DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed June 30, 2025.
Claims 1-20 are pending
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 .
Claim Rejection - 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 to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 1, 15, and 19:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method.
Independent claim 15 falls within the statutory category of machine.
Independent Claim 19 falls within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 15, and 19 claimed invention are directed to a judicial exception.
The claim elements in the independent claims (claim 1 being representative) which set forth the abstract idea are:
A method comprising:
receiving, a first treatment plan designed to treat a cardiovascular health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the cardiovascular health issue of the user, enable the user to perform an exercise at different exertion levels;
while the user to perform the first treatment plan for the user, receiving cardiovascular data configured to measure the cardiovascular data associated with the user;
transmitting the cardiovascular data, wherein are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level,
and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user;
receiving the second treatment plan.
which falls within “certain methods of organizing human activity” as following rules or instructions and managing personal behavior to generate a treatment plan. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent Claims 1, 15, and 19 this judicial exception is not integrated into a practical application.
In Claim 1 the additional elements are:
a computer
a computing device
a treatment apparatus
one or more sensors
one or more machine learning models
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a computer, is recited as executing the abstract idea as “apply-it” as a computer tool
The additional element, a computing device, is recited as a tool or equivalent to apply the abstract idea as “apply-it” to gather data
The additional element, a treatment apparatus, is recited as a tool or equivalent to apply the abstract idea as “apply-it” (e.g. “using”) to manipulate data
The additional element, one or more sensors, is recited as a tool or equivalent to apply the abstract idea as “apply-it” to gather data
The additional element, one or more machine learning models, is recited as a tool or equivalent to apply the abstract idea as “apply-it” (e.g. “are used”) to analyze data
In claim 15 the additional elements are:
An interface with a display
A processing device
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a) and (b), are recited as executing the abstract idea and as computer tools to apply the abstract idea as “apply-it” to gather, output, and analyze data
In claim 19 the additional elements are:
A tangible, non-transitory computer-readable medium stores instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), are recited as executing the abstract idea and as computer tools to apply the abstract idea as “apply-it” to analyze data
Accordingly, claims 1, 15, and 19 do not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 1, 15, and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than applying the abstract idea thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 2-14, 16-18, and 20:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-114 falls within the statutory category of method.
The dependent claims 16-18 fall within the statutory category of machine.
The dependent claim 20 falls within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-14, 16-18, and 20 claimed invention are directed to a judicial exception.
Dependent claims 2-14, 16-18, and 20 continue to limit the abstract idea in the independent claims by (1) further limiting the second treatment plan, (2) further limiting the standardized measure of perceived exertion (3) further limiting the first treatment plan, (4) further limiting the cardiovascular data, (5) further limiting performing the treatment plan, and (6) further limiting the cardiovascular heart issue thus inheriting the same abstract idea which falls within “certain methods of organizing human activity” as following rules or instructions and managing personal behavior to generate a treatment plan. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): In Claims 2-14, 16-18, and 20 this judicial exception is not integrated into a practical application.
In Claims 2-14, 16-18, and 20 the additional elements not already recited in the independent claims are:
Trained one or more machine learning models
A cycling machine
An electrocardiogram sensor
Second computing device
Telemedicine session
Third computing device
A wearable device
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is used as a tool to analyze data as “apply-it" to analyze data
The additional elements, (b), is used as a tool to analyze data as “apply-it" to execute a treatment plan
The additional elements, (c), (d), (e), (f), and (g) is used as a tool to analyze data as “apply-it" to gather data
Eligibility Step 2B (Does the claim amount to significantly more?): Dependent claims 2-14, 16-18, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than apply-it and thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of prior U.S. Patent No. 17736891. This is a statutory double patenting rejection.
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 1-7 and 9-20 are rejected under 35 U.S.C.103 as being unpatentable over Volosin et. al (hereinafter Volosin) (US20210035674A1) in view of Daniel (US20200005928A1)
As per claim 1, Volosin teaches:
A computer-implemented method comprising: receiving, at a computing device, a first treatment plan designed to treat a cardiovascular health issue of a user, (abstract discloses, “. The server includes a processor configured to receive input regarding the rehabilitation plan ; generate one or more plans specifying individualized set of rehabilitative exercise sessions for a patient , receive electrocardiogram ( ECG ) and non - ECG physiological information acquired from the patient compare the ECG and / or non - ECG physiological information to predetermined criteria and dynamically adjust the cardiac rehabilitation plan based on the comparison to create an adjusted cardiac rehabilitation plan”)
wherein the first treatment plan comprises at least two exercise sessions that, based on the cardiovascular health issue of the user, enable the user to perform an exercise at different exertion levels; (se figs. 7C-7E and see [ 0143 ] discloses, “As shown in FIG . 8 , the processor can receive 802 physician - provided information for a cardiac rehabilitation plan and , based upon the received information , generate 804 the cardiac rehabilitation plan . For example , the processor can provide user interfaces similar to those as shown in FIGS . 7C , 7D and 7E , and receive physician - provided information as it is entered into the user interface ( s ) . FIG . 9A illustrates an expanded set of process actions for generating 804 a cardiac rehabilitation plan , similar to processes 500 and 600 as shown in FIGS . 5 and 6 and as described above . However , FIG . 9A provides additional detail for an alternative process for generating a cardiac rehabilitation plan for a specific patient . For example , the processor can determine 902 an associated difficulty level for the cardiac rehabilitation plan based upon the physician - provided information . As noted above , the difficulty level for a cardiac rehabilitation plan can be labeled with a descriptor such as “ easy , ” “ medium , ” and “ hard ” as described herein , the difficulty levels providing an indication of how strenuous the cardiac rehabilitation plan is and how much exertion is required as the patient progresses through the plan and completes various exercise sessions . In certain implementations , the difficulty level of a cardiac rehabilitation plan can be automatically determined 902 based upon the patient's condition as determined from , for example , the patient's medical record or as provided by the physician.” And see [0144] and [0145] and see [0117])
while the user uses a treatment apparatus to perform the first treatment plan for the user, receiving cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user; (see [0351] discloses, “. Such actions can include adding new patients for whom cardiac rehabilitation progress is to be monitored from one or more of a doctor , hospital , or other HCP computer network system , adding new devices associated with patients who are newly pre scribed one or more cardiac monitoring and / or treatment devices , remotely changing the monitoring and / or treatment parameters of such devices , entering informational , reminder , and / or prescriptive messages that are transmitted to displays associated with the devices for viewing by patients or other persons associated with the patients , monitoring the operational status of devices , querying the devices for the most current ECG , physical activity , and / or other physiological information collected by the devices , remotely adding or changing one or more types of monitoring service implemented by the device ( e.g. , switching a cardiac monitoring only service to a cardiac monitoring and treatment service ) , querying a doctor , hospital or other HCP network system for updated information associated with a patient” and see [0175] discloses, “Returning to FIG . 1 , the non - ECG physiological information can be derived from non - ECG physiological sensors 123 configured to be attached to the patient as disclosed herein . For example , such sensors and associated circuitry for determining one or both of an anaerobic thresh old and oxygen consumption rates can be configured to be attached directly to an arm a subject via an adjustable ( e.g. , elastic ) strap or adhesive pad . Alternatively , or in addition , the non - ECG physiological sensors and associated circuitry can be attached to a belt or waistband , e.g. , of the garment ( e.g. , garment 1710 of FIG . 17A ) . In some configurations , the non - ECG sensors can extend from the controller 1720 in FIG . 17A and be configured to be attached to the skin of the patient via an attachment mechanism such as an adhesive pad.” And see [0176] discloses, “The processor 118 can execute instructions configured to measure both anaerobic threshold and oxygen consumption rates . For example , the controller 100 can transmit , ( e.g. , wirelessly transmit via network interface 106 ) the non - ECG physiological data parameters such as anaerobic threshold and oxygen consumption rates , to a remote computing device such as a monitoring server 308 ( FIG . 3 ) for comparison against predetermined criteria and subsequent dynamic adjustment of the plan based on the comparison.” And see [ 0177 ] discloses, “For example , initial or baseline values for an anaerobic threshold and / or oxygen consumption rate can be calculated during a clinical visit by the patient . Once the initial or baseline value ( s ) are determined , an HCP can set the predetermined criteria using such initial or baseline value ( s ) . For example , if the anaerobic threshold determined from initial or baseline blood lactate measurements , rate of carbon dioxide production ( VCO2 ) , or [ H + ] measurements is around 1.95 +/- 0.27 L / min , the predetermined criteria for such anaerobic threshold for the patient can be set to around a maximum of 2 L / min.”)
transmitting the cardiovascular data, wherein …[…]…generate a second treatment plan, ([0079] discloses, “Where adjustments are needed , the server can automatically generate one or more updated cardiac rehabilitation plans over the course of the patient's rehabilitation efforts . These updates can include dynamic adjustments to the rehabilitation plan based on the received ECG and non - ECG physiological information , the additional physical response data , and the patient's progression through the rehabilitation plan . In addition , the server can cause the patient's wearable medical device to execute the adjusted rehabilitation plan using the processes described generally above and described in more detail below”)
wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user; (see [0164] and see [0165] and see [0166] discloses, “The Peak VO2 max for a session can be calculated using the peak heart rate for the session . A peak VO2 parameter can be user - configurable under supervision of an HCP and can be used to determine adjustments to the plan . For example , if the patient's peak VO2 max indicates that the patient is able to easily perform the " easy " exercises , the plan can dynamically adjust to increase the intensity to the " medium " exercises , or further on to the “ hard ” exercises.”)
receiving the second treatment plan. ([0080] discloses, “In some examples , the system ( e.g. , the server and / or the wearable medical device ) can implement user interface through which the system can receive input from the HCP regarding the initial rehabilitation plan and / or updated rehabilitation plans . This user interface can include controls that display and / or receive input regarding elements of a rehabilitation plan. In this way , these example systems enable an HCP to approve and / or modify the initial rehabilitation plan and / or dynamic adjustments specified in one or more updated rehabilitation plans.”)
However, Volosin does not teach:
…[…]…one or more machine learning models are used to …[…]…
However, Daniel does teach:
…[…]…one or more machine learning models are used to…[…]… ([0058] discloses, “The information is processed by applying one or more decision - making algorithms , such as one or more rule - based and / or machine learning procedures . In step 606 , the system generates and outputs a risk report that is personalized for the user , as well as a personalized wellness plan with respect to nutrition , behavior and activity . The personalized wellness plan can include , for example , meal and nutrition plans , exercise plans , sleep routine planning , lifestyle and behavior modification , etc.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Volosin’s teachings as previously cited with Daniel’s teachings as previously cited, the motivation being, Volosin teaches the need to dynamically adjust the rehabilitation plans (see abstract for e.g.), therefore, it would be obvious to someone of ordinary skill to utilize machine learning to take a manual computer process and more efficiently execute the dynamic adjustment using machine learning on a computer and would not make the invention inoperable.
As per claim 2, Volosin further teaches:
The computer-implemented method of claim 1, wherein the second treatment plan comprises a modified parameter pertaining to the treatment apparatus, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the treatment apparatus, a speed, or some combination thereof, (see [0164] and see [0165] and see [0166] discloses, “The Peak VO2 max for a session can be calculated using the peak heart rate for the session . A peak VO2 parameter can be user - configurable under supervision of an HCP and can be used to determine adjustments to the plan . For example , if the patient's peak VO2 max indicates that the patient is able to easily perform the " easy " exercises , the plan can dynamically adjust to increase the intensity to the " medium " exercises , or further on to the “ hard ” exercises.” And see medium " exercises , or further on to the “ hard ” exercises.” And see [0167] discloses, “In implementations , metabolic equivalent of tasks ( METs ) can be assigned as indicated in the below table . In examples , a MET is used to describe the energy needed to sit quietly . Moderate - intensity activities are those that the patient moving fast enough or strenuously enough to burn off three to six times as much energy per minute as they would do when sitting quietly ( e.g. , exercises that are assigned 3 to 9 METs ) . Hard activities can be assigned more than 6 METs as shown below” and see table 8)
and the computer-implemented method further comprises: controlling the treatment apparatus based on the modified parameter. ([0071] discloses, “Wearable medical devices are continuously worn and so can continuously monitor ECG and other physiological information of ambulatory patients . Systems , devices , and techniques herein implement cardiac rehabilitation plans that drawn on such continuously available real - time ECG and non - ECG physiological information in at least two ways . First , the device is configured to provide individualized plans that are dynamically adjusted based on the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient . Second , the device is configured to measure the patient's adherence to such plans based on the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient . In these implementations , rather than relying only on patient responses and patient self - reporting of progress or adherence , the device is configured to use the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient to determine how to automatically adjust the plan and / or record accurately whether the patient is performing the activities in the plan . For example , the non - ECG physiological information can include patient motion information ( such as step rate , patient position , and posture ) , respiration information , lung fluid level information , pulse information , blood oxygenation information ( e.g. , VO2 metrics , VCO2 metrics , etc. ) , blood pressure information , and other such information . In some implementations , the device is configured to process patient feedback received via a user interface ( e.g. , responses to questions , pre- and post - workout questions and surveys , indications of exertion , such as a rating of perceived exertion or RPE ) when dynamically adjusting a plan or tracking patient adherence to the plan.” And see [0176] discloses, “The processor 118 can execute instructions configured to measure both anaerobic threshold and oxygen consumption rates . For example , the controller 100 can transmit , ( e.g. , wirelessly transmit via network interface 106 ) the non - ECG physiological data parameters such as anaerobic threshold and oxygen consumption rates , to a remote computing device such as a monitoring server 308 ( FIG . 3 ) for comparison against predetermined criteria and subsequent dynamic adjustment of the plan based on the comparison.”)
As per claim 3, Volosin further teaches:
The computer-implemented method of claim 1, wherein the standardized measure of perceived exertion comprises metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE). And see [0167] discloses, “In implementations , metabolic equivalent of tasks ( METs ) can be assigned as indicated in the below table . In examples , a MET is used to describe the energy needed to sit quietly . Moderate - intensity activities are those that the patient moving fast enough or strenuously enough to burn off three to six times as much energy per minute as they would do when sitting quietly ( e.g. , exercises that are assigned 3 to 9 METs ) . Hard activities can be assigned more than 6 METs as shown below” and see table 8) and see [0233] discloses, “Shown below in Table 15 is another sample cardiac plan . Here , the RPE indicating exercise intensity is designated on the Borg scale of 6 to 20. Further , the notations “ VVL ” means very , very light , “ VL ” means very light , “ L ” means light , and “ SH ” means somewhat hard.” And see table 15)
As per claim 4, Volosin further teaches:
The computer-implemented method of claim 1, …[…]…generate the second treatment plan …[…]….using data pertaining to the standardized measure of perceived exertion, other users' cardiovascular data, and other users' cardiovascular health issues. (see [0164] and see [0165] and see [0166] discloses, “The Peak VO2 max for a session can be calculated using the peak heart rate for the session . A peak VO2 parameter can be user - configurable under supervision of an HCP and can be used to determine adjustments to the plan . For example , if the patient's peak VO2 max indicates that the patient is able to easily perform the " easy " exercises , the plan can dynamically adjust to increase the intensity to the " medium " exercises , or further on to the “ hard ” exercises.” And see [0313] discloses, “The processor can transmit 1606 the patient progress data to a community portal . For example , the community portal can include other patients who are progressing through cardiac rehabilitation programs , healthcare providers , friends and family of the patient , and other similar supportive acquaintances in the patient's life . Those members the community portal can provide feedback such as positive reinforcement comments for the patient . The processor can be configured to receive 1608 the community feedback from the community portal to cause the community feedback to be displayed 1610 to the patient” and see e.g. [0324])
However, Volosin does not teach the underlined portion:
The computer-implemented method of claim 1, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the desired exertion level for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users' cardiovascular data, and other users' cardiovascular health issues.
However, Daniel does teach:
The computer-implemented method of claim 1, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the desired exertion level for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users' cardiovascular data, and other users' cardiovascular health issues. ([0058] discloses, “The information is processed by applying one or more decision - making algorithms , such as one or more rule - based and / or machine learning procedures . In step 606 , the system generates and outputs a risk report that is personalized for the user , as well as a personalized wellness plan with respect to nutrition , behavior and activity . The personalized wellness plan can include , for example , meal and nutrition plans , exercise plans , sleep routine planning , lifestyle and behavior modification , etc.” and see [0060] discloses, “With respect to exercise , decision - making algorithms may define such aspects as exercise intensity and frequency ( e.g. , minutes per week ) . The continually updated information in the database is used to not only provide feedback to users , but to continually refine and train the decision - making algorithms , so that the algorithms may deliver more effective and personalized feedback over time.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Volosin’s teachings as previously cited with Daniel’s teachings as previously cited for the same reasoning given for claim 1.
As per claim 5, Volosin further teaches:
The computer-implemented method of claim 1, wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, or some combination thereof. ([0086] discloses, “In some examples , a wearable medical device is configured to routinely assess various parameters before , during , and after each rehabilitation session , as deemed appropriate by an HCP . These parameters can include heart rate assessment , blood pressure assessment , changes in patient weight , symptoms of exercise intolerance , symptoms of evidence of change in clinical status , changes in medication adherence , ECG monitoring , non - ECG physiological signal monitoring , and patient feedback . Parameters similar to those as shown in Table 1 can be evaluated by a supervising professional prior to or during an exercise session to determine if the patient should skip / postpone an exercise session or stop an ongoing exercise session . Additionally , it should be noted that the parameters assessed , as described above , can be adjusted for an individual patient as the patient progresses through the rehabilitation period”)
As per claim 6, Volosin further teaches:
The computer-implemented method of claim 1, wherein the transmitting the cardiovascular data further comprises transmitting the cardiovascular data to a second computing device that relays the cardiovascular data to a third computing device of a healthcare professional. ([0023] discloses, “The server computer can include a first network interface configured to receive the rehabilitation goal information from the HCP computing device and a second network interface configured to remotely communicate with at least a wearable cardiac monitoring device being worn by the cardiac rehabilitation patient , the wearable cardiac monitoring device including a plurality of electrocardiogram ( ECG ) electrodes configured to continuously sense an ECG signal for the cardiac rehabilitation patient and a motion sensor configured to monitor a movement of the cardiac rehabilitation patient and produce a motion signal . The second network interface can be further configured to receive patient baseline information from the wearable cardiac monitoring device.”)
As per claim 7, Volosin further teaches:
The computer-implemented method of claim 1, wherein the cardiovascular data comprises a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non- cardiovascular diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof. ( [0071] discloses, “Wearable medical devices are continuously worn and so can continuously monitor ECG and other physiological information of ambulatory patients . Systems , devices , and techniques herein implement cardiac rehabilitation plans that drawn on such continuously available real - time ECG and non - ECG physiological information in at least two ways . First , the device is configured to provide individualized plans that are dynamically adjusted based on the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient . Second , the device is configured to measure the patient's adherence to such plans based on the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient . In these implementations , rather than relying only on patient responses and patient self - reporting of progress or adherence , the device is configured to use the most current and / or real time ECG and non - ECG physiological information from the ambulatory patient to determine how to automatically adjust the plan and / or record accurately whether the patient is performing the activities in the plan . For example , the non - ECG physiological information can include patient motion information ( such as step rate , patient position , and posture ) , respiration information , lung fluid level information , pulse information , blood oxygenation information ( e.g. , VO2 metrics , VCO2 metrics , etc. ) , blood pressure information , and other such information . In some implementations , the device is configured to process patient feedback received via a user interface ( e.g. , responses to questions , pre- and post - workout questions and surveys , indications of exertion , such as a rating of perceived exertion or RPE ) when dynamically adjusting a plan or tracking patient adherence to the plan.”)
As per claim 9, Volosin further teaches:
The computer-implemented method of claim 1, wherein the cardiovascular heart issue comprises heart surgery performed on the user a heart transplant performed on the user, a heart arrhythmia of the user, an atrial fibrillation of the user, tachycardia, bradycardia, supraventricular tachycardia, congestive heart failure, heart valve disease, arteriosclerosis, atherosclerosis, pericardial disease, pericarditis, myocardial disease, myocarditis, cardiomyopathy, congenital heart disease, or some combination thereof. ([0039] discloses, “In some example , the at least one physiological event can include at least one of an atrial fibrillation event , a bradycardia event , a tachycardia event such as tachycardia onset and tachycardia offset , a pause in heart rate , high average heart rate , a transitory heart rate spike , and a transitory heart rate dip”)
As per claim 10, Volosin further teaches:
The computer-implemented method of claim 1, wherein: the computing device is a relay between the one or more sensors and a second computing device of a healthcare professional, and the computing device and the second computing device are communicatively coupled in a telemedicine session. ( [0114] discloses, “FIG . 7D illustrates a sample user interface screen 725 that the processor can provide to a physician in response to , for example , receiving a selection of the plan control 720d of FIG . 7C by the physician . For example , the user interface screen 725 can be implemented as a portion of the plan generation process 414 as described above . The processor can provide the user interface screen 725 to interact with the physician via user interface 406 of FIG . 4.”)
As per claim 11, Volosin further teaches:
The computer-implemented method of claim 1, wherein: the computing device is a relay between the one or more sensors and a second computing device of a healthcare professional, and a third computing device is attached to the treatment apparatus and presents, on the display, information pertaining to the treatment plan, the second treatment plan, or both. ([0023] discloses, “The server computer can include a first network interface configured to receive the rehabilitation goal information from the HCP computing device and a second network interface configured to remotely communicate with at least a wearable cardiac monitoring device being worn by the cardiac rehabilitation patient , the wearable cardiac monitoring device including a plurality of electrocardiogram ( ECG ) electrodes configured to continuously sense an ECG signal for the cardiac rehabilitation patient and a motion sensor configured to monitor a movement of the cardiac rehabilitation patient and produce a motion signal . The second network interface can be further configured to receive patient baseline information from the wearable cardiac monitoring device.” And see [0080] discloses, “In some examples , the system ( e.g. , the server and / or the wearable medical device ) can implement user interface through which the system can receive input from the HCP regarding the initial rehabilitation plan and / or updated rehabilitation plans . This user interface can include controls that display and / or receive input regarding elements of a rehabilitation plan . In this way , these example systems enable an HCP to approve and / or modify the initial rehabilitation plan and / or dynamic adjustments specified in one or more updated rehabilitation plans”)
As per claim 12, Volosin further teaches:
The computer-implemented method of claim 1, further comprising: controlling the treatment apparatus based on an operating parameter specified in the treatment plan, the second treatment plan, or both. ([0228] discloses, “Referring back to FIG . 8 , following adjustment of the cardiac rehabilitation plan , the processor can transmit the adjusted cardiac rehabilitation plan to the patient's wearable medical device and cause 814 execution of the adjusted cardiac rehabilitation plan on the device . In certain implementations , the processor can further confirm that the wear able medical device is operating according to the adjusted cardiac rehabilitation plan.”)
As per claim 13, Volosin further teaches:
The computer-implemented method of claim 1, further comprising: while the user uses a treatment apparatus to perform the first treatment plan for the user, receiving feedback from the user, ([0071] discloses, “In some implementations , the device is configured to process patient feedback received via a user interface ( e.g. , responses to questions , pre- and post - workout questions and surveys , indications of exertion , such as a rating of perceived exertion or RPE ) when dynamically adjusting a plan or tracking patient adherence to the plan”)
wherein the feedback comprises input from a microphone, a touchscreen, a keyboard, a mouse, a touchpad, a wearable device, the computing device, or some combination thereof; ([0071] discloses, “Wearable medical devices are continuously worn and so can continuously monitor ECG and other physiological information of ambulatory patients….[…]….In some implementations , the device is configured to process patient feedback received via a user interface ( e.g. , responses to questions , pre- and post - workout questions and surveys , indications of exertion , such as a rating of perceived exertion or RPE ) when dynamically adjusting a plan or tracking patient adherence to the plan”)
transmitting the feedback, …[…]…uses the feedback to generate the second treatment plan. ([0071] discloses, “In some implementations , the device is configured to process patient feedback received via a user interface ( e.g. , responses to questions , pre- and post - workout questions and surveys , indications of exertion , such as a rating of perceived exertion or RPE ) when dynamically adjusting a plan or tracking patient adherence to the plan”)
However, Volosin does not teach the underlined portion:
transmitting the feedback, wherein the one or more machine learning models uses the feedback to generate the second treatment plan.
However, Daniel does teach the underlined version:
transmitting the feedback, wherein the one or more machine learning models uses the feedback to generate the second treatment plan. ([0058] discloses, “The information is processed by applying one or more decision - making algorithms , such as one or more rule - based and / or machine learning procedures . In step 606 , the system generates and outputs a risk report that is personalized for the user , as well as a personalized wellness plan with respect to nutrition , behavior and activity . The personalized wellness plan can include , for example , meal and nutrition plans , exercise plans , sleep routine planning , lifestyle and behavior modification , etc.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Volosin’s teachings as previously cited with Daniel’s teachings as previously cited for the same reasoning given for claim 1.
As per claim 14, Volosin further teaches:
The computer-implemented of claim 1, further comprising presenting the second treatment plan on a display. ([0080] discloses, “In some examples , the system ( e.g. , the server and / or the wearable medical device ) can implement user interface through which the system can receive input from the HCP regarding the initial rehabilitation plan and / or updated rehabilitation plans . This user interface can include controls that display and / or receive input regarding elements of a rehabilitation plan. In this way , these example systems enable an HCP to approve and / or modify the initial rehabilitation plan and / or dynamic adjustments specified in one or more updated rehabilitation plans.”)
As per claims 15-18, they are system claims which repeat the same limitations of claims 1-4 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Volosin and Daniel as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 1-4 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 15-18 are rejected for the same reasons given above for claims 1-4.
As per claim 19-20 it is an article of manufacture claim which repeats the same limitations of claims 1-2, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Volosin and Daniel as well as motivations to combine disclose the underlying process steps that constitute the method of claims 1-2, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 19-20 are rejected for the same reasons given above for claims 1-2.
Claim 8 is rejected under 35 U.S.C.103 as being unpatentable over Volosin et. al (hereinafter Volosin) (US20210035674A1) in view of Daniel (US20200005928A1) and in further view of Tabahi (US8821354B1)
As per claim 8, Volosin further teaches:
The computer-implemented method of claim 1, wherein the treatment apparatus comprises…[…]…, and the one or more sensors comprise an electrocardiogram sensor, a pulse oximeter, a blood pressure sensor, a respiration rate sensor, a spirometry sensor, or some combination thereof. ([0090 ] discloses, “FIG . 1 illustrates an example component - level view of the medical device controller 100 included in , for example , a wearable medical device such as a WCD . As shown in FIG . 1 , the medical device controller 100 can include a therapy delivery circuitry 102 , a data storage 104 , a network interface 106 , a user interface 108 , at least one battery 110 , a sensor interface 112 ( e.g. , to interface with both ECG sensing electrodes 122 and non - ECG physiological sensors 123 such as motion sensors , vibrational sensors , lung fluid sensors , infrared and near - infrared - based pulse oxygen sensor , blood pressure sensors , among others ) , a cardiac event detector 116 , and least one processor 118. In some examples , the patient monitoring medical device can include a medical device controller 100 that includes like components as those described above but does not include the therapy delivery circuitry 102 ( shown in dotted lines ).”)
However, Volosin and Daniel do not teach the underlined portion:
The computer-implemented method of claim 1, wherein the treatment apparatus comprises a cycling machine, and the one or more sensors comprise an electrocardiogram sensor, a pulse oximeter, a blood pressure sensor, a respiration rate sensor, a spirometry sensor, or some combination thereof.
However, Tabahi does teach:
The computer-implemented method of claim 1, wherein the treatment apparatus comprises a cycling machine, and the one or more sensors comprise an electrocardiogram sensor, a pulse oximeter, a blood pressure sensor, a respiration rate sensor, a spirometry sensor, or some combination thereof. (Abstract discloses, “The abdominal muscle and cycle workout machine features a modified workout bench having curved contours that enable an end user to lie in a recumbent position in order to perform cycling exercises while simultaneously conducting exercise that tar gets abdominal muscles.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Volosin’s teachings as previously cited and Daniel’s teachings as previously cited with Tabahi’s teachings as previously cited, the motivation being, Volosin teaches cycling as a treatment in the rehabilitation plans (see fig. 15) therefore, it would be obvious to someone of ordinary skill to utilize a cycling machine to execute cycling.
Prior Art Cited But Not Relied Upon
Tripp et. al -US11122832B2
A weight loss system is presented herein . In one embodiment , the weight loss system that accelerates weight loss in a subject eating a caloric restricted diet with a minimum daily protein intake of about 3 oz . and engaging in daily physical activity equivalent to about 5,000 steps per day is presented . The weight loss system can comprise an effective amount of : an antimicrobial component that includes berberine , cinnamon and curcumin ; fish oil ; a probiotic com ponent that includes Lactobacillus spp . , Bifidobacterium spp . , and Streptococcus spp .; an antioxidant , phytochemical component that includes apple extract , grape extract , green tea extract , and olive extract ; and a vitamin component that includes vitamins A , B , C , D , and E. The weight loss system can stimulate a weight loss of at least 3 % more than if not administered to the subject . Further presented herein is a method of facilitating weight loss in a subject and a method of improving the health of a subject participating in a weight loss program
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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