radar signals based on corresponding frequency band characteristics.
Regarding claim 5 and 13, Cuddihy teaches wherein monitoring the heart rate signal and the
respiratory rate signal comprises: defining one or more patterns for the heart rate signal and the respiratory rate signal for the pre-defined time period based on the received health data (fig. 1-3; paragraph 38-41); The processing unit 116 extracts characteristics and patterns from the gross motion, heartbeat and respiration data captured by monitoring the subject 102 over a period of time.
Regarding claim 6 and 14, Cuddihy teaches wherein comparing the monitored heart rate signal
and respiratory rate signal with one or more pre-defined thresholds comprises: defining the one or more pre-defined thresholds based at least in part on electrocardiogram (ECG) data associated with people of one or more age groups and people suffering from one or more health issues associated with heart and respiratory system; and determining if the monitored heart rate signal and respiratory rate signal meets the one or more pre-defined thresholds (fig. 1-3; paragraph 28, 40, 47-48, and 64).The processing unit 116 may also adjust the subject's physiological parameters in light of a known health condition or mental state of the subject 102. The processing unit 116 compares the measured heartbeat and respiration data with corresponding baseline values for early detection of changes in cardiac function that indicate an increased risk of heart disease.
Regarding claim 7 and 15, Cuddihy teaches further comprising: training the machine learning
algorithm based at least in part on electrocardiogram (ECG) data associated with people of one or more age groups and people suffering from one or more health issues associated with heart and respiratory system (fig. 1-3; paragraph 28, 40, 47-48, and 64).The processing unit 116 may also adjust the subject's physiological parameters in light of a known health condition or mental state of the subject 102. The designated limits may be pre-programmed into the system, input by a user or learned by the system over a period of time. Therefore, a machine learning algorithm is disclosed.
Regarding claim 8 and 16, Cuddihy teaches wherein generating one or more alerts comprises at
least one of: triggering one or more alert signals based on the possible health issue predicted for the user; and notifying at least one of a medical personnel or an emergency contact of the user based on the possible health issue predicted for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
Regarding claim 9, Cuddihy teaches system for monitoring health of a user in a facility, the system comprising:
a sensor assembly, wherein the sensor assembly comprises one or more sensors (fig. 1-3; paragraph 21 and 24-26);
a processor (fig. 1-3; paragraph 37);
a memory (fig. 1-3; paragraph 38-39) communicatively coupled to the processor, wherein the memory comprises one or more instructions which when executed by the processor cause the system to: receive telemetry data from a first sensor of the one or more sensors, wherein at least a portion of the telemetry data comprises health data of the user in the facility (fig. 1-3; paragraph 21 and 24-26); The system 100 further includes one or more sensors 114 for measuring additional parameters that may affect the state of the subject.
filter the health data of the user to determine heart rate signal and respiratory rate signal (fig. 1-3; paragraph 25 and 41). The filter element 216 generates motion frames 218, heartbeat frames 220 and respiration frames 222 from the received radar signals based on corresponding frequency band characteristics.
monitor the heart rate signal and the respiratory rate signal over a pre-defined time period (fig. 1-3; paragraph 38-41); The processing unit 116 extracts characteristics and patterns from the gross motion, heartbeat and respiration data captured by monitoring the subject 102 over a period of time.
compare the monitored heart rate signal and respiratory rate signal with one or more pre-defined thresholds (fig. 1-3; paragraph 28, 40, 48, and 64); The processing unit 116 compares the measured heartbeat and respiration data with corresponding baseline values for early detection of changes in cardiac function that indicate an increased risk of heart disease.
predict by a machine learning algorithm, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal (fig. 1-3; paragraph 29, 39, 47, 57, and 63); The processing unit 116 evaluates the changes for tracking recovery, medication effects, or predicting increased risk of impending health impairment. The designated limits may be pre-programmed into the system, input by a user or learned by the system over a period of time. Therefore, a machine learning algorithm is disclosed.
and generate one or more alerts in response to predicting the possible health issue for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
However, Cuddihy does not explicitly teach wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor.
Bloem, in the same field of endeavor, teaches wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor (col. 8, lines 21-41). It is disclosed “the sensor signal generator can be an actuator (i.e., a linear actuator, rotational actuator, or the like) coupled to a motion target. The actuator can be activated to initiate a motion that is known. The known motion can provide a known response in a motion sensor given information regarding the relative position and orientation of the two.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the sensor assembly of Cuddihy to add the actuator from Bloem for the benefit of initiating a rotational motion using positional information given by the sensors from Cuddihy.
Regarding claim 17, Cuddihy a teaches non-transitory (paragraph 10), computer-readable
storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to:
receive telemetry data from a first sensor of one or more sensors placed in a sensor assembly in the facility, wherein at least a portion of the telemetry data comprises health data of a user in a facility (fig. 1-3; paragraph 21 and 24-26); The system 100 further includes one or more sensors 114 for measuring additional parameters that may affect the state of the subject.
filter the health data of the user to determine heart rate signal and respiratory rate signal (fig. 1-3; paragraph 25 and 41). The filter element 216 generates motion frames 218, heartbeat frames 220 and respiration frames 222 from the received radar signals based on corresponding frequency band characteristics.
monitor the heart rate signal and the respiratory rate signal over a pre-defined time period (fig. 1-3; paragraph 38-41); The processing unit 116 extracts characteristics and patterns from the gross motion, heartbeat and respiration data captured by monitoring the subject 102 over a period of time.
compare the monitored heart rate signal and respiratory rate signal with one or more pre- defined thresholds (fig. 1-3; paragraph 28, 40, 48, and 64); The processing unit 116 compares the measured heartbeat and respiration data with corresponding baseline values for early detection of changes in cardiac function that indicate an increased risk of heart disease.
predict by a machine learning algorithm, a possible health issue for the user based on the comparison of the monitored heart rate signal and respiratory rate signal (fig. 1-3; paragraph 29, 39, 47, 57, and 63); The processing unit 116 evaluates the changes for tracking recovery, medication effects, or predicting increased risk of impending health impairment. The designated limits may be pre-programmed into the system, input by a user or learned by the system over a period of time. Therefore, a machine learning algorithm is disclosed.
and generate one or more alerts in response to predicting the possible health issue for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
However, Cuddihy does not explicitly teach wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor.
Bloem, in the same field of endeavor, teaches wherein a position of the user is tracked by a second sensor of the one or more sensors, and wherein the sensor assembly is moved based on the position of the user tracked by the second sensor (col. 8, lines 21-41). It is disclosed “the sensor signal generator can be an actuator (i.e., a linear actuator, rotational actuator, or the like) coupled to a motion target. The actuator can be activated to initiate a motion that is known. The known motion can provide a known response in a motion sensor given information regarding the relative position and orientation of the two.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the sensor assembly of Cuddihy to add the actuator from Bloem for the benefit of initiating a rotational motion using positional information given by the sensors from Cuddihy.
Regarding claim 20, Cuddihy teaches wherein the one or more processors is further configured
to: define the one or more pre-defined thresholds based at least in part on electrocardiogram (ECG) data associated with people of one or more age groups and people suffering from one or more health issues associated with heart and respiratory system (fig. 1-3; paragraph 21 and 24-26); The system 100 further includes one or more sensors 114 for measuring additional parameters that may affect the state of the subject.
and determine if the monitored heart rate signal and respiratory rate signal meets the one or more pre-defined thresholds (fig. 1-3; paragraph 28, 40, 48, and 64); The processing unit 116 compares the measured heartbeat and respiration data with corresponding baseline values for early detection of changes in cardiac function that indicate an increased risk of heart disease.
train the machine learning algorithm based at least in part on the ECG data associated with people of one or more age groups and people suffering from one or more health issues associated with heart and respiratory system; (fig. 1-3; paragraph 29, 39, 47, 57, and 63); The processing unit 116 evaluates the changes for tracking recovery, medication effects, or predicting increased risk of impending health impairment. The designated limits may be pre-programmed into the system, input by a user or learned by the system over a period of time. Therefore, a machine learning algorithm is disclosed.
trigger one or more alert signals based on the possible health issue predicted for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
and notify at least one of a medical personnel or an emergency contact of the user based on the possible health issue predicted for the user (fig. 1-3; paragraph 30 and 49). The processing unit 116 triggers an alert through an alerting system 118 coupled to the radar system 108 and/or the processing subsystem 116. The processing unit 116, for example, may generate and alert if the detected heartbeat and/or respiration values of an infant remain outside corresponding threshold values for more than a determined period of time.
Claims 2, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Cuddihy in
view of Bloem in view of COFFEY et al. US Pub.: US 20230000396 A1, hereinafter Coffey.
Regarding claim 2, 10, and 18, Cuddihy teaches wherein receiving telemetry data from the one
or more sensors in the facility comprises: tracking a position of the user using at least one sensor of the one or more sensors, and wherein the motion corresponds to at least one of: a pivotal motion and a rotatory motion; and receiving the health data associated with the user from the one or more sensors, including Passive Infrared (PIR) sensor, in the facility (paragraph 21 and 37).
However, Cuddihy does not teach first sensor comprises an Impulse Radio Ultrawide Band (IR-
UWB) sensor and wherein the second sensor comprises a Passive Infrared (PIR) sensor; and wherein moving the sensor assembly comprises at least one of: a pivotal motion and a rotatory motion.
Bloem, in the same field of endeavor, teaches wherein moving the sensor assembly comprises at least one of: a pivotal motion and a rotatory motion (col. 8, lines 21-41). It is disclosed “the sensor signal generator can be an actuator (i.e., a linear actuator, rotational actuator, or the like) coupled to a motion target. The actuator can be activated to initiate a motion that is known. The known motion can provide a known response in a motion sensor given information regarding the relative position and orientation of the two.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify the sensor assembly of Cuddihy to add the actuator from Bloem for the benefit of initiating a rotational motion using positional information given by the sensors from Cuddihy.
Coffey, in the same field of endeavor, teaches wherein the one or more sensors comprise: Impulse Radio Ultrawide Band (IR-UWB) sensor and Passive Infrared (PIR) sensor. The control system can apply an algorithm to incoming data from a sensor (e.g., an ultra-wide band (UWB)-based sensor and an infrared (IR)-based sensor.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor system of Cuddihy to add the Impulse Radio Ultrawide Band (IR-UWB) sensor from Coffey for the benefit of tracking moving objects with a high degree of precision within the environment in which the object is moving. The wide bandwidth of the signal along with very short duration impulses allows for high resolution sensing and multipath capability, along with RF co-existence.
Claims 3, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cuddihy in
view of Bloem in view of Bridges et al. US Pub.: US 20230233089 A1.
Regarding claim 3, 12, and 20, Cuddihy in view of Bloem does not teach wherein segregating the
heart rate signal and the respiratory rate signal comprises: applying a transformation to the filtered health data to obtain transformed health data, wherein the transformation transforms the filtered health data from time domain to frequency domain; classifying one or more peaks in the transformed health data for a pre-defined range as the respiratory rate signal; isolating the respiratory signal from the transformed health data; identifying a frequency of at least one peak from amongst the one or more peaks as a highest frequency; determining a location of the at least one peak in the transformed health data; and selecting one or more values at the location of the at least one peak in the transformed health data as heart rate signal.
Bridges, in the same field of endeavor, teaches wherein segregating the heart rate signal
and the respiratory rate signal comprises: applying a transformation to the filtered health data to obtain transformed health data, wherein the transformation transforms the filtered health data from time domain to frequency domain; classifying one or more peaks in the transformed health data for a pre-defined range as the respiratory rate signal; isolating the respiratory signal from the transformed health data; identifying a frequency of at least one peak from amongst the one or more peaks as a highest frequency; determining a location of the at least one peak in the transformed health data; and selecting one or more values at the location of the at least one peak in the transformed health data as heart rate signal (Table 1; paragraph 76).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processing steps of Cuddihy in view of Bloem with the transforming and isolating steps of Bridges for the benefit of accurately quantifying and identify abnormalities in the sensor signals.
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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/T.J.T./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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