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 .
Receipt of Applicant’s Amendment filed November 12, 2025, is acknowledged.
Response to Amendment
Claims 1, 7-9, 12, 17, 20-22, and 24 have been amended. Claims 2-6, 13-16, 23, and 30 have been canceled. Claims 32-34 are new. Claims 1, 7-12, 17-22, 24-29, and 31-34 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 1, 7-12, 17-22, 24-29, and 31-34 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A response is provided below in bold where appropriate.
Applicant argues 35 USC §101 Rejection, starting pg. 9 of Remarks:
Rejections under 35 USC 4 101
Claim 1, 7-12, 17-22, 24-29, and 31 were rejected under 35 U.S.C. § 101 as directed to an abstract idea without significantly more. More specifically, the Office Action identifies claim 17 for analysis of the invention and applies said analysis to independent claims 1 and 24 respectively. Additionally, the Office Action alleges that claim 17, and therefore claims 1 and 24, fail Step 2A- Prong 1 of the Alice test and that they fall "within the 'Mental Processes' grouping of abstract ideas." Applicant respectfully disagrees and submits that at least independent claims 1, 17, and 24 are not a mental process and therefore not an abstract idea because they could not "practically be performed in the human mind."
Claims that have process elements or limitations that cannot be "practically performed in the human mind" do not recite a mental process. MPEP 2106. See SRIInt'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699 (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that "could not, as a practical matter, be performed entirely in a human's mind").
Here amended claims 1, 17, and 24 contain limitations that cannot be "practically performed in the human mind". First, the claims require, inter alia, "one or more sensors" to measure "a plurality of patient parameters." While previous technologies would have used some form of human interaction to determine patient parameters, no technology can allow the human mind to merely measure patient parameters. Furthermore, it would be impossible for the human mind to measure "a plurality of patient parameters" without the use of the one or more sensors. Second, the claims require the system to "automatically generate a modified measurement schedule." This implies the lack of human intervention and therefore the lack of a human mind. Furthermore, the human mind cannot "automatically generate a modified measurement schedule" that is based on multiple elements such as a first and a second plurality of patient parameters. Finally, the system and processes of claims 1, 17, and/or 24 do more than just generate information that could be done by a human. The systems utilize the information such as the first and second plurality of patient parameters to "automatically generate a modified measurement schedule" and then the system implements the schedule based on the patient parameters. Accordingly, Applicant submits that the systems and processes described in at least the independent claims 1, 17, and/or 24 cannot be practically performed by a human mind and therefore are eligible under 2A - Prong 1 of the Alice test.
From Applicant’s argument above…
>>”While previous technologies would have used some form of human interaction to determine patient parameters, no technology can allow the human mind to merely measure patient parameters. Furthermore, it would be impossible for the human mind to measure "a plurality of patient parameters" without the use of the one or more sensors.”<<
A person can mentally count heartbeats and pulse. A person can mentally compute that a person is turning pale.
Notwithstanding the aforementioned discussion, Applicant submits that the Office Action failed to properly apply 2A - Prong 2 of the Alice test. If the claim recites or possibly recites a judicial exception, then the Office must determine if the cited exception is "integrated into a practical application of that exception." The Office Action merely asserts that claim 17 does not have limitations that amount to significantly more than the judicial exception and generally claims that the use of sensors, processors, and other components are not enough to illustrate the integration into a practical application.
A practical application requires an additional element. No additional elements were found that make the abstract claims statutory.
Claim elements that are directed to an improvement to other technology or technical field are patent eligible. See MPEP @@ 2106.04(d)(I) and 2106.05(a) (citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016)). Enfish recognized that software that may be defined by "logical structures and processes" rather than physical features can add "non-abstract improvements to computer technology" and that such improvements are patent eligible. Furthermore, the appeals panel review decision for Desjardins illustrated that "Examiners and panels should not evaluate claims at such a high level of generality" and should more carefully consider the decision in Enfish. Exparte Desjardins, No. 2024-000567, at 1 (P.T.A.B. Apps. Rev. Sept. 26, 2025).
Enfish improved computer technology. Desjardins improved artificial intelligence. Applicant’s claims do not appear to be improving computer or AI technology.
Here, the process of identifying events based on a variety of patient parameters to automatically adjust the patient measurement schedule illustrates an improvement in medical technology above that which is currently available This is akin to the improvements in Enfish. This is especially true when considering the modification event can be determined by the patient location. Therefore, when considering the claim as a whole it is difficult to assert that the claim is a mere abstract idea, but rather reflects "non- abstract improvements" to current patient monitoring technology and is therefore patent eligible. As such, Applicant respectfully requests the rejection be withdrawn.
Respectfully the claims are using generic processors and generic sensors. There is no improvement to processor or sensor technology as claimed. Using such technologies at a high level is not enough to make abstract claims statutory.
Claims 7-12, 18-22, and 25-34 are likewise rejected under 35 U.S.C. § 101 as being directed to an abstract idea. However, those claims depend directly or indirectly from independent claims 1, 17, and/or 24, which are allowable in light of the discussion above. Therefore, claims 7-12, 18-22, and 25-34 are allowable based, at least, on their dependence on an allowable base claim and Applicant respectfully requests the rejection be withdrawn.
Based on the above response, the rejection is respectfully modified for the claim amendments but maintained.
Applicant argues 35 USC §103 Rejection, starting pg. 11 of Remarks:
Applicant’s Amendments have resulted in new prior art, rendering the arguments moot.
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, 7-12, 17-22, 24-29, and 31-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 7-12, 17-22, 24-29, and 31-34 are directed to a system or method, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 17 as the claim that represents the claimed invention for analysis and is similar to system claims 1 and 27.
Claim 17 recites the limitations of:
A method for automatically generating customized measurement schedules by a medical device, the method comprising:
executing a predefined measurement schedule using one or more processors programmed to activate the one or more sensors in order to measure a first plurality of patient parameters;
receiving the first plurality of measured patient parameters from the one or more sensors;
receiving a second plurality of patient parameters, where the second plurality of patient parameters includes at least a patient location;
determining if a modification event has been detected based on at least one of the first plurality of measured patient parameters and the second plurality of patient parameters;
automatically generating a modified measurement schedule in response to the modification event;
implementing the modified measurement schedule; and
providing an indication that modified measurement schedule has been generated and implemented.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in non-bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. A person can execute a predefined schedule and measure a plurality of patient parameters (e.g., take a person’s pulse using their fingers as sensors and listen to heartbeats), receive a first plurality of patient parameters, receive (write down) a second plurality of patient parameters that includes a patient location (person at xyz address), determine any modifications to first and second parameters, generate a modified schedule in response to detecting an event (pulse is high, heart beat is high), implement the modified schedule (check blood pressure now and heart rate now), provide an indication that a modified measurement has been generated and implemented (e.g., write down new blood pressure and heart rate). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 24 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claims are managing personal behavior by executing a schedule and measuring patient parameters, receiving first and second patient parameters, determining a modification to the patient parameters, generating a modified measurement schedule based on patient parameters, implementing the modified schedule and providing an indication the schedule has been implemented, the claims are abstract under certain methods of organizing human activity as they are managing personal behavior (e.g., receiving information from a patient, generating a modified schedule based on the patient information, and implementing the modified schedule on the patient is managing personal behavior based on teaching). Providing an indication that a modified schedule has been generated and implemented is teaching, also managing personal behavior.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: medical device, sensors, processors, (Claim 1); medical device, processors, sensors (Claim 17); monitor mount, medical device, sensors, processors (Claim 24). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The sensors are generic sensors recited at a high level of generality. The processor programmed to activate one or more sensors is recited at a high level of generality. The monitor mount is taught and claimed at a high level of generality and is insignificant to the claimed invention itself. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 17, and 24 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware 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. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving and provide (transmitting) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 17, and 24 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 7-12, 18-22, 25-29, and 31-34 further define the abstract idea that is present in their respective independent claims 1, 17, and 24 and thus correspond to Mental Processes and Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The claims just further limit abstract limitations. Claim 10 recites pager, station and medical device as generic device at a high level of generality. Therefore, the claims 7-12, 18-22, 25-29, and 31-34 are directed to an abstract idea. Thus, the claims 1, 7-12, 17-22, 24-29, and 31-34 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 9-12 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2022/0284994 to Ellis et al. in view of Pub. No. US 2020/0373007 to Hall et al.
Regarding claim 1
A medical device configured to automatically generate customized measurement schedules, the medical device comprising:
one or more sensors attached to a patient programmed to measure a
first plurality of patient parameters of the patient according to a predefined measurement schedule;
Ellis et al. teaches:
Sensor devices…
“The system also supports collection of different types of data from different sensor devices (both integrated into the user device and separately connected), as well as direct user input (e.g. via symptom questionnaires), using configurable assignment schedules. The system can simultaneously manage multiple study configurations across multiple studies/disease areas, with information sent in near real-time to the central monitoring system where it is tracked for compliance, checked for quality and analysed.” [0026]
Smart watches (sensors attached to a patient) to monitor health status of users (measure first plurality of patient parameters)…
“Modern personal computer/communication devices such as smartphones, smart watches and personal fitness monitors include various sensors useful for monitoring the health status of users. For example, accelerometers can be used to monitor physical exercise, and infrared sensors can be used to measure heart rate. Many smartphone manufacturers provide health and fitness monitoring applications and services using data collected from such sensors. More specialised medical sensor devices are also available that can be connected to smartphones via Bluetooth, Wi-Fi or similar, such as spirometers and thermometers, and that can use a smartphone to display and process the sensor data produced by the devices.” [0002]
Determined schedule (therefore, predefined measurement schedule)…
“After scheduling, the data collection application performs the data collection activities in accordance with the determined schedule. This involves, for each scheduled data collection activity, identifying (step 914) based on the schedule data a target time at which the given data collection activity is to be performed (this could have been determined at the scheduling stage, but for any dynamic event-based timestamps this occurs once the timestamp has been resolved, e.g. in response to the triggering event for the timestamp being detected), and identifying the relevant data source from which data is to be acquired (e.g. a sensor for a sensor-based data collection activity).” [0204]
one or more processors;
Fig. 11, ref. 1102 is example of processor…
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“FIG. 11 illustrates elements of a computer system for implementing described processes and techniques. FIG. 11 shows a single representative user device 104, for example a smartphone (though in practice the system will include many such devices), and a data collection server 1100 for implementing various functions of the study management system.” [0284]
wherein the one or more processors are programmed to:
receive the first plurality of measured patient parameters from the one or
more sensors based on the predefined measurement schedule;
“Different internal sensors 302 and external sensor devices 108, and different processing modules 316, may be selected as a function of different study requirements. For example, in a study of sleep, an actigraphy device may be used to collect movement data overnight. This movement data is then processed by suitable algorithms (e.g. publicly available and validated algorithms may be used) which derive properties of sleep such as total sleep time, number of times awake etc. In another example, the external sensor device 108 may be a mobile spirometer. The patient blows into the mobile spirometer two or more times and the mobile spirometer records the flow of the patient's breath through the device. The flow data is then processed to calculate lung function measurements. In other examples, the external sensor device 108 may be a single lead ECG (electrocardiogram) sensor, which is a sensor applied via a patch which the patient sticks to their chest near the heart. In this example, the electrical signals of the heartbeat are recorded and an algorithm processes the data to derive the number of heartbeats per minute and other supported measures.” [0096]
Configurable assignment (predefined) schedules…
“The system also supports collection of different types of data from different sensor devices (both integrated into the user device and separately connected), as well as direct user input (e.g. via symptom questionnaires), using configurable assignment schedules. The system can simultaneously manage multiple study configurations across multiple studies/disease areas, with information sent in near real-time to the central monitoring system where it is tracked for compliance, checked for quality and analysed.” [0026]
Another example of defines (predefined) schedule…
“In step 214, the researcher then defines the assignment schedule for the selected assignments, which can be daily, weekly, monthly or otherwise configured according to study requirements. For example, in the study of sleep a patient may be required to complete a daily sleep diary.” [0040]
Update in real time a schedule based on sensor readings…
“Through the use of event-based timestamps and multiple condition types including conditions based on user input, sensor readings, and other factors, this system is able to dynamically create and update a schedule of assignments in real-time to ensure each patient's compliance with the study protocol applicable for that patient.” [0246]
receive a second plurality of patient parameters wherein the second plurality
of patient parameters includes at least a patient location;
User (patient) health data (medical condition), therefore second patient parameter…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Current location and time zone (parameter of location) and relies on (receives) local notifications (also patient location parameter) on the smartphone or peripheral device…
“The system is able to send time-critical text/email notifications to an end user's device. When time zone data is unreliable, it becomes challenging to guarantee that a notification can be sent at a precise time in relation to the user's current location and time zone. The system compiles time zone data from available devices and peripherals in order to properly schedule notifications and assignments. The system is also able to schedule time-critical sensor data collection windows via this approach. Where possible, this system relies on local notifications on the smartphone, generated by either the schedule, a mobile assignment, or by a peripheral device.” [0283] Inherent with time zone is a patient location associated with the time zone.
See Location below.
automatically generate a modified measurement schedule automatically in
response to a modification event detected from at least one of the first plurality of
received measured patient parameters, and the second plurality of patient
parameters;
Dynamically (automatically) schedule assignments where schedule assignments are dynamic (modified)…
“In addition to instructing the mobile device on how to run assignments via task definitions, the configuration also specifies scheduling information which is interpreted by a scheduling engine of the application to dynamically schedule assignments in an event-based manner, where sensor readings, user-specific medical conditions, and external events can change the flow of the assignments…”
Dynamic assignment schedule (determining a modification event has been detected)…
“The proposed solution allows efficient and configurable management of how and when assignments run whilst also enabling the generation of an audit trail. Meeting the complex logical demands in a low-bandwidth environment is achieved by creating a virtual and dynamic assignment schedule. The schedule is specified by schedule data in the configuration package and is parsed by a schedule parser to generate any configurable set of assignments and associated data collection activities.” [0199]
Example of actual target time based on event detected, which is different from initially determined schedule…
“In step 912, the schedule data is parsed and a schedule of data collection activities is determined. In step 913, the mobile application is configured based on the specified set of data collection activities and schedule data. Note that target times for data collection activities may be fully specified in absolute terms or in a form that can be resolved at the time scheduling is originally performed. However, as described later, the system also supports dynamically resolved schedule timings, whereby target times for data collection activities (and other actions such as notifications or data uploads) are expressed relative to the occurrence of specific events, with the actual target time for an action only determined once the event has been detected. Thus, for such data collection activities, the initially generated schedule includes unresolved, dynamically determined target times (in the form of event-based timestamps as discussed in more detail below) that will be resolved to concrete timings at some later point.” [0203]
Example of resolved time used to initiate (automatically generate) a scheduled data collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
automatically implement the modified measurement schedule; and
One example of update configuration (therefore, automatically implement) schedule…
“If, during the health study, updated configuration data is received from the study management system (e.g. following a change in study protocol as discussed above), then the mobile application updates the locally stored configuration and reconfigures the application accordingly (as per steps 911-913). The reconfiguration may modify the data collection activities performed, the associated schedule etc. Data collection then continues in accordance with the updated configuration.” [0208]
Resolve target time used (automatically implement) the scheduled data (modified scheduled) collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
provide an indication that modified measurement schedule has been
generated and implemented.
Push and SMS (providing an indication) of scheduling and taking sensor readings…
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Location
Ellis et al. teaches sensors and location for a patient. They also teach various health sensor devices to collect health data. They do not specifically teach details of patient location.
Hall et al. also in the business of sensors and patient location teach:
Physiological monitoring device to generate location information…
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
Using GPS for location information…
“As shown in FIG. 2, whether the physiological monitoring device 7 is docked in a monitor mount 10 or separated therefrom, the physiological monitoring device 7 is maintained in proximity with the patient 1. The physiological monitoring device 7 includes a global positioning system (GPS) or other location data system 26 that can be connected to the communication interface circuitry of microcontroller 3b so that the physiological monitoring device 7 can transmit to the clinician, caregiver, or other devices the location of the patient 1 corresponding to the location of the physiological monitoring device 7 continuously and/or at predetermined intervals. The location of the patient 1 may be transmitted at all times including during transport. Additionally, the current location of the patient 1 can be used by the microcontroller 3b to determine an estimated time of arrival of the patient 1 at, for example, a predetermined destination. The microcontroller 3b may also use transport information, including location information acquired at different sample times (e.g., including a current sample time and one or more previous sample times) to determine whether the patient 1 is stationary or in motion.” [0061]
Location information cross-referenced to other patient information….
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ellis et al. the ability to determine patient location as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the benefits of using location information for patients and Ellis et al. benefits as they also determine location using time zones and this provides more specific location information.
The combined references teach location and using GPS. They do not explicitly teach smartphones with GPS. However, one of ordinary skill in the art would recognize that smart phones have GPS for location purposes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that smart phones could provide location information. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of smart phones and would provide predictable results.
Regarding claim 9
The medical device of claim 1, wherein the first plurality of patient parameters of the patient includes at least one of non-invasive blood pressure (NIBP), temperature, heart rate, an electrocardiogram (ECG), non-invasive peripheral oxygen saturation (SpO2), end tidal carbon dioxide (etCO2), apnea of the patient, neuromuscular transmission (NMT), and cardiac output (CO).
Ellis et al. teaches:
Blood-pressure sensors…
“Sensor-based data collection activities use one or more device sensors and/or external sensors connected to the user device (or connected to external data collection platforms) to obtain health-related data in the form of sensor data. Sensors can include general-purpose sensors (e.g. accelerometers built into a smartphone) and/or special-purpose medical sensors (e.g. spirometers, blood pressure sensors etc).” [0076]
ECG…
“Different internal sensors 302 and external sensor devices 108, and different processing modules 316, may be selected as a function of different study requirements. For example, in a study of sleep, an actigraphy device may be used to collect movement data overnight. This movement data is then processed by suitable algorithms (e.g. publicly available and validated algorithms may be used) which derive properties of sleep such as total sleep time, number of times awake etc. In another example, the external sensor device 108 may be a mobile spirometer. The patient blows into the mobile spirometer two or more times and the mobile spirometer records the flow of the patient's breath through the device. The flow data is then processed to calculate lung function measurements. In other examples, the external sensor device 108 may be a single lead ECG (electrocardiogram) sensor, which is a sensor applied via a patch which the patient sticks to their chest near the heart. In this example, the electrical signals of the heartbeat are recorded and an algorithm processes the data to derive the number of heartbeats per minute and other supported measures.” [0096]
Regarding claim 10
The medical device of claim 1, wherein providing a notification that a modified measurement schedule has been automatically generated and implemented includes providing a notification by at least one of an electronic mail, a text message, a page message sent to a pager, a notification being transmitted to a central monitoring station, and an alert displayed on the medical device.
Ellis et al. teaches:
Push and SMS notification of scheduling and taking sensor readings based on resolved (modified) launch and due times (schedule) …
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Regarding claim 11
The medical device of claim 10, wherein the notification includes at least one of a visual and an audible notification indicating generation and implementation of the modified measurement schedule.
Ellis et al. teaches:
Push and SMS notification (visual notification) …
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Regarding claim 12
The medical device of claim 1, wherein the one or more processors stores in a memory a time, date, and the modification event that caused the generation and implementation of the automatically generated modified measurement schedule.
Ellis et al. teaches:
Smartphones and watches, which would include data and time…
“Modern personal computer/communication devices such as smartphones, smart watches and personal fitness monitors include various sensors useful for monitoring the health status of users. For example, accelerometers can be used to monitor physical exercise, and infrared sensors can be used to measure heart rate. Many smartphone manufacturers provide health and fitness monitoring applications and services using data collected from such sensors. More specialised medical sensor devices are also available that can be connected to smartphones via Bluetooth, Wi-Fi or similar, such as spirometers and thermometers, and that can use a smartphone to display and process the sensor data produced by the devices.” [0002]
Example of timestamp (time and date) based on activity (event)…
“After scheduling, the data collection application performs the data collection activities in accordance with the determined schedule. This involves, for each scheduled data collection activity, identifying (step 914) based on the schedule data a target time at which the given data collection activity is to be performed (this could have been determined at the scheduling stage, but for any dynamic event-based timestamps this occurs once the timestamp has been resolved, e.g. in response to the triggering event for the timestamp being detected), and identifying the relevant data source from which data is to be acquired (e.g. a sensor for a sensor-based data collection activity).” [0204]
Data stored in local database (medical device)…
“The health-related data generated by the data collection activities is collected and temporarily stored in a local database at the user device by the application in step 917. In step 918 the obtained health-related data is transmitted as result data to the central study management system. As described in more detail elsewhere, this may involve transmitting data from multiple data collection activities in a batch transmission and/or performing transmission at a scheduled time (which may be scheduled based on dynamic event-based timestamps) or based on network connectivity. Alternatively, result data from a data collection activity may be transmitted as soon as it is available, in near real-time. Optionally, some client-side pre-processing may be applied to the health-related data obtained by the data collection activities to obtain the result data that is transmitted.” [0206]
Regarding claim 32
The medical system of claim 1, wherein the second plurality of patient parameters further includes at least one of personal information of the patient, and a medical condition of the patient.
Ellis et al. teaches:
User (patient) health data (medical condition)…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Using data such as weight…
“Data input activities are based on direct user input of health-related data values, such as patient questionnaires and health diaries. In such activities a user is prompted to supply specific information, which could include subjective symptom assessments as well as manual input of measurements obtained using non-connected devices (e.g. a weight measurement obtained using conventional scales). Data input activities may implement capture of health-related data in the form of electronic patient reported outcomes (ePRO), clinician-reported outcomes (ClinRO) or equivalent. A data input activity may be implemented as an input form displayed on a display of the user device, with a predefined set of input fields to be completed by a user via data input at the user device (e.g. using touch screen and onscreen keyboard/handwriting recognition, voice recognition etc.).” [0077]
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (6) above in view of Pub. No. US 2008/0281168 to Gibson et al.
Regarding claim 7
The medical device of claim 1, wherein the modification event is that at least one of the plurality of measured patient parameters is within a certain percentage of an alarm limit.
Ellis et al. teaches:
Trigger event (alarm) and meeting a threshold…
“The trigger event is preferably a specific named event selected from an extensible set of predefined events. The system supports a range of event types and sources, including, for example: sensor events corresponding to a sensor measurement; a particular sensor event could indicate any measurement obtained from a particular sensor, or could indicate a measurement meeting a particular criterion, such as a predetermined sensor value threshold;… “ [0213] – [0214]
The combined references teach modification event. They do not teach percentage.
Gibson et al. also in the business of events teaches:
Percentage change…
“In addition to the above features, the herein described monitoring device can be further configured such that the user can perform alarm management on the monitoring device 20 by permitting the user to actuate a feature provided on the user interface 92 that creates a predetermined percentage change to the alarm limits for a single parameter each time the SELECT button 96 is depressed at the time of an existing alarm. The initialization and initial percentage settings for each of the alarm parameter settings is performed according to this specific embodiment as part of the configuration of the monitoring device 20 prior to use of the monitoring device 20 through the PC 192 using the configuration file to override factory configuration settings, the new settings being stored by the CPU 174.” [0186]
NIBP, SpO2 limits changed by a percent….
“… For example, upper and lower alarm limits for HR/PR can initially be set to alarm at an upper rate of 90 and a lower rate of 60. Using the latter feature, each time the SELECT button 96 is actuated for the above feature in the control menu, the alarm limits can be incremented by a predetermined percentage (e.g., 5 percent, 10 percent, or other). For example, if a five percent change were configured for the herein described monitoring device 20, the alarm limits would change to 94 (upper)/57 (lower) the first time the SELECT button 96 is depressed, 99 (upper)/54 (lower) the second time the button is depressed, and so forth. Similarly, NIBP (systolic pressure, diastolic pressure and mean pressure), SpO.sub.2 and respiration rate limits can be similarly adjusted wherein the amount from factory (default) preset value alarm limit values can be adjusted, depending on the patient mode, for individual parameters as part of the pre-configuration routine using the PC 192. A tabular listing 950 is shown in FIG. 65 for appropriate percentage changes to the alarm limits according to one example.” [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use percentages as taught by Gibson et al. 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. Further motivation is provided by Gibson et al. who teaches the advantages of using percentages for monitoring change.
Regarding claim 8
The medical device of claim 1, wherein the modification event is based on one or more of the first plurality measured patient parameters being within a predefined percentage of an alarm limit.
Ellis et al. teaches:
Trigger event (alarm) and meeting a threshold…
“The trigger event is preferably a specific named event selected from an extensible set of predefined events. The system supports a range of event types and sources, including, for example: sensor events corresponding to a sensor measurement; a particular sensor event could indicate any measurement obtained from a particular sensor, or could indicate a measurement meeting a particular criterion, such as a predetermined sensor value threshold;… “ [0213] – [0214]
The combined references teach vital sign devices and thresholds. They also teach change and modification and alarm/warning. They do not teach percentage with alarm.
Gibson et al. also in the business of vital signs teaches:
Percentage change with alarm limit…
“In addition to the above features, the herein described monitoring device can be further configured such that the user can perform alarm management on the monitoring device 20 by permitting the user to actuate a feature provided on the user interface 92 that creates a predetermined percentage change to the alarm limits for a single parameter each time the SELECT button 96 is depressed at the time of an existing alarm. The initialization and initial percentage settings for each of the alarm parameter settings is performed according to this specific embodiment as part of the configuration of the monitoring device 20 prior to use of the monitoring device 20 through the PC 192 using the configuration file to override factory configuration settings, the new settings being stored by the CPU 174.” [0186]
Percent change with alarm limits and NIBP, SpO2 limits changed by a percent….
“… For example, upper and lower alarm limits for HR/PR can initially be set to alarm at an upper rate of 90 and a lower rate of 60. Using the latter feature, each time the SELECT button 96 is actuated for the above feature in the control menu, the alarm limits can be incremented by a predetermined percentage (e.g., 5 percent, 10 percent, or other). For example, if a five percent change were configured for the herein described monitoring device 20, the alarm limits would change to 94 (upper)/57 (lower) the first time the SELECT button 96 is depressed, 99 (upper)/54 (lower) the second time the button is depressed, and so forth. Similarly, NIBP (systolic pressure, diastolic pressure and mean pressure), SpO.sub.2 and respiration rate limits can be similarly adjusted wherein the amount from factory (default) preset value alarm limit values can be adjusted, depending on the patient mode, for individual parameters as part of the pre-configuration routine using the PC 192. A tabular listing 950 is shown in FIG. 65 for appropriate percentage changes to the alarm limits according to one example.” [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use percentages as taught by Gibson et al. 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. Further motivation is provided by Gibson et al. who teaches the advantages of using percentages for monitoring change and using alarm limits to notify changes are happening.
Claims 17-19, 22, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2022/0284994 to Ellis et al. in view of Pub. No. US 2020/0373007 to Hall et al.
Regarding claim 17
A method for automatically generating customized measurement schedules by a medical device, the method comprising:
executing a predefined measurement schedule using one or more processors programmed to activate the one or more sensors in order to measure a first plurality of patient parameters;
Ellis et al. teaches:
“The system also supports collection of different types of data from different sensor devices (both integrated into the user device and separately connected), as well as direct user input (e.g. via symptom questionnaires), using configurable assignment schedules. The system can simultaneously manage multiple study configurations across multiple studies/disease areas, with information sent in near real-time to the central monitoring system where it is tracked for compliance, checked for quality and analysed.” [0026]
“After scheduling, the data collection application performs the data collection activities in accordance with the determined schedule. This involves, for each scheduled data collection activity, identifying (step 914) based on the schedule data a target time at which the given data collection activity is to be performed (this could have been determined at the scheduling stage, but for any dynamic event-based timestamps this occurs once the timestamp has been resolved, e.g. in response to the triggering event for the timestamp being detected), and identifying the relevant data source from which data is to be acquired (e.g. a sensor for a sensor-based data collection activity).” [0204]
Data collection activities are initiated (activate) and sensor data is obtained…
“In step 915, the data collection activities are initiated at the relevant target times, and the data collection activities are then performed in step 916, based on the specified data collection modes, to acquire health-related data. For example, for a sensor-based data collection activity, sensor data is obtained from the identified sensor during performance of the data collection activity. This step may involve providing instructions to a user to perform an action (e.g. complete a walk test, use an external sensor device such as a spirometer etc.), and the sensor data obtained may include a single reading from a single sensor (e.g. a temperature measurement), multiple readings from a single sensor (e.g. multiple accelerometer readings over a specified time period), or one or more readings from each of a set of multiple different sensors. For direct data input activities (e.g. a symptom questionnaire), the application displays an input form as specified in the configuration and obtains user input for a set of specified health-related data values. For interactive assignments, the assignment is run by the mobile application and user interactions are recorded, and health-related data is derived from the detected user interactions (e.g. by measuring response times, response accuracy, or other suitable characteristics of the interactions as described above).” [0205]
Another example of triggering (activate) sensor readings…
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Fig. 11, ref. 1102 is example of processor…
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“FIG. 11 illustrates elements of a computer system for implementing described processes and techniques. FIG. 11 shows a single representative user device 104, for example a smartphone (though in practice the system will include many such devices), and a data collection server 1100 for implementing various functions of the study management system.” [0284]
receiving the first plurality of measured patient parameters from the one or more sensors;
Reading (receiving) multiple readings (plurality of parameters) from one or more sensors…
“In step 915, the data collection activities are initiated at the relevant target times, and the data collection activities are then performed in step 916, based on the specified data collection modes, to acquire health-related data. For example, for a sensor-based data collection activity, sensor data is obtained from the identified sensor during performance of the data collection activity. This step may involve providing instructions to a user to perform an action (e.g. complete a walk test, use an external sensor device such as a spirometer etc.), and the sensor data obtained may include a single reading from a single sensor (e.g. a temperature measurement), multiple readings from a single sensor (e.g. multiple accelerometer readings over a specified time period), or one or more readings from each of a set of multiple different sensors. For direct data input activities (e.g. a symptom questionnaire), the application displays an input form as specified in the configuration and obtains user input for a set of specified health-related data values. For interactive assignments, the assignment is run by the mobile application and user interactions are recorded, and health-related data is derived from the detected user interactions (e.g. by measuring response times, response accuracy, or other suitable characteristics of the interactions as described above).” [0205]
receiving a second plurality of patient parameters, where the second plurality of patient parameters includes at least a patient location;
User (patient) health data (medical condition), therefore second patient parameter…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Current location and time zone (parameter of location) and relies on (receives) local notifications (also patient location parameter) on the smartphone or peripheral device…
“The system is able to send time-critical text/email notifications to an end user's device. When time zone data is unreliable, it becomes challenging to guarantee that a notification can be sent at a precise time in relation to the user's current location and time zone. The system compiles time zone data from available devices and peripherals in order to properly schedule notifications and assignments. The system is also able to schedule time-critical sensor data collection windows via this approach. Where possible, this system relies on local notifications on the smartphone, generated by either the schedule, a mobile assignment, or by a peripheral device.” [0283] Inherent with time zone is a patient location associated with the time zone.
See Location below.
determining if a modification event has been detected based on at least one of the first plurality of measured patient parameters and the second plurality of patient parameters;
“In a concrete example, the application could be configured to study gait and balance for one disease in conjunction with a wrist-worn accelerometer linked to a smartphone (or other user device), where the application is configured with daily assignments which collect point-in-time data from a smartphone accelerometer and the linked wrist-worn accelerometer collects continuous data over several days—including the times when the patient completes assignments on the smartphone. The data from the smartphone and wrist-worn device is processed using a gait and balance algorithm that calculates distance walked and walking speed. At the same time, the same mobile application and the same device may be used to study sleep with the application configured with a sleep diary and the data recorded from the wrist-worn device used to determine how long the patient slept and how many times they woke up at night. In the second example, the wrist-worn device data is processed with a sleep algorithm.” [0028]
Dynamic assignment schedule (determining a modification event has been detected)…
“The proposed solution allows efficient and configurable management of how and when assignments run whilst also enabling the generation of an audit trail. Meeting the complex logical demands in a low-bandwidth environment is achieved by creating a virtual and dynamic assignment schedule. The schedule is specified by schedule data in the configuration package and is parsed by a schedule parser to generate any configurable set of assignments and associated data collection activities.” [0199]
Example of actual target time based on event detected, which is different from initially determined schedule…
“In step 912, the schedule data is parsed and a schedule of data collection activities is determined. In step 913, the mobile application is configured based on the specified set of data collection activities and schedule data. Note that target times for data collection activities may be fully specified in absolute terms or in a form that can be resolved at the time scheduling is originally performed. However, as described later, the system also supports dynamically resolved schedule timings, whereby target times for data collection activities (and other actions such as notifications or data uploads) are expressed relative to the occurrence of specific events, with the actual target time for an action only determined once the event has been detected. Thus, for such data collection activities, the initially generated schedule includes unresolved, dynamically determined target times (in the form of event-based timestamps as discussed in more detail below) that will be resolved to concrete timings at some later point.” [0203]
Time zone (patient location) change…
“The system may provide functionality to enable appropriate time zone handling throughout the application. In an embodiment, when a user is signed up, they provide a time zone that gets stored in a database. This time zone is subject to change day to day, however. In a heavily regulated environment, it is useful for the system to be able to automatically apply the proper time zone of a subject throughout their lifecycle in the system, but a constant feed of time zone data is generally not available from user devices.” [0281]
See Location below.
automatically generating a modified measurement schedule in response to the modification event;
Example of resolved time used to initiate (automatically generate) a scheduled data collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
Another example of defines (predefined) schedule…
“In step 214, the researcher then defines the assignment schedule for the selected assignments, which can be daily, weekly, monthly or otherwise configured according to study requirements. For example, in the study of sleep a patient may be required to complete a daily sleep diary.” [0040]
Update in real time a schedule based on sensor readings…
“Through the use of event-based timestamps and multiple condition types including conditions based on user input, sensor readings, and other factors, this system is able to dynamically create and update a schedule of assignments in real-time to ensure each patient's compliance with the study protocol applicable for that patient.” [0246]
implementing the modified measurement schedule; and
Initiate (implementing) the scheduled data (modified scheduled) collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
providing an indication that modified measurement schedule has been generated and implemented.
Push and SMS (providing an indication) of scheduling and taking sensor readings…
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Location
Ellis et al. teaches sensors and location for a patient. They also teach various health sensor devices to collect health data. They do not specifically teach details of patient location.
Hall et al. also in the business of sensors and patient location teach:
Physiological monitoring device to generate location information…
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
Using GPS for location information…
“As shown in FIG. 2, whether the physiological monitoring device 7 is docked in a monitor mount 10 or separated therefrom, the physiological monitoring device 7 is maintained in proximity with the patient 1. The physiological monitoring device 7 includes a global positioning system (GPS) or other location data system 26 that can be connected to the communication interface circuitry of microcontroller 3b so that the physiological monitoring device 7 can transmit to the clinician, caregiver, or other devices the location of the patient 1 corresponding to the location of the physiological monitoring device 7 continuously and/or at predetermined intervals. The location of the patient 1 may be transmitted at all times including during transport. Additionally, the current location of the patient 1 can be used by the microcontroller 3b to determine an estimated time of arrival of the patient 1 at, for example, a predetermined destination. The microcontroller 3b may also use transport information, including location information acquired at different sample times (e.g., including a current sample time and one or more previous sample times) to determine whether the patient 1 is stationary or in motion.” [0061]
Location information cross-referenced to other patient information….
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ellis et al. the ability to determine patient location as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the benefits of using location information for patients and Ellis et al. benefits as they also determine location using time zones and this provides more specific location information.
The combined references teach location and using GPS. They do not explicitly teach smartphones with GPS. However, one of ordinary skill in the art would recognize that smart phones have GPS for location purposes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that smart phones could provide location information. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of smart phones and would provide predictable results.
Regarding claim 18
The method of claim 17, wherein the modification event includes at least one of a change in a medical condition of the patient, a change in a location of the patient, a change in personal information of the patient, and a change in physiological information of the patient.
Ellis et al. teaches:
Patient not completing activity (change in medical condition) and trigger rescheduling…
“In an example, a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes—a common test in COPD (chronic obstructive pulmonary disease) and heart disease to assess patient functional status. Quality checks on the received raw data file may show that only 1 minute's worth of data was received. This could be due to the patient not completing the activity, or due to a technical issue e.g. the wrist-worn accelerometer was faulty or low on power and only captured intermittent segments during the 6 minutes of walking. As a further example, a signal analysis may reveal that a heart rate sensor or other sensor was faulty or not used or worn correctly, resulting in an intermittent or low-quality signal. In such cases, an assignment is flagged as “incomplete” because it was not completed as required and/or required data is missing or of insufficient quality. In some implementations, incomplete assignments are logged as such in the central system and notifications issued to clinical personnel to follow up with the patient. In other implementations, an incomplete assignment may trigger automated rescheduling of the assignment for the patient.” [0060]
User changing time zone (change in location)…
“… Preferred embodiments therefore use an alternative format for defining timings, to allow generation of dynamic dates such that if the user is changing time zone or changing their daily routine, the client contains an instruction set that describes how to handle these changing behaviours.” [0198] Inherent with user changing time zone is changing location.
Regarding claim 19
The method of claim 18, wherein the change in a location of the patient includes at least one of the patient moving to an emergency room, an operating room, an intensive care unit, a neonatal intensive care unit, a post anesthesia care unit, a recovery room, and a labor and delivery room.
The combined references teach location. They do not teach specific location.
Hall et al. also in the business of location teaches:
Determine a location of a patient in a hospital…
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
Where to locate patient and ETA (move) patient to operating room…
“Transport location information and ETA can support patient care facility emergency code situations in which a crisis team is required to intervene by communicating the patient's location and ETA. It is contemplated by the disclosure of the present application that an accelerometer, a GPS, and/or other location technology may provide intelligence about when a patient transport is actually occurring and can be used to refine the detection of a transport activity. The GPS or location technology can be used along with a patient care facility layout or a patient care facility map to track the location of a patient in an event of a patient care facility emergency code, which could be used to communicate information to a crises team (e.g., where to locate the patient and the ETA of the patient to a patient care area, such as an operating room).” [0116]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine patient location such as operating room as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the benefits of using location information for patients in emergency situations and the combined references benefit as they are also monitoring patients.
Regarding claim 22
The method of claim 17, wherein the plurality of measured patient parameters of the patient includes include at least one of non-invasive blood pressure (NIBP), temperature, heart rate, an electrocardiogram (ECG), non-invasive peripheral oxygen saturation (SpO2), end tidal carbon dioxide (etCO2), apnea of the patient, neuromuscular transmission (NMT), and cardiac output (CO).
Ellis et al. teaches:
Blood-pressure sensors…
“Sensor-based data collection activities use one or more device sensors and/or external sensors connected to the user device (or connected to external data collection platforms) to obtain health-related data in the form of sensor data. Sensors can include general-purpose sensors (e.g. accelerometers built into a smartphone) and/or special-purpose medical sensors (e.g. spirometers, blood pressure sensors etc).” [0076]
ECG…
“Different internal sensors 302 and external sensor devices 108, and different processing modules 316, may be selected as a function of different study requirements. For example, in a study of sleep, an actigraphy device may be used to collect movement data overnight. This movement data is then processed by suitable algorithms (e.g. publicly available and validated algorithms may be used) which derive properties of sleep such as total sleep time, number of times awake etc. In another example, the external sensor device 108 may be a mobile spirometer. The patient blows into the mobile spirometer two or more times and the mobile spirometer records the flow of the patient's breath through the device. The flow data is then processed to calculate lung function measurements. In other examples, the external sensor device 108 may be a single lead ECG (electrocardiogram) sensor, which is a sensor applied via a patch which the patient sticks to their chest near the heart. In this example, the electrical signals of the heartbeat are recorded and an algorithm processes the data to derive the number of heartbeats per minute and other supported measures.” [0096]
Regarding claim 33
The medical system of claim 17, wherein the second plurality of patient parameters includes at least one of personal information of the patient, and a medical condition of the patient.
Ellis et al. teaches:
User (patient) health data (medical condition)…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Using data such as weight…
“Data input activities are based on direct user input of health-related data values, such as patient questionnaires and health diaries. In such activities a user is prompted to supply specific information, which could include subjective symptom assessments as well as manual input of measurements obtained using non-connected devices (e.g. a weight measurement obtained using conventional scales). Data input activities may implement capture of health-related data in the form of electronic patient reported outcomes (ePRO), clinician-reported outcomes (ClinRO) or equivalent. A data input activity may be implemented as an input form displayed on a display of the user device, with a predefined set of input fields to be completed by a user via data input at the user device (e.g. using touch screen and onscreen keyboard/handwriting recognition, voice recognition etc.).” [0077]
Claims 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (8) above in view of Pub. No. US 2008/0281168 to Gibson et al.
Regarding claim 20
The method of claim 17, wherein the modification event is based on a percentage change in one or more of the first plurality of measured patient parameters.
Ellis et al. teaches:
Trigger event (alarm) and meeting a threshold…
“The trigger event is preferably a specific named event selected from an extensible set of predefined events. The system supports a range of event types and sources, including, for example: sensor events corresponding to a sensor measurement; a particular sensor event could indicate any measurement obtained from a particular sensor, or could indicate a measurement meeting a particular criterion, such as a predetermined sensor value threshold;… “ [0213] – [0214]
The combined references teach modification event. They do not teach percentage.
Gibson et al. also in the business of events teaches:
Percentage change…
“In addition to the above features, the herein described monitoring device can be further configured such that the user can perform alarm management on the monitoring device 20 by permitting the user to actuate a feature provided on the user interface 92 that creates a predetermined percentage change to the alarm limits for a single parameter each time the SELECT button 96 is depressed at the time of an existing alarm. The initialization and initial percentage settings for each of the alarm parameter settings is performed according to this specific embodiment as part of the configuration of the monitoring device 20 prior to use of the monitoring device 20 through the PC 192 using the configuration file to override factory configuration settings, the new settings being stored by the CPU 174.” [0186]
NIBP, SpO2 limits changed by a percent….
“… For example, upper and lower alarm limits for HR/PR can initially be set to alarm at an upper rate of 90 and a lower rate of 60. Using the latter feature, each time the SELECT button 96 is actuated for the above feature in the control menu, the alarm limits can be incremented by a predetermined percentage (e.g., 5 percent, 10 percent, or other). For example, if a five percent change were configured for the herein described monitoring device 20, the alarm limits would change to 94 (upper)/57 (lower) the first time the SELECT button 96 is depressed, 99 (upper)/54 (lower) the second time the button is depressed, and so forth. Similarly, NIBP (systolic pressure, diastolic pressure and mean pressure), SpO.sub.2 and respiration rate limits can be similarly adjusted wherein the amount from factory (default) preset value alarm limit values can be adjusted, depending on the patient mode, for individual parameters as part of the pre-configuration routine using the PC 192. A tabular listing 950 is shown in FIG. 65 for appropriate percentage changes to the alarm limits according to one example.” [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use percentages as taught by Gibson et al. 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. Further motivation is provided by Gibson et al. who teaches the advantages of using percentages for monitoring change.
Regarding claim 21
The method of claim 17, wherein the modification event is based on one or more of the first plurality of measured patient parameters being within a predefined percentage of an alarm limit.
Ellis et al. teaches:
Trigger event (alarm) and meeting a threshold…
“The trigger event is preferably a specific named event selected from an extensible set of predefined events. The system supports a range of event types and sources, including, for example: sensor events corresponding to a sensor measurement; a particular sensor event could indicate any measurement obtained from a particular sensor, or could indicate a measurement meeting a particular criterion, such as a predetermined sensor value threshold;… “ [0213] – [0214]
The combined references teach vital sign devices and thresholds. They also teach change and modification and alarm/warning. They do not teach percentage with alarm.
Gibson et al. also in the business of vital signs teaches:
Percentage change with alarm limit…
“In addition to the above features, the herein described monitoring device can be further configured such that the user can perform alarm management on the monitoring device 20 by permitting the user to actuate a feature provided on the user interface 92 that creates a predetermined percentage change to the alarm limits for a single parameter each time the SELECT button 96 is depressed at the time of an existing alarm. The initialization and initial percentage settings for each of the alarm parameter settings is performed according to this specific embodiment as part of the configuration of the monitoring device 20 prior to use of the monitoring device 20 through the PC 192 using the configuration file to override factory configuration settings, the new settings being stored by the CPU 174.” [0186]
Percent change with alarm limits and NIBP, SpO2 limits changed by a percent….
“… For example, upper and lower alarm limits for HR/PR can initially be set to alarm at an upper rate of 90 and a lower rate of 60. Using the latter feature, each time the SELECT button 96 is actuated for the above feature in the control menu, the alarm limits can be incremented by a predetermined percentage (e.g., 5 percent, 10 percent, or other). For example, if a five percent change were configured for the herein described monitoring device 20, the alarm limits would change to 94 (upper)/57 (lower) the first time the SELECT button 96 is depressed, 99 (upper)/54 (lower) the second time the button is depressed, and so forth. Similarly, NIBP (systolic pressure, diastolic pressure and mean pressure), SpO.sub.2 and respiration rate limits can be similarly adjusted wherein the amount from factory (default) preset value alarm limit values can be adjusted, depending on the patient mode, for individual parameters as part of the pre-configuration routine using the PC 192. A tabular listing 950 is shown in FIG. 65 for appropriate percentage changes to the alarm limits according to one example.” [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use percentages as taught by Gibson et al. 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. Further motivation is provided by Gibson et al. who teaches the advantages of using percentages for monitoring change and using alarm limits to notify changes are happening.
Claims 24-28, 31, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2022/0284994 to Ellis et al. in view of Pub. No. US 2020/0373007 to Hall et al.
Regarding claim 24
A medical system, comprising:
a monitor mount; and
Ellis et al. teaches:
Phone (monitor) placed in pocket or bag (holder)…
“The data is initially stored in a local database at the mobile application/user device and the system implements data synchronization between the local database and central system. In preferred embodiments, data is uploaded to the central system in step 238 in a batch. For example, given a lung function test and completed questionnaire, the application instance transmits the raw flow volume loop from the mobile spirometer as well as the response to the questionnaire. In other examples such as the study of Parkinson's Disease, the patient may be required to complete a walking assignment in which the phone is placed in a pocket or bag and motion of the patient is recorded by the smartphone accelerometer. The patient may also be required to complete a questionnaire which assesses the severity of their Parkinson's disease symptoms. The uploaded data may then include a data set of accelerometer readings acquired over a particular time period together with the questionnaire inputs. [0051]
See Monitor Mount below.
a medical device mounted to the monitor mount and programmed to
automatically generate customized measurement schedules, the medical device
comprising:
Assignment schedule for assigned (customized) tasks…
“The mobile application provides necessary prompts and instructions to the user for using external devices. For example, the assignment schedule may prompt the patient to put on or remove a wrist-worn accelerometer that is to be used for an assignment. Instructions for completing assignment tasks and how to use specialised sensor devices may be provided to a user as text, animations, video etc.” [0054]
Customized task definition…
“The data collection activities are specified in the configuration package generated by the central system and communicated to the user devices as task definitions using the above task model. In preferred embodiments, all task definitions are device agnostic. For predefined tasks in the catalogue, task definitions are preferably also stored in the catalogue in this format. After selection from the catalogue, the configuration may be generated directly from the task definition or the task definition may be customized by the researcher if needed.” [0190]
See Monitor Mount below.
one or more sensors attached to a patient programmed configured to measure a first plurality of patient parameters according to a predefined measurement schedule;
Sensor devices…
“The system also supports collection of different types of data from different sensor devices (both integrated into the user device and separately connected), as well as direct user input (e.g. via symptom questionnaires), using configurable assignment schedules. The system can simultaneously manage multiple study configurations across multiple studies/disease areas, with information sent in near real-time to the central monitoring system where it is tracked for compliance, checked for quality and analysed.” [0026]
Smart watches (sensors attached to a patient) to monitor health status of users (measure first plurality of patient parameters)…
“Modern personal computer/communication devices such as smartphones, smart watches and personal fitness monitors include various sensors useful for monitoring the health status of users. For example, accelerometers can be used to monitor physical exercise, and infrared sensors can be used to measure heart rate. Many smartphone manufacturers provide health and fitness monitoring applications and services using data collected from such sensors. More specialised medical sensor devices are also available that can be connected to smartphones via Bluetooth, Wi-Fi or similar, such as spirometers and thermometers, and that can use a smartphone to display and process the sensor data produced by the devices.” [0002]
Determined schedule (therefore, predefined measurement schedule)…
“After scheduling, the data collection application performs the data collection activities in accordance with the determined schedule. This involves, for each scheduled data collection activity, identifying (step 914) based on the schedule data a target time at which the given data collection activity is to be performed (this could have been determined at the scheduling stage, but for any dynamic event-based timestamps this occurs once the timestamp has been resolved, e.g. in response to the triggering event for the timestamp being detected), and identifying the relevant data source from which data is to be acquired (e.g. a sensor for a sensor-based data collection activity).” [0204]
one or more processors;
Fig. 11, ref. 1102 is example of processor…
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media_image1.png
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“FIG. 11 illustrates elements of a computer system for implementing described processes and techniques. FIG. 11 shows a single representative user device 104, for example a smartphone (though in practice the system will include many such devices), and a data collection server 1100 for implementing various functions of the study management system.” [0284]
wherein the one or more processors are programmed to:
receive the first plurality of measured patient parameters from the
one or more sensors based on the predefined measurement schedule;
“Different internal sensors 302 and external sensor devices 108, and different processing modules 316, may be selected as a function of different study requirements. For example, in a study of sleep, an actigraphy device may be used to collect movement data overnight. This movement data is then processed by suitable algorithms (e.g. publicly available and validated algorithms may be used) which derive properties of sleep such as total sleep time, number of times awake etc. In another example, the external sensor device 108 may be a mobile spirometer. The patient blows into the mobile spirometer two or more times and the mobile spirometer records the flow of the patient's breath through the device. The flow data is then processed to calculate lung function measurements. In other examples, the external sensor device 108 may be a single lead ECG (electrocardiogram) sensor, which is a sensor applied via a patch which the patient sticks to their chest near the heart. In this example, the electrical signals of the heartbeat are recorded and an algorithm processes the data to derive the number of heartbeats per minute and other supported measures.” [0096]
Configurable assignment (predefined) schedules…
“The system also supports collection of different types of data from different sensor devices (both integrated into the user device and separately connected), as well as direct user input (e.g. via symptom questionnaires), using configurable assignment schedules. The system can simultaneously manage multiple study configurations across multiple studies/disease areas, with information sent in near real-time to the central monitoring system where it is tracked for compliance, checked for quality and analysed.” [0026]
receive a second plurality patient parameters, wherein the second
plurality of patient parameters includes at least a patient location;
User (patient) health data (medical condition), therefore second patient parameter…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Current location and time zone (parameter of location) and relies on (receives) local notifications (also patient location parameter) on the smartphone or peripheral device…
“The system is able to send time-critical text/email notifications to an end user's device. When time zone data is unreliable, it becomes challenging to guarantee that a notification can be sent at a precise time in relation to the user's current location and time zone. The system compiles time zone data from available devices and peripherals in order to properly schedule notifications and assignments. The system is also able to schedule time-critical sensor data collection windows via this approach. Where possible, this system relies on local notifications on the smartphone, generated by either the schedule, a mobile assignment, or by a peripheral device.” [0283] Inherent with time zone is a patient location associated with the time zone.
See Location below.
automatically generate a modified measurement schedule in response to a modification event detected from at least one of the first plurality of received measured patient parameters, and the second plurality of patient parameters;
Dynamically (automatically) schedule assignments where schedule assignments are dynamic (modified)…
“In addition to instructing the mobile device on how to run assignments via task definitions, the configuration also specifies scheduling information which is interpreted by a scheduling engine of the application to dynamically schedule assignments in an event-based manner, where sensor readings, user-specific medical conditions, and external events can change the flow of the assignments…”
Dynamic assignment schedule (determining a modification event has been detected)…
“The proposed solution allows efficient and configurable management of how and when assignments run whilst also enabling the generation of an audit trail. Meeting the complex logical demands in a low-bandwidth environment is achieved by creating a virtual and dynamic assignment schedule. The schedule is specified by schedule data in the configuration package and is parsed by a schedule parser to generate any configurable set of assignments and associated data collection activities.” [0199]
Example of actual target time based on event detected, which is different from initially determined schedule…
“In step 912, the schedule data is parsed and a schedule of data collection activities is determined. In step 913, the mobile application is configured based on the specified set of data collection activities and schedule data. Note that target times for data collection activities may be fully specified in absolute terms or in a form that can be resolved at the time scheduling is originally performed. However, as described later, the system also supports dynamically resolved schedule timings, whereby target times for data collection activities (and other actions such as notifications or data uploads) are expressed relative to the occurrence of specific events, with the actual target time for an action only determined once the event has been detected. Thus, for such data collection activities, the initially generated schedule includes unresolved, dynamically determined target times (in the form of event-based timestamps as discussed in more detail below) that will be resolved to concrete timings at some later point.” [0203]
Example of resolved time used to initiate (automatically generate) a scheduled data collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
Another example of defines (predefined) schedule…
“In step 214, the researcher then defines the assignment schedule for the selected assignments, which can be daily, weekly, monthly or otherwise configured according to study requirements. For example, in the study of sleep a patient may be required to complete a daily sleep diary.” [0040]
Update in real time a schedule based on sensor readings…
“Through the use of event-based timestamps and multiple condition types including conditions based on user input, sensor readings, and other factors, this system is able to dynamically create and update a schedule of assignments in real-time to ensure each patient's compliance with the study protocol applicable for that patient.” [0246]
automatically implement the modified measurement schedule; and
One example of update configuration (therefore, automatically implement) schedule…
“If, during the health study, updated configuration data is received from the study management system (e.g. following a change in study protocol as discussed above), then the mobile application updates the locally stored configuration and reconfigures the application accordingly (as per steps 911-913). The reconfiguration may modify the data collection activities performed, the associated schedule etc. Data collection then continues in accordance with the updated configuration.” [0208]
Resolve target time used (automatically implement) the scheduled data (modified scheduled) collection activity…
“Timing information is specified based upon a time amount, a time unit, and one or more trigger events. These define an event-based timestamp. A reference time for the timestamp is determined by detection of an event matching the specified trigger event (or one of the specified events if multiple alternative trigger events are specified). The event time (e.g. a time of receipt or creation of the event, which could be specified by an event time stamp of the event) defines the reference time used for timestamp resolution. The target time for the time stamp is then resolved by adding the specified time amount (converted as needed to the required time base in accordance with the specified unit) to the reference time. That resolved target time is stored as part of the time stamp and can subsequently be referenced and used to initiate a scheduled data collection activity such as a particular assignment or assignment task, or to trigger a notification or other action. The following set outs an example data format for storing a dynamic, event-based timestamp:” [0211]
provide an indication that modified measurement schedule has
been generated and implemented.
Push and SMS (providing an indication) of scheduling and taking sensor readings…
“These dynamically resolved, event-based timestamps are used for resolving assignment launch and due times, push and SMS notification scheduling, triggering individual data collection activities, such as taking sensor readings, and uploading sensor data from the mobile application to the study management system. However, the system could be expanded to allow the timestamps to be used to trigger any programmatic action.” [0223]
Monitor Mount
Ellis et al. teaches monitoring devices and holder for monitoring devices. They do not teach mount.
Hall et al. also in the business of monitoring devices teaches:
Monitor mount…
“One or more embodiments provide a method for displaying transport indicators related to a patient transport on a physiological monitoring device. The method includes: operating the physiological monitoring device in a non-transport mode while docked to one of at least one monitor mount; displaying first location context information corresponding to a first patient care area on a display of the physiological monitoring device while the physiological monitoring device is in the non-transport mode in the first patient care area; the physiological monitoring device detecting an undocking event in response to undocking the physiological monitoring device from a first monitor mount of the at least one monitor mount, wherein the first monitor mount is located in the first patient care area; and in response to detecting the undocking event, the physiological monitoring device switching into a transport mode, including changing the first location context information to transport context information on the display.” [0007]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ellis et al. the ability to use a monitor mount as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the advantages of using a monitor mount with monitoring devices and Ellis et al. benefits as they also teach holding a monitoring device with an object.
Location
Ellis et al. teaches sensors and location for a patient. They also teach various health sensor devices to collect health data. They do not specifically teach details of patient location.
Hall et al. also in the business of sensors and patient location teach:
Physiological monitoring device to generate location information…
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
Using GPS for location information…
“As shown in FIG. 2, whether the physiological monitoring device 7 is docked in a monitor mount 10 or separated therefrom, the physiological monitoring device 7 is maintained in proximity with the patient 1. The physiological monitoring device 7 includes a global positioning system (GPS) or other location data system 26 that can be connected to the communication interface circuitry of microcontroller 3b so that the physiological monitoring device 7 can transmit to the clinician, caregiver, or other devices the location of the patient 1 corresponding to the location of the physiological monitoring device 7 continuously and/or at predetermined intervals. The location of the patient 1 may be transmitted at all times including during transport. Additionally, the current location of the patient 1 can be used by the microcontroller 3b to determine an estimated time of arrival of the patient 1 at, for example, a predetermined destination. The microcontroller 3b may also use transport information, including location information acquired at different sample times (e.g., including a current sample time and one or more previous sample times) to determine whether the patient 1 is stationary or in motion.” [0061]
Location information cross-referenced to other patient information….
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ellis et al. the ability to determine patient location as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the benefits of using location information for patients and Ellis et al. benefits as they also determine location using time zones and this provides more specific location information.
The combined references teach location and using GPS. They do not explicitly teach smartphones with GPS. However, one of ordinary skill in the art would recognize that smart phones have GPS for location purposes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that smart phones could provide location information. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of smart phones and would provide predictable results.
Regarding claim 25
The medical system of claim 24, wherein the modification event includes at least one of a change in a medical condition of the patient, a change in a location of the patient, a change in personal information of the patient, and a change in physiological information of the patient.
Ellis et al. teaches:
Patient not completing activity (change in medical condition) and trigger rescheduling…
“In an example, a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes—a common test in COPD (chronic obstructive pulmonary disease) and heart disease to assess patient functional status. Quality checks on the received raw data file may show that only 1 minute's worth of data was received. This could be due to the patient not completing the activity, or due to a technical issue e.g. the wrist-worn accelerometer was faulty or low on power and only captured intermittent segments during the 6 minutes of walking. As a further example, a signal analysis may reveal that a heart rate sensor or other sensor was faulty or not used or worn correctly, resulting in an intermittent or low-quality signal. In such cases, an assignment is flagged as “incomplete” because it was not completed as required and/or required data is missing or of insufficient quality. In some implementations, incomplete assignments are logged as such in the central system and notifications issued to clinical personnel to follow up with the patient. In other implementations, an incomplete assignment may trigger automated rescheduling of the assignment for the patient.” [0060]
User changing time zone (change in location)…
“… Preferred embodiments therefore use an alternative format for defining timings, to allow generation of dynamic dates such that if the user is changing time zone or changing their daily routine, the client contains an instruction set that describes how to handle these changing behaviours.” [0198] Inherent with user changing time zone is changing location.
Regarding claim 26
The medical system of claim 25, wherein the change in the medical condition of the patient includes at least one of detection hypovolemia, sepsis, cardiac events, and shock.
Ellis et al. teaches:
Patient not completing activity (change in medical condition) due to heart disease (cardiac events) …
“In an example, a sensor data file is received for a walking assignment. The walking assignment requires the patient to walk for 6 minutes—a common test in COPD (chronic obstructive pulmonary disease) and heart disease to assess patient functional status. Quality checks on the received raw data file may show that only 1 minute's worth of data was received. This could be due to the patient not completing the activity, or due to a technical issue e.g. the wrist-worn accelerometer was faulty or low on power and only captured intermittent segments during the 6 minutes of walking. As a further example, a signal analysis may reveal that a heart rate sensor or other sensor was faulty or not used or worn correctly, resulting in an intermittent or low-quality signal. In such cases, an assignment is flagged as “incomplete” because it was not completed as required and/or required data is missing or of insufficient quality. In some implementations, incomplete assignments are logged as such in the central system and notifications issued to clinical personnel to follow up with the patient. In other implementations, an incomplete assignment may trigger automated rescheduling of the assignment for the patient.” [0060]
Regarding claim 27
The medical system of claim 25, wherein the change in a location of the patient includes at least one of the patient moving to an emergency room, an operating room, an intensive care unit, a neonatal intensive care unit, a post anesthesia care unit, a recovery room, and a labor and delivery room.
The combined references teach location. They do not teach specific location.
Hall et al. also in the business of location teaches:
Determine a location of a patient in a hospital…
“The location data system 26 is configured determine a location of the physiological monitoring device 7 and generate location information based on determined the location. For example, location data provided by the location data system 26, which may include information pertaining to a floor level, a hallway or corridor, a terminal, a room number, or other location information used to determine a precise location within a building, can be compared to or cross-referenced to stored information related to a patient care facility layout (e.g., a hospital layout) or a patient care facility map (e.g., a hospital map) by the microcontroller 3b. The location data system 26 may also include an accelerometer or other motion detector device to detect movement of the physiological monitoring device 7.” [0062]
Where to locate patient and ETA (move) patient to operating room…
“Transport location information and ETA can support patient care facility emergency code situations in which a crisis team is required to intervene by communicating the patient's location and ETA. It is contemplated by the disclosure of the present application that an accelerometer, a GPS, and/or other location technology may provide intelligence about when a patient transport is actually occurring and can be used to refine the detection of a transport activity. The GPS or location technology can be used along with a patient care facility layout or a patient care facility map to track the location of a patient in an event of a patient care facility emergency code, which could be used to communicate information to a crises team (e.g., where to locate the patient and the ETA of the patient to a patient care area, such as an operating room).” [0116]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine patient location such as operating room as taught by Hall et al. 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. Further motivation is provided by Hall et al. who teaches the benefits of using location information for patients in emergency situations and the combined references benefit as they are also monitoring patients.
Regarding claim 28
The medical system of claim 25, wherein the change in personal information of the patient includes obtaining information about at least one of current medications taken by or administered to the patient, information about the patient's medical history, and personal information including information about the patient's age, height, weight, and/or gender.
Ellis et al. teaches:
Using data such as weight…
“Data input activities are based on direct user input of health-related data values, such as patient questionnaires and health diaries. In such activities a user is prompted to supply specific information, which could include subjective symptom assessments as well as manual input of measurements obtained using non-connected devices (e.g. a weight measurement obtained using conventional scales). Data input activities may implement capture of health-related data in the form of electronic patient reported outcomes (ePRO), clinician-reported outcomes (ClinRO) or equivalent. A data input activity may be implemented as an input form displayed on a display of the user device, with a predefined set of input fields to be completed by a user via data input at the user device (e.g. using touch screen and onscreen keyboard/handwriting recognition, voice recognition etc.).” [0077]
User (patient) medical history…
“Conditions which are used to determine whether a particular assignment group, task or sensor reading is applicable to the current user based on user-specific conditions, such as medical history (e.g. presence/absence of a specific medical condition), or user device capabilities (e.g. availability of a specific integrated sensor or connected sensor device required for an assignment/task). Compliance Metrics which set additional rules on how the assignments can be completed. Some examples could be a time limit that begins once the first assignment is started, or readings that would indicate that a user took or didn't take medication before an assignment when it is required. A task list which links to the task definitions that should be available for completion within the launch-due window. For example, the tasks list could include a walk test and a symptom questionnaire.” [0243] – [0245]
Regarding claim 31
The medical system of claim 24, wherein patient parameters of the patient include at least one of non-invasive blood pressure (NIBP),temperature, heart rate, an electrocardiogram (ECG non-invasive peripheral oxygen saturation (SpO2), end tidal carbon dioxide (etCO2), apnea of the patient, neuromuscular transmission (NMT), and cardiac output (CO).
Ellis et al. teaches:
Blood-pressure sensors…
“Sensor-based data collection activities use one or more device sensors and/or external sensors connected to the user device (or connected to external data collection platforms) to obtain health-related data in the form of sensor data. Sensors can include general-purpose sensors (e.g. accelerometers built into a smartphone) and/or special-purpose medical sensors (e.g. spirometers, blood pressure sensors etc).” [0076]
ECG…
“Different internal sensors 302 and external sensor devices 108, and different processing modules 316, may be selected as a function of different study requirements. For example, in a study of sleep, an actigraphy device may be used to collect movement data overnight. This movement data is then processed by suitable algorithms (e.g. publicly available and validated algorithms may be used) which derive properties of sleep such as total sleep time, number of times awake etc. In another example, the external sensor device 108 may be a mobile spirometer. The patient blows into the mobile spirometer two or more times and the mobile spirometer records the flow of the patient's breath through the device. The flow data is then processed to calculate lung function measurements. In other examples, the external sensor device 108 may be a single lead ECG (electrocardiogram) sensor, which is a sensor applied via a patch which the patient sticks to their chest near the heart. In this example, the electrical signals of the heartbeat are recorded and an algorithm processes the data to derive the number of heartbeats per minute and other supported measures.” [0096]
Regarding claim 34
The medical system of claim 24, wherein the second plurality of patient parameters includes at least one of personal information of the patient, and a medical condition of the patient.
Ellis et al. teaches:
User (patient) health data (medical condition)…
“Disclosed is a computer-implemented method performed at a user device for obtaining health-related data. The health-related data may comprise one or more of: data indicative of user health, and source data from which data indicative of user health can be derived. The method comprises receiving, over a communications network, a data collection configuration from a health monitoring system, the data collection configuration comprising schedule data defining a schedule of data collection activities to be performed at the user device, each data collection activity for acquiring at the user device respective health-related data; determining based on the schedule data, for each of the data collection activities, a target time at which the data collection activity is to be performed; initiating the data collection activities at the user device in accordance with their respective determined target times to obtain health-related data for the data collection activities; and transmitting result data to the health monitoring system based on the obtained health-related data.” [0006]
Metadata (personal information) about the patient…
“If a matching active study patient is identified, then in step 242, the data received is stored as raw data in a database. The data received contains metadata about the patient, assignment name, device used and timestamps. The data received also contains assignment data which is extracted (step 243). The data itself will vary, based on the assignment. For example, in one implementation the assignment data may be a continuous accelerometer sensor file. In another implementation, the assignment data may be an entry in a sleep diary noting time in bed, time out of bed the next day and then number of times a patient was awake during the night.” [0058]
Using data such as weight…
“Data input activities are based on direct user input of health-related data values, such as patient questionnaires and health diaries. In such activities a user is prompted to supply specific information, which could include subjective symptom assessments as well as manual input of measurements obtained using non-connected devices (e.g. a weight measurement obtained using conventional scales). Data input activities may implement capture of health-related data in the form of electronic patient reported outcomes (ePRO), clinician-reported outcomes (ClinRO) or equivalent. A data input activity may be implemented as an input form displayed on a display of the user device, with a predefined set of input fields to be completed by a user via data input at the user device (e.g. using touch screen and onscreen keyboard/handwriting recognition, voice recognition etc.).” [0077]
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in view of Pub. No. US 2008/0281168 to Gibson et al.
Regarding claim 29
The medical system of claim 24, wherein the modification event is based on:
a percentage change in one or more of the measured patient parameters; or
one or more of the measured patient parameters being within a predefined percentage of an alarm limit; or
a combination thereof.
Ellis et al. teaches:
Trigger event (alarm) and meeting a threshold…
“The trigger event is preferably a specific named event selected from an extensible set of predefined events. The system supports a range of event types and sources, including, for example: sensor events corresponding to a sensor measurement; a particular sensor event could indicate any measurement obtained from a particular sensor, or could indicate a measurement meeting a particular criterion, such as a predetermined sensor value threshold;… “ [0213] – [0214]
The combined references teach modification event. They do not teach percentage.
Gibson et al. also in the business of events teaches:
Percentage change…
“In addition to the above features, the herein described monitoring device can be further configured such that the user can perform alarm management on the monitoring device 20 by permitting the user to actuate a feature provided on the user interface 92 that creates a predetermined percentage change to the alarm limits for a single parameter each time the SELECT button 96 is depressed at the time of an existing alarm. The initialization and initial percentage settings for each of the alarm parameter settings is performed according to this specific embodiment as part of the configuration of the monitoring device 20 prior to use of the monitoring device 20 through the PC 192 using the configuration file to override factory configuration settings, the new settings being stored by the CPU 174.” [0186]
NIBP, SpO2 limits changed by a percent….
“… For example, upper and lower alarm limits for HR/PR can initially be set to alarm at an upper rate of 90 and a lower rate of 60. Using the latter feature, each time the SELECT button 96 is actuated for the above feature in the control menu, the alarm limits can be incremented by a predetermined percentage (e.g., 5 percent, 10 percent, or other). For example, if a five percent change were configured for the herein described monitoring device 20, the alarm limits would change to 94 (upper)/57 (lower) the first time the SELECT button 96 is depressed, 99 (upper)/54 (lower) the second time the button is depressed, and so forth. Similarly, NIBP (systolic pressure, diastolic pressure and mean pressure), SpO.sub.2 and respiration rate limits can be similarly adjusted wherein the amount from factory (default) preset value alarm limit values can be adjusted, depending on the patient mode, for individual parameters as part of the pre-configuration routine using the PC 192. A tabular listing 950 is shown in FIG. 65 for appropriate percentage changes to the alarm limits according to one example.” [0187]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use percentages as taught by Gibson et al. 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. Further motivation is provided by Gibson et al. who teaches the advantages of using percentages for monitoring change.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following prior art teaches schedule:
US-20240312612-A1; US-20080058773-A1; US-20130331664-A1; US-20140155705-A1; US-20170109493-A1; US-20220139550-A1; US-20220386090-A1; US-9659037-B2
Gondal et al., “Integrated Sensing and Diagnosis – The next step in Real Time Patient Health Care,” 2007, 6th IEEE/ACIS International Conference on Computer and Information Science, pp. 1-6.
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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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684