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 .
Claims 1,3-4,6-8,13-16,18-26 are pending. Claims 1,3-4,6-8,13-16,18-26 are rejected herein.
Priority
This application claims priority to provisional application #63/595,043 and so has a priority date equivalent to an effective date of 01 November 2023.
Objections
It is unclear if there is a spelling mistake in claim 6. The claim reads “… wherein the changing the state of the at least one of the plurality of environmental sensors alters a monitoring focus of the at least on of the plurality of environmental sensors.” The Examiner thinks this second on was intended to be one.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1,3-4,6-8,13-16,18-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The Statutory Categories
The claim recites two systems to monitor a person under care by one or more stakeholders, which are both within a statutory category for subject matter eligibility analysis purposes.
Step 2A Prong One: The Abstract Idea
The limitations of receiving a plurality of tokens from environmental […entities associated with the location of the patient while…] monitoring the person under care, each of the tokens comprising a detected data set representing behaviors of the person under care in an environment, each of the behaviors is represented by a multi-dimensional feature set forming part of a health care profile for the person under care; […] storing a digital twin of the plurality of tokens […], the digital twin comprising a dynamic tokenized representation of the multi-dimensional feature set forming part of a health care profile of the person under care in the environment; […] evaluating the digital twin of the plurality of tokens […], the evaluation of the digital twin including the digital twin to identify potential future states of the person under care and, based on the evaluation of the digital twin, to optimize a set of configuration of the plurality of [..entities…] and change a state of the at least one of the plurality of environmental […entities…] as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting a non-transitory computer readable storage medium, at transceiver and one processor, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the non-transitory computer readable storage medium, at transceiver and one processor, this claim encompasses a person thinking about receiving a plurality of tokens from environmental […entities associated with the location of the patient while…] monitoring the person under care, […] storing a digital twin of the plurality of tokens […]; […] evaluating the digital twin of the plurality of tokens […] and changing a state of the at least one of the plurality of environmental […entities…] in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Practical Application
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a transceiver, non-transitory computer-readable storage medium and at least one processor that implements the abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., a generic general-purpose computer or components thereof) such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites environmental sensors and tokens. The environmental sensors and tokens generally link the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
Step 2B: Significantly More
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a transceiver, a non-transitory computer-readable storage medium and at least one processor to perform the noted steps 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 (“significantly more”).
The claim further recites environmental sensors and tokens. The environmental sensors and tokens generally link the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
Dependent Claims and Additional Elements
Claims 3,4,6,7,8,14-16,18-16 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 3 merely describes identifying potential future states of the person under care by analyzing the digital twin and the plurality of token. Claim 4 merely describes analyzing the digital twin and the plurality of tokens. Claim 6 merely describes altering a plurality of monitoring focuses. Claim 7 merely describes increasing fidelity or granularity. Claim 8 merely describes detecting specific data sets. Claim 14 merely describes identifying potential future states of the person under care by analyzing the digital twin. Claim 15 merely describes identifying potential future states of the person under care by analyzing the digital twin and the plurality of tokens. Claim 16 merely describes analyzing the digital twin and the plurality of tokens. Claim 18 merely describes altering a plurality of monitoring focuses. Claim 19 merely describes the monitoring focuses increases the fidelity or granularity. Claim 20 merely describes wherein changing the state involves a quiescent state or alert state. Claim 21 merely describes changing the state from a quiescent state to an event or alert state. Claim 22 merely describes changing the state of an entity from a list of entities. Claim 23 merely describes wherein changing the state involves a quiescent state. Claim 24 merely describes changing the state from a quiescent state to an event or alert state. Claim 25 merely describes the type of entity. Claim 26 merely describes wherein the game is of a type selected from the group consisting of pattern detection games, pattern matching games, accuracy detection games, speed detection games, cost detection games, balance games, consensus games, risk assessment games, and audit games.
The dependent claims contain a variety of additional elements including at least one processor, environmental sensors, breathing sensor, heart rate sensor and encryption key, motion sensor, haptic sensor, audio sensor, camera, acceleration sensor, altitude sensor, temperature sensor, humidity sensor. The processor was analyzed the same as the computer and/or computer part(s) in the independent claim and does not provide a practical application or significantly more for the same reasons. The sensors and camera generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicates that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
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.
Claim(s) 1,3-4,13-16,20-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0202107 A1 (hereafter Bostic) in view of US 2020/0185107 A1 (hereafter Cox).
Regarding Claim 1
Bostic teaches:
A system to monitor a person under care by one or more stakeholders, comprising: a transceiver configured to receive a plurality of tokens from a plurality of environmental sensors in the location configured to monitor the person under care, [Bostic teaches at para. [0309] the environmental sensor and/or the one or more wearable sensors will be Internet of Things (IoT) sensor and will be in communication with one or more Iot communication device, networks and databases. Bostic teaches at para. [0310] in some embodiments, the platform 100 will be configured to determine a personalized treatment plan for the patient based on at least one of the digital twin of the patient and the digital twin of the population of patients, the health information, the healthcare research information, and the sensor data using the one or more machine learning modules. Bostic teaches at Figure 17 monitoring location risks. Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors and wearable sensors worn by the patient, store the sensor data at the platform, and present the sensor data to the user of the platform. Collectively these teach a plurality of environmental sensors configured to monitor the person under care. Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and, in an example, QR-code scanning will indicate compliance with the testing protocol and determines employee eligibility for work. This teaches receiving a plurality of tokens as there are a plurality of test results. Bostic teaches at para. [0267] one non-limiting example of the communication device 1200 is a transceiver, although other forms of hardware are within the scope of the present disclosure. Collectively, this teaches a transceiver configured to receive a plurality of tokens from a plurality of environmental sensors in the location configured to monitor the person under care.]
each of the tokens comprising a detected data set representing behaviors of the person under care in an environment, [Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors and wearable sensors worn by the patient, store the sensor data at the platform, and present the sensor data to the user of the platform. This teaching indicates that the person under care will be in an environment. Bostic teaches at para. [0075] In embodiments, the method includes detecting misuse of a controlled medication of a patient, that includes obtaining, by a processing system, lab test results of a patient from a lab testing system; obtaining, by the processing system, patient attributes of the patient from one or more patient data sources; generating, by the processing system, a usage profile corresponding to the patient based on the lab test results of the patient and the patient attributes; determining, by the processing system, whether the usage profile is indicative of potential misuse of the controlled medication based on one or more features of the usage profile; and in response to determining potential misuse of the controlled medication, transmitting a notification that indicates the potential misuse by the patient. The potential misuse that is reflected by the laboratory results that are represented by a digital token is interpreted to be the detected data set representing behaviors of the person under care in an environment.]
each of the behaviors is represented by a multi-dimensional feature set forming part of a health care profile for the person under care; [Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors and wearable sensors worn by the patient, store the sensor data at the platform, and present the sensor data to the user of the platform. Bostic teaches at para. [0075] In embodiments, the method includes detecting misuse of a controlled medication of a patient, that includes obtaining, by a processing system, lab test results of a patient from a lab testing system; obtaining, by the processing system, patient attributes of the patient from one or more patient data sources; generating, by the processing system, a usage profile corresponding to the patient based on the lab test results of the patient and the patient attributes; determining, by the processing system, whether the usage profile is indicative of potential misuse of the controlled medication based on one or more features of the usage profile; and in response to determining potential misuse of the controlled medication, transmitting a notification that indicates the potential misuse by the patient. Bostic teaches at para. [0288] in some embodiments, the digital twin of the patient will include one or more numbers, trends, predictions, charts, graphs, threshold, ranges, 2-dimensional models, and/or 3-dimensional models of the patient, the one or more health states of the patient, risk factors of the patient, biometrics of the patient, data derived from health information related to the patient, and/or any other suitable metrics and/or information related to the patient. Collectively, Bostic teaches each of the behaviors is represented by a multi-dimensional feature set forming part of a health care profile for the person under care.]
a non-transitory computer-readable storage medium configured to store a digital twin of the plurality of token from the plurality of environmental sensors, [Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and in an example, QR-coded scanning will indicate compliance with the testing protocol and determines employee eligibility for work. This teaches the plurality of tokens; there are a plurality of test results. Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors and wearable sensors worn by the patient, store the sensor data at the platform, and present the sensor data to the user of the platform. Bostic teaches at para. [0288] in some embodiments, the digital twin of the patient will include one or more numbers, trends, predictions, charts, graphs, threshold, ranges, 2-dimensional models, and/or 3-dimensional models of the patient, the one or more health states of the patient, risk factors of the patient, biometrics of the patient, data derived from health information related to the patient, and/or any other suitable metrics and/or information related to the patient. Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. Bostic teaches at para. [0341] the processor, on any machine utilizing one, will include non-transitory memory that store methods, codes, instructions and programs as described herein and elsewhere. Collectively, this teaches a non-transitory computer-readable storage medium configured to store a digital twin of the plurality of token from the plurality of environmental sensors.]
[…]
at least one processor configured to evaluate the digital twin of the plurality of tokens from the plurality of environmental sensors, the evaluation of the digital twin including using the digital twin to identify potential future states of the person under care, [Bostic teaches at para. [0521] simulating, at the healthcare data system computing device, a future health state of said population of patients based on the digital twin of said patient via the digital twin of said patient and the machine learning module;… Bostic teaches at para. [0309] the platform will implement the sensor data in the digital twin of the patient using the digital twin module. Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. Collectively, this teaches the evaluation of the digital twin including using the digital twin to identify potential future states of the person under care. Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors, and/or wearable sensor worn by the patient, store the sensor data at the platform 100, and present the sensor data to the user of the platform 100. Bostic teaches at para. [0309] the platform 100 will train the one or more machine learning modules using the sensor data according to one or more machine learning and/or deep learning techniques. Bostic teaches at para. [0309] in some embodiments, the environmental sensor and/or the one or more wearable sensor will be Internet of Thing (IoT) sensors and will be in communication with one or more IoT communication devices, network, and/or databases. Bostic teaches at para. [0310] in some embodiments, the platform 100 will be configured to determine a personalized treatment plan for the patient based on at least one of the digital twin of the patient and the digital twin of the population of patients, the health information, the healthcare research information, and the sensor data using the one or more machine learning modules. Bostic teaches at para. [0310] by combining one or more of the digital twins of the patient and the digital twin of the population of patients, the health information, the healthcare research information, and the sensor data via the machine learning module, the machine learning module may formulate one or more very specific and precise personalized treatment plans particularly suited to the patient. Collectively this teaches evaluate the digital twin of the plurality of tokens from the plurality of environmental sensors. Collectively, this teaches at least one processor configured to evaluate the digital twin of the plurality of tokens from the plurality of environmental sensors, the evaluation of the digital twin including using the digital twin to identify potential future states of the person under care.]
the digital twin comprising a dynamic tokenized representation of the multi-dimensional feature set forming part of a health care profile of the person under care in the environment; [Bostic teaches at para. [0288] in some embodiments, the digital twin of the patient will include one or more numbers, trends, predictions, charts, graphs, threshold, ranges, 2-dimensional models, and/or 3-dimensional models of the patient, the one or more health states of the patient, risk factors of the patient, biometrics of the patient, data derived from health information related to the patient, and/or any other suitable metrics and/or information related to the patient. Bostic teaches at para. [0309] the platform will implement the sensor data in the digital twin of the patient using the digital twin module. Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. Bostic teaches at para. [0309] the platform will receive sensor data from one or more environmental sensors, and/or wearable sensor worn by the patient, store the sensor data at the platform 100, and present the sensor data to the user of the platform 100. Collectively, Bostic teaches the digital twin comprising a dynamic tokenized representation of the multi-dimensional feature set forming part of a health care profile of the person under care in the environment.]
Bostic may not explicitly teach:
and based on evaluation of the digital twin, to optimize a set of configurations of the plurality of environmental sensors; and change a state of at least one of the plurality of environmental sensors.
Cox teaches:
and based on evaluation of the digital twin, to optimize a set of configurations of the plurality of environmental sensors; and change a state of at least one of the plurality of environmental sensors. [Cox teaches at the Abstract disclosed is a computer system comprising a processor arrangement communicatively coupled to a data storage arrangement storing a virtual model of a patient, said virtual model comprising at least one of a digital representation of at least a part of the anatomy of the patient and a physiological model of a bodily process of the patient; and a communication module communicatively coupled to said processor arrangement and arrange to receive sensor data from one or more sensors arranged to monitor said patient, wherein the processor arrangement is arranged to retrieve said virtual model from the data storage arrangement; receive said sensor data from the communication module; generate an instruction for altering a mode of operation of at least one sensor of the one or more sensors in response to said evaluation or in response to a user request; and transmit said instruction to the at least one sensor or to a device for invoking control of said at least one sensor with the communication module. Cox teaches at para. [0048] the actual sensor data will be used to validate such a prediction, e.g., by using the actual sensor data to simulate the physical condition of the patient at the same point in time, e.g. a point in time in the future or the actual point in time, and will be used to update the digital twin if necessary, e.g. if such a validation highlights a discrepancy between the simulated physical conditions using the ‘old’ and ‘new’ sensor data respectively.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify healthcare management using digital twins of Bostic to the digital twin operation of Cox with the motivation of rather than simply increasing medical resources, for which the financial resources may not be available, there exists a need to improve the efficiency of such healthcare systems (Cox at para. [0004]).
Regarding Claim 13
Due to its similarity to Claim 1, Claim 13 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 3
Bostic/Cox teach the system of claim 1. Bostic/Cox further teach:
wherein the at least one processor is further configured to identify potential future states of the person under care by analyzing the digital twin and the plurality of tokens. [Bostic teaches at para. [0328] test results will be verified and uploaded directly from participating laboratories to a secure mobile solution that links the employee identity to their patient profile. Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and in an example, QR-coded scanning will indicate compliance with the testing protocol and determines employee eligibility for work. The eligibility to work determination is interpreted here as identifying potential future states of the person under care by analyzing the plurality of tokens. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. This teaches identifying potential future states of the person under care by analyzing the digital twin.]
Regarding Claim 4
Bostic/Cox teach the system of claim 3. Bostic/Cox further teach:
wherein the processor uses machine learning to analyze the digital twin and the plurality of tokens. [Bostic teaches at para. [0328] test results will be verified and uploaded directly from participating laboratories to a secure mobile solution that links the employee identity to their patient profile. Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and in an example, QR-coded scanning will indicate compliance with the testing protocol and determines employee eligibility for work. This teaches the plurality of tokens; each employee has a token so there is a plurality. Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. Collectively, this teaches wherein the processor uses machine learning to analyze the digital twin and the plurality of tokens.]
Regarding Claim 14
Bostic/Cox teach the system of claim 13. Bostic/Cox further teach:
wherein the at least one processor is further configured to identify potential future states of the person under care by analyzing the digital twin. [Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Collectively, this teaches wherein the at least one processor is further configured to identify potential future states of the person under care by analyzing the digital twin. Bostic teaches at para. [0023] in embodiments, a method for determining a medical service need, including ingesting, by a computing device, patient data of a patient received from one of a plurality of patient data providers; transmitting the ingested data to a data store; transmitting the data store to a machine learning module, wherein the machine learning module applies at least one algorithm selected from the set comprising transformation algorithms, normalization operation and refinement operations; and using the machine learning module to simulate a future health state for the patient; matching the simulated future health state to predicted patient medical service need; matching the predicted medical service need to at least one of the patient’s healthcare providers; and transmitting an alert to the at least one healthcare provider indicating the predicted patient medical service need.]
Regarding Claim 15
Bostic/Cox teach the system of claim 14. Bostic/Cox further teach:
wherein the at least one processor is further configured to identify potential future states of the person under care by analyzing the digital twin and the plurality of tokens. [Bostic teaches at para. [0328] test results will be verified and uploaded directly from participating laboratories to a secure mobile solution that links the employee identity to their patient profile. Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and in an example, QR-coded scanning will indicate compliance with the testing protocol and determines employee eligibility for work. The eligibility to work determination is interpreted here as identifying potential future states of the person under care by analyzing the plurality of tokens. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. This teaches identifying potential future states of the person under care by analyzing the digital twin and the plurality of tokens.]
Regarding Claim 16
Bostic/Cox teach the system of claim 15. Bostic/Cox further teach:
wherein the processor uses machine learning to analyze the digital twin and the plurality of tokens. [Bostic teaches at para. [0328] test results will be verified and uploaded directly from participating laboratories to a secure mobile solution that links the employee identity to their patient profile. Bostic teaches at para. [0328] a digital token will act as a badge for employees based on their test results status, and in an example, QR-coded scanning will indicate compliance with the testing protocol and determines employee eligibility for work. This teaches the plurality of tokens; each employee has a token so there is a plurality. Bostic teaches at para. [0371] updating, at the healthcare data system computing device, the digital twin of said patient based on the simulation of the future health state of said patient;…. Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;…. Collectively, this teaches wherein the processor uses machine learning to analyze the digital twin and the plurality of tokens.]
Regarding Claim 20
Bostic/Cox teach the system of claim 1. Bostic/Cox further teach:
The system of claim 1, wherein changing the state of the plurality of environmental sensors includes placing the at least one sensor in the plurality of environmental sensors in a quiescent state. [Cox teaches at para. [0051] in order to avoid unnecessary use of the one or more sensors, each sensor will (initially) be operated in a mode of operation in which the sensor preserves energy, for example to extend the operational lifetime of a battery-powered sensor. Cox teaches at para. [0051] for example, in case of a primary sensor or monitoring a particular physical condition of a patient, the sensor will be operated at a low data operating or sampling frequency, such as for example an operating frequency of 2 minutes per quarter of an hour for a heart rate monitoring sensor. Cox teaches at para. [0051] in case of the presence of a secondary sensor for monitoring a particular physical state of a patient, e.g., a sensor verifying or supporting the primary sensor for monitoring a particular physical condition of a patient, such a secondary sensor will be switched off. Cox teaches at para. [0051] more generally, the mode of operation of such a sensor will be controlled with the digital twin to strike a balance between the accuracy of the simulations of the patient’s physical condition with the digital twin and the convenience of the patient, e.g., to preserve operational and battery lifetime of the sensor(s). Collectively, this teaches wherein changing the state of the plurality of environmental sensors includes placing the at least one sensor in the plurality of environmental sensors in a quiescent state.]
Regarding Claim 23
Due to its similarity to Claim 20, Claim 23 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 20.
Regarding Claim 21
The system of claim 1, wherein changing the state of the plurality of environment sensors includes changing the state of at least one sensor in the plurality of environmental sensors from a quiescent state to an event or alert state. [Cox teaches at para. [0051] in order to avoid unnecessary use of the one or more sensors, each sensor will (initially) be operated in a mode of operation in which the sensor preserves energy, for example to extend the operational lifetime of a battery-powered sensor. Cox teaches at para. [0051] for example, in case of a primary sensor or monitoring a particular physical condition of a patient, the sensor will be operated at a low data operating or sampling frequency, such as for example an operating frequency of 2 minutes per quarter of an hour for a heart rate monitoring sensor. Cox teaches at para. [0051] in case of the presence of a secondary sensor for monitoring a particular physical state of a patient, e.g., a sensor verifying or supporting the primary sensor for monitoring a particular physical condition of a patient, such a secondary sensor will be switched off. Cox teaches at para. [0051] more generally, the mode of operation of such a sensor will be controlled with the digital twin to strike a balance between the accuracy of the simulations of the patient’s physical condition with the digital twin and the convenience of the patient, e.g., to preserve operational and battery lifetime of the sensor(s). Collectively, this teaches wherein changing the state of the plurality of environment sensors includes changing the state of at least one sensor in the plurality of environmental sensors from a quiescent state to an event or alert state. The alert state is the sensing/functional sensor state.]
Regarding Claim 24
Due to its similarity to Claim 21, Claim 24 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 21.
Regarding Claim 22
The system of claim 1, wherein changing the state of the plurality of environment sensors includes changing the state of at least one sensor in the plurality of environmental sensors, the at least on sensor being a sensor having a type from the group consisting of a motion sensor, a haptic sensor, an audio sensor, a camera, an acceleration sensor, an altitude sensor, a temperature sensor, a humidity sensor. [Cox teaches at para. [0044] such sensors will electrically, mechanically, thermally, chemically or optically measure digital signal and parameters of the patient from which physiological indicators such as temperature, heart rate, blood pressure, blood flow rate, fractional flow reserve, respiration rate, blood chemistry such as blood glucose level, sweat levels, brain activity (EEG), motion, speech, image-based monitoring (e.g., to monitor body regions of the patient) and so on will be calculated or estimated. Cox teaches at para. [0051] this is not limited to reducing the need for the patient to recharge or replace the batteries of the one or more sensors, but will also be intended to limit discomfort to the patient, e.g., by only performing “uncomfortable” measurements such as blood pressure measurements when the digital twin considers such measurements necessary based on the simulated actual or future physical condition of the patient. Cox teaches other non-limiting examples of effects of such sensor measurements potentially perceived as uncomfortable by the patient include pressure changes, temperature variations, irritations to the skin noise, visual changes light), haptic changes (vibration) that will happen as a result of adapting the mode of operation of the sensor, as well as instructions or directions to the patient for trigging the patient to manually adjust a sensor setting, e.g., perform a measurement with a sensor. Collectively this teaches wherein the at least one sensor is a motion sensor and a haptic sensor.]
Regarding Claim 25
Due to its similarity to Claim 22, Claim 25 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 22.
Claims 6-8,18-19 are rejected US 2020/0051679 A1 (hereafter Bostic) in view of US 2020/0185107 A1 (hereafter Cox) in view of US 20180315509 A1 (hereafter Zhang).
Regarding Claim 6
Bostic/Cox teach the system of claim 1. Bostic/Cox further teach:
wherein the changing the state of the at least one of the plurality of environmental sensors alters a monitoring focus of the at least one of the plurality of environmental sensors. [Cox teaches at para. [0044] such sensors will electrically, mechanically, thermally, chemically or optically measure digital signal and parameters of the patient from which physiological indicators such as temperature, heart rate, blood pressure, blood flow rate, fractional flow reserve, respiration rate, blood chemistry such as blood glucose level, sweat levels, brain activity (EEG), motion, speech, image-based monitoring (e.g., to monitor body regions of the patient) and so on will be calculated or estimated. Cox teaches at para. [0051] this is not limited to reducing the need for the patient to recharge or replace the batteries of the one or more sensors, but will also be intended to limit discomfort to the patient, e.g., by only performing “uncomfortable” measurements such as blood pressure measurements when the digital twin considers such measurements necessary based on the simulated actual or future physical condition of the patient. Cox teaches other non-limiting examples of effects of such sensor measurements potentially perceived as uncomfortable by the patient include pressure changes, temperature variations, irritations to the skin noise, visual changes light), haptic changes (vibration) that will happen as a result of adapting the mode of operation of the sensor, as well as instructions or directions to the patient for triggering the patient to manually adjust a sensor setting, e.g., perform a measurement with a sensor. Collectively this teaches wherein the at least one sensor is a motion sensor and a haptic sensor.]
Bostic/Cox may not explicitly teach:
wherein the changing the state of the at least one of the plurality of environmental sensors alters a monitoring focus of the at least one of the plurality of environmental sensors.
Zhang teaches:
wherein the changing the state of the at least one of the plurality of environmental sensors alters a monitoring focus of the at least one of the plurality of environmental sensors. [Zhang teaches at para. [0055] the risk assessment circuit will be configured to adjust one or more thresholds (e.g., CHF thresholds, etc.), individual sensor or parameter weightings to optimize one or more of an event rate or event ratio. Zhang teaches at para. [0055] for example, a heart failure threshold or parameter will be adjusted to minimize an OUT alert state event rate (Rout), to minimize an IN alert state event rate (ERin), or maximize or minimize an event rate ratio (e.g., maximize ERRIN/ERRout, etc.). Zhang teaches at para. [0057] for example, the risk assessment circuit will be configured to adjust a weighting of one or more sensors in a composite CHF risk indicator indicative of a risk of worsening heart failure to optimize an event rate or an event rate ratio of a specific patient or a group of patients, such as to help identify those patients in the group having the highest risk of worsening heart failure, or to determine a risk of worsening heart failure of a specific patient. Collectively this teaches wherein the changing the state of the at least one of the plurality of environmental sensors alters a monitoring focus of the at least one of the plurality of environmental sensors. Optimizing the event rate or event ratio of the sensor is altering the monitoring focus of the sensor.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify healthcare management using digital twins of Bostic to the digital twin operation of Cox to the heart failure event rate assessment of Zhang with the motivation of addressing unique challenges related to event rate assessment for congestive heart failure.
Regarding Claim 7
Bostic/Cox/Zhang teach the system of claim 6. Bostic/Cox/Zhang further teach:
wherein the monitoring focus increases the fidelity or granularity of the at least one of the environmental sensors. [Zhang teaches at para. [0055] the risk assessment circuit will be configured to adjust one or more thresholds (e.g., CHF thresholds, etc.), or individual sensor or parameter weightings to optimize one or more of an event rate or event ratio. Zhang teaches at para. [0055] for example, a heart failure threshold or parameter will be adjusted to minimize an OUT alert state event rate (ERout), to minimize an IN alert state event rate (ERin), or maximize or minimize an event rate ratio (e.g., maximize ERRIN/ERRout, etc.). Zhang teaches at para. [0057] for example, the risk assessment circuit will be configured to adjust a weighting of one or more sensors in a composite CHF risk indicator indicative of a risk of worsening heart failure to optimize an event rate or an event rate ratio of a specific patient or a group of patients, such as to help identify those patients in the group having the highest risk of worsening heart failure, or to determine a risk of worsening heart failure of a specific patient. Collectively this teaches wherein the monitoring focus increases granularity of the at least one of the environmental sensors. The risk assessment circuit is interpreted as the environmental sensor. Optimizing the event rate or event ratio of the sensor is altering the monitoring focus increases granularity of the environmental sensor.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify healthcare management using digital twins of Bostic to the digital twin operation of Cox to the heart failure event rate assessment of Zhang with the motivation of addressing unique challenges related to event rate assessment for congestive heart failure.
Regarding Claim 18 and 19
Due to their similarity to Claims 6 and 7, Claims 18 and 19 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 6 and 7.
Regarding Claim 8
Bostic/Cox/Zhang teach the system of claim 7. Bostic/Cox/Zhang further teach:
wherein the detected data set is from a breathing sensor, or heart-rate sensor. [Bostic teaches at para. [0309] in some embodiments, the platform 100 will receive sensor data from one or more environmental sensor and wearable sensor worn by the patient, store the sensor data at the platform 100, and present the sensor data to the user of the platform 100. Bostic teaches at para. [0309] the environmental sensor and/or wearable sensor will include one or more of sensor implemented on a smartphone, smart glasses, VR headsets, AR glasses, biometrics sensors, pacemakers, heart-rate monitors, blood sugar sensor, or any other suitable type of environmental and/or wearable sensor. The heart rate monitor that is a wearable device is interpreted as a heart rate sensor. Collectively, Bostic teaches wherein the detected data set is from a heart rate sensor.]
Claims 26 are rejected US 2020/0051679 A1 (hereafter Bostic) in view of US 2020/0185107 A1 (hereafter Cox) in view of US 8714983 B2 (hereafter Kil).
Regarding Claim 26
Bostic/Cox teaches the system of claim 13. Bostic/Cox may not explicitly teach:
wherein the game is of a type selected from the group consisting of pattern detection games, pattern matching games, accuracy detection games, speed detection games, cost detection games, balance games, consensus games, risk assessment games, audit games.
Cox teaches the following noted feature:
wherein the […activity…] is of a type selected from the group consisting of pattern detection […activities…], pattern matching […activities…], accuracy detection […activities…], speed detection […activities…], cost detection […activities…], balance […activities…], consensus […activities…], risk assessment […activities…], audit […activities…]. [Cox teaches at para. [0044] such sensors will electrically, mechanically, thermally, chemically or optically measure digital signal and parameters of the patient from which physiological indicators such as temperature, heart rate, blood pressure, blood flow rate, fractional flow reserve, respiration rate, blood chemistry such as blood glucose level, sweat levels, brain activity (EEG), motion, speech, image-based monitoring (e.g., to monitor body regions of the patient) and so on will be calculated or estimated. Cox teaches at para. [0047] alternatively, such monitoring will be performed automatically such that a consult or procedure for the patient is only scheduled when his or her digital twin predicts the imminent occurrence of a critical medical condition or any other change in the physical condition of the patient that ideally requires the patient to be brought face to face with a health care professional. Collectively, this teaches wherein the […activity…] is risk assessment […activities…].]
Kil teaches the following noted feature:
…games…[Kil teaches at the Abstract input data indicative of the health behavior is obtained and converted into a gaming parameter. Collectively, this teaches games.]
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Kil with teaching of Cox since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the gamification of health data input concept of the secondary reference(s) for the risk activity means of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Response to Arguments
35 U.S.C. 101
Applicant disagrees with Step 2A, Prong 1 analysis. The claim as drafted is not directed to “certain methods of organizing human activity” or to any other abstract idea. The claim is directed to a monitoring system, a machine that, based on collected data and predictions of future states, adjusts the operation of various sensors to better control those sensors and thereby better monitor a person under care and their environment. The Office’s position, as best as it can be understood seems to mistakenly think that because the system monitors a patient that somehow the monitoring amounts to a method of organizing human activity. Applicant respectfully disagrees. The system is not managing “personal behavior or interactions between people”, it is instead controlling the operation of a sensor system with a plurality of sensors.
The argument is moot given the response to amendment. The claim is now rejected under mental processes subgrouping.
Applied the way the Office is applying Step 2A prong 1, similar arguments would apply to a stethoscope or a blood pressure cuff. A clinician measuring with a stethoscope or a blood pressure monitor and giving medical advice or predicts future patient pathologies based on reading from those device does not make those devices into a “method of organizing human activity”. This holds true for the collection of data and adjustment of sensors based on analysis of that data. Applicant’s claims are not directed to an abstract idea, and in particular do not recite a method of organizing human activity.
The Examiner partially disagrees. It is possible, depending on the specific operations and how it is claimed, for operations operating on the processor and memory of the stethoscope or blood pressure cuff to be ineligible via an Alice/Mayo analysis.
A clinician measuring with a stethoscope or a blood pressure monitor and giving medical advice or predicting future patient pathologies based on reading from those device does not make those devices into a “method of organizing human activity”, however, if recited positively, the interaction between the patient and clinician that is necessary when measuring blood pressure with a cuff may very well be certain methods of organizing human activity.
Regardless, the Examiner cannot anticipate the exact way in which these hypothetical features, steps, and/or limitations are claimed.
Applicant argues that the analysis under Step 2A prong 2 is also flawed. The claim is not simply a general purpose transceiver, storage medium, and processor, as implied by the Office Action’s analysis, but includes numerous other recited feature which, when taken as a whole, integrate any alleged abstract idea into a practical application consistent with the Alice-Mayo analysis. Applicant disagrees with the statement that the claims are not meaningfully limited and suggests that this statement ignores almost the entire text of Applicant’s claims.
Notice this was not the only statement that was made in that rejection. Please see the updated subject matter eligibility rejection. Applicant’s invention was and is addressed in full. Where specific arguments are made concerning how the features of the invention, which taken as a whole, supposedly integrate the alleged abstract idea into a practical application consistent with the Alice-Mayo analysis, the Examiner has responded.
Applicant argues that the claims improves another technology or technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environments, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not “directed to” the recited judicial exception.
See relevant response to this redundant question below. Notice that here no actual examples are given to support the argument. The Examiner has responded where specific arguments have been applied to the subject matter.
In particular, claim 1 recite using a digital twin model to change the way the sensors operate, improving monitoring efficacy and reducing risk for the patient when such improved monitoring is shown by the digital twin analysis to be appropriate. The claim does not monopolize the field; any other method of tuning or adjusting sensors could be used, and digital twins could be used for other than the recited purposes. Applicant argues that the claimed invention improves performance of a sensor system in a manger analogous to improving the performance of a computer system. By using the digital twin, and altering sensor configurations based on that analysis, false positives or other sensor errors can be avoided, making the sensor system more effective. This problem of false positives is described in paragraph [000024] of the published specification:
…where a sensor indicates an event that could represent the PUM being in danger, for example having a fall, when in fact the PUM simply tripped with no ill effects. This often causes false positives with attendant costs, stress and unnecessary resource deployment.
In sum, the features and steps of the claim that are resulting in the supposed improvement are not, to one of ordinary skill in the art, positively recited in the claim.
As currently recited, the digital twin is part of the abstract idea. It is not apparent that this specific usage of the digital twin is linked to the claimed improvement. Note that Applicant’s argument, if valid, could apply to hypothetical situation of using the digital twin in any abstract idea resulting in the alteration of sensors to demonstrate an improvement. In this regard, the specific abstract idea including the digital twin (even as an additional element, as described) cannot be said to be improving the sensor technology because a technical improvement is not recited. An abstract idea is recited with instructions to implement the abstract idea on the computer. Regardless, even taken as an additional element, the digital twin is generally linking to a particular technological environment only. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
Moreover, false positives with attendant costs, stress and unnecessary resource deployment are impacted by a variety of factors, including factors that are other than technical in nature.
Additionally, the claims preempt the use of digital twins in the sensor field, a factor which is symptomatic of ineligibility as determined through the Alice/Mayo subject matter eligibility analysis.
The claimed system helps solve this problem of false positives and improving sensor accuracy, as pointed out in paragraph [0095] of the published specification: Such an approach can reduce false positives and extraneous sensor data
See response above.
Applicant argues that the Office does not provide the required level of analysis needed for a prima facie section 101 rejection, or indeed any analysis at all. The Office simply says “Claim x merely recites “ for each claim. There is no explanation why any of these features are not practical applications that are outside of the scope of Section 101.
The statement that the Office has failed to provide any analysis at all is incorrect. Please see the rejections issued. The formatting of the rejections cited by Applicant, of the dependent claims, is intended to reflect that the recited limitations represent a refinement to the abstract idea upon which the claims depend without a practical application and significantly more. Notice, that the additional elements of the dependent claims were analyzed (it was explained why these features/steps/limitations could not provide a practical application and significantly more).
For example, it was stated that the dependent claims contain a variety of additional elements including at least one processor, environmental sensors, breathing sensor, heart rate sensor and encryption key. The processor was analyzed the same as the computer and/or computer part(s) in the independent claim and does not provide a practical application or significantly more for the same reasons.
It was also stated in the rejection that the sensors (and the encryption key, as was previously recited before being canceled) are generally linking the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
Finally, see the new 35 U.S.C. 101 rejection with responses to additional elements added through amendment.
35 U.S.C. 103
Applicant respectfully submits that this neither teaches nor suggests the recited “using the digital twin to identify potential future states of the person under care and, based on the evaluation of the digital twin to optimize a set of configurations of the plurality of environmental sensors.”
The Examiner disagrees. Bostic teaches “…using the digital twin to identify potential future states of the person under care…” Bostic teaches at para. [0369] simulating, at the healthcare data system computing device, a future health state of said patient based on the digital twin of said patient using the digital twin of said patient and the machine learning module;….
and based on evaluation of the digital twin, to optimize a set of configurations of the plurality of environmental sensors; and change a state of at least one of the plurality of environmental sensors. [Cox teaches at the Abstract disclosed is a computer system comprising a processor arrangement communicatively coupled to a data storage arrangement storing a virtual model of a patient, said virtual model comprising at least one of a digital representation of at least a part of the anatomy of the patient and a physiological model of a bodily process of the patient; and a communication module communicatively coupled to said processor arrangement and arrange to receive sensor data from one or more sensors arranged to monitor said patient, wherein the processor arrangement is arranged to retrieve said virtual model from the data storage arrangement; receive said sensor data from the communication module; generate an instruction for altering a mode of operation of at least one sensor of the one or more sensors in response to said evaluation or in response to a user request; and transmit said instruction to the at least one sensor or to a device for invoking control of said at least one sensor with the communication module. Cox teaches at para. [0048] the actual sensor data will be used to validate such a prediction, e.g., by using the actual sensor data to simulate the physical condition of the patient at the same point in time, e.g. a point in time in the future or the actual point in time, and will be used to update the digital twin if necessary, e.g. if such a validation highlights a discrepancy between the simulated physical conditions using the ‘old’ and ‘new’ sensor data respectively.]
Bostic also does not change the state of an environmental sensor in the manner claimed based on an analysis of a digital twin. The lab tests taught in Bostic do not employ environmental sensors in the manner recited by claim 1, and changing the operation of a lab testing center does not teach or suggest optimizing a set of configurations of the environmental sensors.
Cox teaches the limitation. Cox is newly added. See the updated rejection and the response above.
As amended, Bostic’s testing lab does not teach or suggest the recited sensors, which are located in the environment where a person under care is located, not at some other location, such as a testing lab.
The Examiner disagrees. Bostic teaches at para. [0309] in some embodiments, the platform 100 will receive sensor data from one or more environmental sensor and wearable sensor worn by the patient, store the sensor data at the platform 100, and present the sensor data to the user of the platform 100. The environmental sensor and the wearable sensor worn by the patient are in the environment where a person under care is located. Bostic teaches at para. [0309] the environmental sensor and/or wearable sensor will include one or more of sensor implemented on a smartphone, smart glasses, VR headsets, AR glasses, biometrics sensors, pacemakers, heart-rate monitors, blood sugar sensor, or any other suitable type of environmental and/or wearable sensor.
Moreover, the Office admits that Bostic does not teach or suggest a digital twin comprising a dynamic tokenized representation… of a health care profile of the person under care in the environment, and relies on a combination with Gross.
Respectfully, as for the Office admitting something, the rejection says the Bostic may not explicitly teach certain limitations it does not absolutely indicate that Bostic cannot teach a certain limitation.
Moreover, the Examiner disagrees. Please see the updated 35 U.S.C. 103 rejection for line by line explanations of how the actual recited limitations are taught. To answer the question, however, Bostic teaches at para. [0310] by combining one or more of the digital twins of the patient and the digital twin of the population of patients, the health information, the healthcare research information, and the sensor data via the machine learning module, the machine learning module may formulate one or more very specific and precise personalized treatment plans particularly suited to the patient. The very specific and precise personalized treatment plans particularly suited to the patient are the health care profile of the person under care in the environment.
While Gross employs a form digital twinning, Gross does not perform the prediction of future states of the person under monitoring as recited by amended claim 1, instead using its token for cryptographic authentication. See Gross at [0098]. Gross therefore fails to cure the deficiencies of Bostic.
The argument is moot. The rejection has been updated at new prior art has been used to teach the limitation and Gross has been eliminated. Please see the updated 35 U.S.C. 103 rejection.
Gross is directed to the fields of access control and data custody, not to any sort of sensor optimization. This is a significantly different field of endeavor to that of Bostic, which monitors lab testing. Applicant therefore submits that the Office’s combination of Bostic and Gross is improper and respectfully requests withdrawal of the prior art rejections made on the basis of the combination of Bostic and Gross.
The argument is moot. The rejection has been updated at new prior art has been used to teach the limitation and Gross has been eliminated. Please see the updated 35 U.S.C. 103 rejection.
Moreover, neither Bostic, nor Gross, nor Poon teach nor suggest changing the state of an environmental sensor based on any sort of digital twin analysis. The office admits this in the rejection of dependent claim 6. Peters is added to allegedly teach this feature. However, there is no express discussion in Peters about changing the state of sensors, or in fact any discussion of altering sensor operation at all. Peters only discusses using different models, not changing sensor configurations or a sensor’s operating state, as recited in amended claim 1.
Again, respectfully, the Office does not admit this, see the phrasing of the rejection which indicates the feature may not be in Bostic. Regardless, the argument is moot Peters has been replaced.
Applicant thus respectfully submits that Peters fails to cure the deficiencies of Bostic, Gross, and Poon admitted by the Office. Accordingly, for the above reasons, Applicant submits that claim 1 should be allowable over Bostic & Gross, and over Bostic, Gross, Poon and Peters, even if the combination were proper.
Even if the 35 U.S.C. 103 rejection were overcome, Applicant still has not overcome the 35 U.S.C. 101 rejection, so, if the combination were not proper, nothing would be allowable.
Regardless, the argument is moot as all the relevant art has been replaced.
Claim 13 has been amended in a manner similar to claim 1 and is allowable for at least similar reasons. Moreover, the cited references do not teach nor suggest, and the Office does not identify, any use of game theory at all, a strategy for a game, or determining a strategy equilibrium as recited in Applicant’s claim 13. Claim 13 should therefore be allowable for at least this additional reason. The Office, at page 12, say “the generation of the improvement plan for the testing lab that involves notifying a user of a specific test is interpreted as a game to optimize the set of configuration of the environmental sensors”. Applicant respectfully disagrees. The Office has not identified any use of game theory, a strategy for a game, or determining a strategy equilibrium as recited in Applicant’s independent claim 13.
The argument is moot, the relevant art has been replaced. See the updated rejection.
Accepting for arguments sake, that the reference cited by the Examiner uses a model for optimization that does not teach or suggest the use of game theory. All models, an even all optimization models are not inherently games and the results of optimization models are not necessarily strategies or strategy equilibria. As pointed out in Applicant’s specification, the use of game theory will have advantages over other approaches – it will better handle unanticipated use cases (see specification paragraph [0228], balance differing incentives from different actors (see specification paragraph [0227]), and improve flexibility and responsiveness (see, specification paragraph [0090]).
These arguments are moot the relevant art has been replaced.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rodolfo S. Antunes, Lucas A. Seewald, Vinicius F. Rodrigues, Cristiano A. Da Costa, Luiz Gonzaga Jr., Rodrigo R. Righi, Andreas Maier, Björn Eskofier, Malte Ollenschläger, Farzad Naderi, Rebecca Fahrig, Sebastian Bauer, Sigrun Klein, and Gelson Campanatti. 2018. A Survey of Sensors in Healthcare Workflow Monitoring. ACM Comput. Surv. 51, 2, Article 42 (March 2019), 37 pages. Antunes teaches the general state of sensor technology in healthcare workflow monitoring.
M. Haghi et al., "A Flexible and Pervasive IoT-Based Healthcare Platform for Physiological and Environmental Parameters Monitoring," in IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5628-5647, June 2020, doi: 10.1109/JIOT.2020.2980432. Haghi teaches receiving data from both environmental and physiological sensors.
WO 2024/035852 A1 (hereafter Murphy) teaches a process for providing digital twins by parsing a dataset into a tabulated format and forming multiple silos from the dataset involved exemplary personas being constructed with machine learning, artificial intelligence, stored procedures, and unique specialized databasing to generate reporting, modify data, and socialize with other personas in agreed upon spaces to create control within a three-dimensional world in IoT networks.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683