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 . This non-final office action on merits is in response to the Patent Application filed on 02/08/2024.
Status of claims
Claims 4, 6-10, 12, 17, 21, 23-29, 32, 34-51, 54-63, 65-66, and 68 are cancelled. Amendments to claims 3, 5, 11, 16, 20, 22, 31, 33, 53, and 67 are acknowledged and have been carefully considered. Claims 1-3, 5, 11, 13-16, 18-20, 22, 30-31, 33, 52-53, 64 and 67 are pending and considered below. This application is a 371 of PCT/US2022/039920, filed on 08/10/2022, claiming priority to Provisional Application No. 63/231460, filed on 08/10/2021.
Information Disclosure Statement
The information disclosure statements (IDSs) filed on 02/08/2024, 09/23/2024, and 06/13/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 5, 11, 13-16, 18-20, 22, 30-31, 33, 52-53, 64 and 67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-3, 5, 11, 13-16 are drawn to a computing device, and claims 18-20, 22, 30-31, 33 are drawn to a method, and claims 52-53, 64 and 67 are drawn to a non-transitory processor-readable medium. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. 101.
Step 2A Prong One
Claim 1 recites the limitation of monitoring communications sent to a care provider communication device to identify a plurality of overload factors and determining whether the plurality of overload factors meets an overload condition for the care provider, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind or by using a pen and paper but for the recitation of generic computer components. That is, other than reciting “a memory; and a processor coupled to the memory and configured with processor-executable instructions” nothing in the claim element precludes the limitation from practically being performed in the mind or by a human using a pen and paper. For example, but for the “a memory; and a processor coupled to the memory and configured with processor-executable instructions” language, the limitations in the context of this claim encompasses the user manually tracking messages received by a care provider, recording behavioral indicators (i.e., delayed responses), evaluating whether the care provider appears overloaded, and deciding to reduce task assignment or communication volume. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or by using a pen and paper 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.
Claim 1 recites as a whole a method of organizing human activity (i.e., managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions)) because the claim recites a method that allows users to perform a mitigation operation in response to determining that the plurality of overload factors meets the overload condition for the care provider. This is a method of managing interpersonal and workplace interactions between healthcare professionals to reduce cognitive load or stress and optimize task assignments. The mere nominal recitation of a generic a memory and a processor coupled to the memory and configured with processor-executable instructions does not take the claim out of the methods of performing a mitigation operation in response to determining that the plurality of overload factors meets the overload condition for the care provider. Thus, the claim recites an abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Independent claims 18 and 52 recite identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this claim is therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per method claim 1, include:
a memory; and a processor coupled to the memory and configured with processor-executable instructions to:
monitor communications sent to a care provider communication device to identify a plurality of overload factors;
determine whether the plurality of overload factors meets an overload condition for the care provider; and
perform a mitigation operation in response to determining that the plurality of overload factors meets the overload condition for the care provider.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of monitoring and evaluating caregiver communications and workload to mitigate overload in a computer environment. The claimed computer components (i.e., a memory and a processor coupled to the memory and configured with processor-executable instructions) are recited at a high level of generality and are merely invoked as tools to perform an existing process of identifying behavioral or communication patterns, evaluating whether a person is overloaded, and deciding whether to adjust tasks or communications accordingly, which are managed in healthcare settings. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of monitoring and evaluating caregiver communications and workload to mitigate overload in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is not patent eligible.
Claims 11, 19, 20, 22, 30, and 31 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 2, 3, 5, 13, 14, 15, 16, 33, 53, 64, and 67 recite the additional element of the processor (claims 2, 3, 5, 13, 14, 15) and sending information to a network computing device (claims 16, 33, and 67), the processing device (claims 53, 64, and 67). However, this additional element amounts to implementing an abstract idea on a generic computing device or mere data gathering (i.e., an insignificant extra-solution activity). As such, this additional element, when considered individually or in combination with the prior devices, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
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.
Claims 1-3, 5, 11, 13-16, 18-20, 22, 30-31, 33, 52-53, 64 and 67 are rejected under 35 U.S.C. 103 as being unpatentable over Rajasenan (U.S. Patent Publication 2015/0248532 A1), referred to hereinafter as Rajasenan, in view of Faulks et al. (U.S. Patent Publication 2021/0174948 A1), referred to hereinafter as Faulks.
Regarding claim 1, Rajasenan teaches a computing device (Rajasenan [0146] “As shown in FIG. 11, computer 1110 includes a processor 1112 that is coupled to an interconnection bus 1114. Processor 1112 may be any suitable processor, processing unit or microprocessor. Although not shown in FIG. 11, system 1110 may be a multi-processor system and, thus, may include one or more additional processors that are identical or similar to processor 1112 and that are communicatively coupled to interconnection bus 1114.”), comprising:
a memory; and a processor coupled to the memory and configured with processor-executable instructions to (Rajasenan [0147] Processor 1112 of FIG. 11 is coupled to a chipset 1118, which includes a memory controller 1120 and an input/output ("I/O") controller 1122. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 1118. The memory controller 1120 performs functions that enable the processor 1112 (or processors if there are multiple processors) to access a system memory 1124 and a mass storage memory 1120.”):
communications sent to a care provider communication device to identify a plurality of overload factors (Rajasenan [0127]“Managed alerts (or messages) can be sent to healthcare providers using personal communication devices, such as smart phones, tablets, pagers, and any other personal communication device to allocate tasks in a real-time or near real-time basis to avoid tipping points. Thus, when a system according to an embodiment determines that a particular healthcare provider is to be assigned to complete a new task, a message (or alert) is sent to a personal communication device associated with the particular healthcare provider to advise him or her of the new task to be performed. Similarly, if tipping point analysis reveals that a healthcare provider has, or is about to, reach a tipping point, the system may offload one or more tasks from the healthcare provider, and notify the healthcare provider that he or she no longer has to perform those tasks via a message (or alert) sent to the personal communication device associated with the healthcare provider.”);
determine whether the plurality of overload factors meets an overload condition for the care provider (Rajasenan [0064] “In an embodiment, tipping point analysis is used to determine which tasks a particular person, such as a healthcare provider, has problems completing as their cognitive load increases. In an embodiment, this is done by process mining historical data of an entity to determine the cognitive load when each individual in the entity begins to experience task failure. For example, in the healthcare context, a particular diagnosis may required 5 tasks be done by a particular doctor. However, process mining and data analysis shows that the particular doctor has problems completing one of the 5 tasks when his cognitive load exceeds a certain percentage. For example, for a patient presenting a CHF case, the doctor may have difficulty completing task 3 of a checklist for CHF treatment when his or her cognitive load exceeds 75%, or a certain number of concurrent tasks”); and
perform a mitigation operation in response to determining that the plurality of overload factors meets the overload condition for the care provider (Rajasenan [0061] “Further, embodiments consider healthcare provider team activation. Healthcare provider team activation occurs when a particular healthcare provider has reached a tipping point and requires intervention to avoid failing to perform tasks at risk. Process mining on incoming data using TAR-ILTA to determine opportunity ensures the highest value for teams activated (and interrupted, as interruptions contribute to cognitive overload as well) and the time they will spend on the intervention. In an embodiment, this is determined by the number and type of tasks that the particular healthcare provider is performing. When the healthcare provider reaches a tipping point, additional members of the healthcare team can be activated to perform a countermeasure to avoid the tipping point. Such countermeasures include double checking tasks and quality assurance, offloading the task-at-risk to another team members (that could also be at another place, using telecommunications or tele-health), offloading tasks other than the task-at-risk to other team members (to get team member below their tipping point), performing the task at a later time. Activation of teams is the key--how many tasks must be done by someone before they can and should activate a team to initiate a countermeasure. Then once activated, the key question the scoring model must address is how many tasks must be done in order to complete the countermeasure and thus be effective in the prevention. Devices that can identify the location of a person, such as smart phones, tracking tools, etc., help in knowing exactly where a person is can drive their TAR--if they are further from the patient, then that could mean being rushed (so overload, anxiety, etc.) and/or that they do not have time to complete an intervention. Thus, this could be added to the TAR-ILTA scoring model.”).
Rajasenan fails to explicitly teach monitor communications sent to a care provider communication device.
Faulks teaches monitor communications sent to a care provider communication device (Faulks [0092] “In contemplated embodiments, the mobile caregiver application is configured to allow caregivers in an acute care setting to use their mobile phones 52 for monitoring alerts and calls from patients; for conducting voice, video, and text messaging between caregivers; and for permitting voice communications to audio stations (e.g., standard audio stations 62 and/or graphical audio stations 64) mounted in patient rooms adjacent to respective patient beds 54. The mobile caregiver application is also configured to act as a secondary notification system that supplements the nurse call system portion of system 50.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to combine Rajasenan’s overload detection and task reallocation system with Faulks’ communication monitoring features in order to improve real time detection and mitigation of care provider overload, as it represents a predictable integration of known technologies for enhancing responsiveness and provider safety in medical task management systems.
Regarding claim 2, Rajasenan and Faulks teach the invention in claim 1, as discussed above, and further teach wherein the processor is further configured with processor-executable instructions such that the plurality of overload factors includes one or more interrupt events (Rajasenan [0147] Processor 1112 of FIG. 11 is coupled to a chipset 1118, which includes a memory controller 1120 and an input/output ("I/O") controller 1122. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 1118. The memory controller 1120 performs functions that enable the processor 1112 (or processors if there are multiple processors) to access a system memory 1124 and a mass storage memory 1120.” and Rajasenan [0127]“Managed alerts (or messages) can be sent to healthcare providers using personal communication devices, such as smart phones, tablets, pagers, and any other personal communication device to allocate tasks in a real-time or near real-time basis to avoid tipping points. Thus, when a system according to an embodiment determines that a particular healthcare provider is to be assigned to complete a new task, a message (or alert) is sent to a personal communication device associated with the particular healthcare provider to advise him or her of the new task to be performed. Similarly, if tipping point analysis reveals that a healthcare provider has, or is about to, reach a tipping point, the system may offload one or more tasks from the healthcare provider, and notify the healthcare provider that he or she no longer has to perform those tasks via a message (or alert) sent to the personal communication device associated with the healthcare provider.”), wherein each interrupt event is associated with a severity metric (Faulks [0021] “The processor may receive the first alert message and may determine a message priority designation from a first priority designation or a second priority designation. The processor may forward the alert message along with the message priority designation for receipt by the server which then may forward the alert message and the priority designation to at least one mobile device of a caregiver assigned to the patient. The plurality of mobile devices may be configured to permit the caregivers to accept responsibility for responding to respective alert messages or to re-route respective alert messages to one or more other caregivers.” and [0094] “The processor of I/O circuitry 68 determines an alert message priority designation for each of the incoming alert messages. For example, in the illustrative embodiment, alert messages are designated as either Normal alert messages or High Priority alert messages. However, in other embodiments, more than two alert message priority designations may be used.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to combine Faulks’ priority designations for alerts with Rajasenan’s interrupt event system to improve task management by differentiating the urgency of overload related alerts, as this is a predictable in the field of healthcare communication systems.
Regarding claim 3, Rajasenan and Faulks teach the invention in claim 2, as discussed above, and further teach wherein the processor is further configured with processor-executable to associate with each interrupt event (Rajasenan [0147] Processor 1112 of FIG. 11 is coupled to a chipset 1118, which includes a memory controller 1120 and an input/output ("I/O") controller 1122. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 1118. The memory controller 1120 performs functions that enable the processor 1112 (or processors if there are multiple processors) to access a system memory 1124 and a mass storage memory 1120.” and Rajasenan [0127]“Managed alerts (or messages) can be sent to healthcare providers using personal communication devices, such as smart phones, tablets, pagers, and any other personal communication device to allocate tasks in a real-time or near real-time basis to avoid tipping points. Thus, when a system according to an embodiment determines that a particular healthcare provider is to be assigned to complete a new task, a message (or alert) is sent to a personal communication device associated with the particular healthcare provider to advise him or her of the new task to be performed. Similarly, if tipping point analysis reveals that a healthcare provider has, or is about to, reach a tipping point, the system may offload one or more tasks from the healthcare provider, and notify the healthcare provider that he or she no longer has to perform those tasks via a message (or alert) sent to the personal communication device associated with the healthcare provider.”) at least one metric selected from the group consisting of
(i) a congruity metric indicating a level of congruity of each interrupt event relative to one or more other interrupt events (Rajasenan [0114] “Support (i.e., flexible resource) cognitive healthcare provider saturation is whether the support healthcare providers themselves are in cognitive overload due to task saturation. This can include how many resident physician or mid-level provider (MLP) "options" there are to distribute fungible tasks, what the time of day or day of week (which can indicate quality or administrative staff availability), the team member schedules (for Nurses, MLPS, residents, etc.), and most importantly the current level of cognitive load that the cavalry team members are currently confronting from their routine work as well as these exceptions for TAR they would be asked to do.” and Rajasenan [0063] “When cognitive capacity is considered, simply providing more training may not be sufficient to avoid cognitive overloads. That is, training itself may not solve cognitive overloads. In fact, training may contribute to cognitive overloads where an individual becomes overwhelmed such that he or she is not capable of absorbing additional information. For example, in the healthcare context, simply providing additional training to healthcare providers may not allow them to perform better where they are already stretched too thin. Forcing the healthcare providers to pursue additional training may leave them insufficient time to do other required tasks. This in turn leads to stress and distraction and, as a result, a higher likelihood of task failure, whether tasks are not performed at all, or tasks are not performed adequately. Similarly, patients may be incapable of absorbing all information related to their care for a number of reasons including, pain, medication, and stress. Thus, time and money can be saved by avoiding training where it will exacerbate cognitive overload.”)and (ii) an acuity metric indicating a level of acuity of the interrupt event (Rajasenan [0059] “Note that cognitive overload is more likely when there are 2 very complex (and thus high cognitive load patients) than when it is 2 simple patients that will take little time and intellectual effort. Patient at risk identification can help distinguish between these 2 scenarios.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to associate interrupt events in Rajasenan’s system with congruity and acuity metrics to better quantify and manage care provider overload, as these metrics are known tools that would predictably improve decision making in evolving workload management systems.
Regarding claim 5, Rajasenan and Faulks teach the invention in claim 1, as discussed above, and further teach wherein monitoring communications sent to a care provider communication device to identify a plurality of overload factors (Rajasenan [0127]“Managed alerts (or messages) can be sent to healthcare providers using personal communication devices, such as smart phones, tablets, pagers, and any other personal communication device to allocate tasks in a real-time or near real-time basis to avoid tipping points. Thus, when a system according to an embodiment determines that a particular healthcare provider is to be assigned to complete a new task, a message (or alert) is sent to a personal communication device associated with the particular healthcare provider to advise him or her of the new task to be performed. Similarly, if tipping point analysis reveals that a healthcare provider has, or is about to, reach a tipping point, the system may offload one or more tasks from the healthcare provider, and notify the healthcare provider that he or she no longer has to perform those tasks via a message (or alert) sent to the personal communication device associated with the healthcare provider.”) the processor comprises at least one of (i) determining response times for receiving a response from a care provider communication device to communications, (ii) monitoring communications that are ignored or declined by the care provider communication device, (iii) monitoring communications that are responded to with a do not disturb instruction, (iv) monitoring a number of communications that have not been answered, (v) monitoring how long the care provider has been working, or (vi) determining intervals between communications to or from the care provider communication device (Faulks [0018] “In some embodiments, a Busy screen may be displayed on the mobile device and the caregiver's availability may be set to busy in response to the caregiver entering a patient room. If desired, the Busy screen may include a selectable icon that, in response to being selected, may result in the caregiver's availability being set to a do not disturb mode in which the caregiver may be unavailable. In some embodiments, an Available screen may be displayed on the mobile device and the caregiver's availability may be set to available in response to the caregiver being located in a common area of the healthcare facility outside of patient rooms. Optionally, the Available screen may include a selectable icon that, in response to being selected, may result in the caregiver's availability being set to a do not disturb mode in which the caregiver is unavailable.” Faulks [0019] “It is contemplated by this disclosure that the plurality of instructions, in response to being executed, may result in the mobile device of the caregiver displaying on the display screen of the mobile device a first selectable icon that, in response to being selected, may result in the caregiver's availability being set to a do not disturb mode in which the caregiver is unavailable. The mobile device may be configured to not receive alerts when the caregiver's availability is set to the do not disturb mode. The do not disturb mode may last for a threshold period of time and then automatically may expire after the threshold period of time unless action is taken to terminate the do not disturb mode early or extend the do not disturb mode for additional time.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to to incorporate Faulks’ “do not disturb” mode monitoring into Rajasenan’s system to improve overload detection and improve alert delivery based on care provider availability, representing a predictable combination of compatible healthcare communication technologies.
Regarding claim 11, Rajasenan and Faulks teach the invention in claim 1, as discussed above, and further teach wherein determining whether the plurality of overload factors meets an overload condition for the care provider comprises determining whether the plurality of overload factors modulated by one or more care provider skill factors or by a health condition of the care provider meets the overload condition (Rajasenan [0064] “In an embodiment, tipping point analysis is used to determine which tasks a particular person, such as a healthcare provider, has problems completing as their cognitive load increases. In an embodiment, this is done by process mining historical data of an entity to determine the cognitive load when each individual in the entity begins to experience task failure. For example, in the healthcare context, a particular diagnosis may required 5 tasks be done by a particular doctor. However, process mining and data analysis shows that the particular doctor has problems completing one of the 5 tasks when his cognitive load exceeds a certain percentage. For example, for a patient presenting a CHF case, the doctor may have difficulty completing task 3 of a checklist for CHF treatment when his or her cognitive load exceeds 75%, or a certain number of concurrent tasks” and Rajasenan [0063] “When cognitive capacity is considered, simply providing more training may not be sufficient to avoid cognitive overloads. That is, training itself may not solve cognitive overloads. In fact, training may contribute to cognitive overloads where an individual becomes overwhelmed such that he or she is not capable of absorbing additional information. For example, in the healthcare context, simply providing additional training to healthcare providers may not allow them to perform better where they are already stretched too thin. Forcing the healthcare providers to pursue additional training may leave them insufficient time to do other required tasks. This in turn leads to stress and distraction and, as a result, a higher likelihood of task failure, whether tasks are not performed at all, or tasks are not performed adequately. Similarly, patients may be incapable of absorbing all information related to their care for a number of reasons including, pain, medication, and stress. Thus, time and money can be saved by avoiding training where it will exacerbate cognitive overload.” and [0099] Rajasenan “These determinations of fungibility can be from persons stating the rules of what they can, and are willing to, transfer to others experiencing overload, or can be determined from historical data analysis that shows past transfer of certain tasks, and to whom, thus enabling us to determine how likely a task can be transferred, and to what type of role (based on the persons, like nurses, clerical staff, etc.) or specific persons.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement modulation of overload factor thresholds using individual care provider characteristics such as skill or cognitive capacity, as this would predictably enhance the personalization of overload detection and task allocation in systems of Rajasenan.
Regarding claim 13, Rajasenan and Faulks teach the invention in claim 1, as discussed above, and further teach wherein the processor is further configured with processor-executable instructions to apply the plurality of overload factors to an overload condition model that is configured to provide as an output whether the plurality of overload factors meets the overload condition (Rajasenan [0115] “Scoring model 620 can store additional metrics for determining the task at risk opportunity score. For example, in an embodiment, scoring model 620 stores information as to whether a prior healthcare layer (the step in the healthcare continuum in which the patient is currently receiving treatment) experienced a cognitive overload. If a prior healthcare layer experienced a cognitive overload, it is more likely a task was not performed correctly, if at all. Such information would be used to increase the total TAR opportunity score.” and Rajasenan [0118] “Once the factors are determined, in step 608 they are processed to determine a TAR opportunity score. The TAR opportunity score is basically a combination of the factors that predicts whether a task at risk is likely to fail and, if so, whether there is additional support available to provide an effective countermeasure to avoid failure of that task. In an embodiment, for example, a weighting of the factors is used to determine a TAR opportunity score. For example, average task value provides the value of the task. The current healthcare team's saturation level and cognitive bandwidth needed determine whether the current team has sufficient cognitive bandwidth to perform the task or whether additional support is required to prevent failure. The support team saturation level and cognitive bandwidth needed determine whether the support team can handle task offload to prevent failure of the task. Using weightings these values can be combined to derive a TAR opportunity score. The higher the TAR opportunity score, the better the opportunity that failure of tasks at risk can be avoided by implementing various countermeasures.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement a model that determines overload conditions using weighted overload related inputs, as taught by Rajasenan, since such modeling is a predictable to enable overload detection and clinical decision making.
Regarding claim 14, Rajasenan and Faulks teach the invention in claim 13, as discussed above, and further teach wherein the processor is further configured with processor-executable instructions to apply one or more care provider skill factors to the overload condition model, wherein the overload condition model is configured to provide as an output whether the plurality of overload factors modulated by the skill factors meets the overload condition (Rajasenan [0115] “Scoring model 620 can store additional metrics for determining the task at risk opportunity score. For example, in an embodiment, scoring model 620 stores information as to whether a prior healthcare layer (the step in the healthcare continuum in which the patient is currently receiving treatment) experienced a cognitive overload. If a prior healthcare layer experienced a cognitive overload, it is more likely a task was not performed correctly, if at all. Such information would be used to increase the total TAR opportunity score.”, Rajasenan [0118] “Once the factors are determined, in step 608 they are processed to determine a TAR opportunity score. The TAR opportunity score is basically a combination of the factors that predicts whether a task at risk is likely to fail and, if so, whether there is additional support available to provide an effective countermeasure to avoid failure of that task. In an embodiment, for example, a weighting of the factors is used to determine a TAR opportunity score. For example, average task value provides the value of the task. The current healthcare team's saturation level and cognitive bandwidth needed determine whether the current team has sufficient cognitive bandwidth to perform the task or whether additional support is required to prevent failure. The support team saturation level and cognitive bandwidth needed determine whether the support team can handle task offload to prevent failure of the task. Using weightings these values can be combined to derive a TAR opportunity score. The higher the TAR opportunity score, the better the opportunity that failure of tasks at risk can be avoided by implementing various countermeasures.”, Rajasenan [0063] “When cognitive capacity is considered, simply providing more training may not be sufficient to avoid cognitive overloads. That is, training itself may not solve cognitive overloads. In fact, training may contribute to cognitive overloads where an individual becomes overwhelmed such that he or she is not capable of absorbing additional information. For example, in the healthcare context, simply providing additional training to healthcare providers may not allow them to perform better where they are already stretched too thin. Forcing the healthcare providers to pursue additional training may leave them insufficient time to do other required tasks. This in turn leads to stress and distraction and, as a result, a higher likelihood of task failure, whether tasks are not performed at all, or tasks are not performed adequately. Similarly, patients may be incapable of absorbing all information related to their care for a number of reasons including, pain, medication, and stress. Thus, time and money can be saved by avoiding training where it will exacerbate cognitive overload.” and [0099] Rajasenan “These determinations of fungibility can be from persons stating the rules of what they can, and are willing to, transfer to others experiencing overload, or can be determined from historical data analysis that shows past transfer of certain tasks, and to whom, thus enabling us to determine how likely a task can be transferred, and to what type of role (based on the persons, like nurses, clerical staff, etc.) or specific persons.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to apply care provider skill factors as modifiers within an overload condition model, as doing this would improve individualized prediction and response to overload, based on Rajasenan’s disclosures of skill based overload assessment and task suitability.
Regarding claim 15, Rajasenan and Faulks teach the invention in claim 13, as discussed above, and further teach wherein the processor is further configured with processor-executable instructions to apply a health condition of the care provider to the overload condition model, wherein the overload condition model is configured to provide as an output whether the plurality of overload factors modulated by the health condition of the care provider meets the overload condition (Rajasenan [0115] “Scoring model 620 can store additional metrics for determining the task at risk opportunity score. For example, in an embodiment, scoring model 620 stores information as to whether a prior healthcare layer (the step in the healthcare continuum in which the patient is currently receiving treatment) experienced a cognitive overload. If a prior healthcare layer experienced a cognitive overload, it is more likely a task was not performed correctly, if at all. Such information would be used to increase the total TAR opportunity score.”, Rajasenan [0118] “Once the factors are determined, in step 608 they are processed to determine a TAR opportunity score. The TAR opportunity score is basically a combination of the factors that predicts whether a task at risk is likely to fail and, if so, whether there is additional support available to provide an effective countermeasure to avoid failure of that task. In an embodiment, for example, a weighting of the factors is used to determine a TAR opportunity score. For example, average task value provides the value of the task. The current healthcare team's saturation level and cognitive bandwidth needed determine whether the current team has sufficient cognitive bandwidth to perform the task or whether additional support is required to prevent failure. The support team saturation level and cognitive bandwidth needed determine whether the support team can handle task offload to prevent failure of the task. Using weightings these values can be combined to derive a TAR opportunity score. The higher the TAR opportunity score, the better the opportunity that failure of tasks at risk can be avoided by implementing various countermeasures.”, and Rajasenan [0161] Healthcare supply module 1408 determines the available healthcare supply that can be used to handle the tasks. In an embodiment healthcare supply can be determined based on a number of factors including healthcare providers and their capabilities, and available healthcare resources, including for example, available equipment, rooms, and other facilities. In an embodiment, real time analysis of healthcare provider physical attributes provided by biometric sensors 1410. The real time biometric data is used in an embodiment to provide a real time indication of stress levels, which affects cognitive capacity. For example, the higher the stress, the less a person is able to function efficiently, that is, the lower their cognitive capacity. Other sensors can be found in Smartphone devices (e.g. accelerometers, time-keeping devices, GPS tracking devices, etc.), and even from the data available on various systems like time clock systems, and information systems that note who is in the hospital, and how many patients they are assigned to, taking notes on, etc.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to integrate care provider health condition data, including real time biometric stress indicators, into the overload model described by Rajasenan, because the system incorporates dynamic factors that affect cognitive performance and this integration would predictably enhance overload determination.
Regarding claim 16, Rajasenan and Faulks teach the invention in claim 1, as discussed above, and further teach wherein performing a mitigation operation in response to determining that the plurality of overload factors meets the overload condition for the care provider comprises at least one of (i) sending information to a network computing device to reduce a frequency of communications to the care provider or (ii) sending information to a network computing device to adjust one or more tasks assigned to the care provider (Rajasenan [0061] “Further, embodiments consider healthcare provider team activation. Healthcare provider team activation occurs when a particular healthcare provider has reached a tipping point and requires intervention to avoid failing to perform tasks at risk. Process mining on incoming data using TAR-ILTA to determine opportunity ensures the highest value for teams activated (and interrupted, as interruptions contribute to cognitive overload as well) and the time they will spend on the intervention. In an embodiment, this is determined by the number and type of tasks that the particular healthcare provider is performing. When the healthcare provider reaches a tipping point, additional members of the healthcare team can be activated to perform a countermeasure to avoid the tipping point. Such countermeasures include double checking tasks and quality assurance, offloading the task-at-risk to another team members (that could also be at another place, using telecommunications or tele-health), offloading tasks other than the task-at-risk to other team members (to get team member below their tipping point), performing the task at a later time. Activation of teams is the key--how many tasks must be done by someone before they can and should activate a team to initiate a countermeasure. Then once activated, the key question the scoring model must address is how many tasks must be done in order to complete the countermeasure and thus be effective in the prevention. Devices that can identify the location of a person, such as smart phones, tracking tools, etc., help in knowing exactly where a person is can drive their TAR--if they are further from the patient, then that could mean being rushed (so overload, anxiety, etc.) and/or that they do not have time to complete an intervention. Thus, this could be added to the TAR-ILTA scoring model.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to implement mitigation operations such as reassigning tasks through network computing devices, as taught by Rajasenan, because these actions represent a predictable response to detected overload conditions in automated medical systems.
Claims 18 and 52 are analogous to claim 1, thus claims 18 and 52 are similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claims 19-20 are analogous to claims 2-3, thus claims 19-20 are similarly analyzed and rejected in a manner consistent with the rejection of claims 2-3.
Claim 22 is analogous to claim 5, thus claim 22 is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Claims 30 and 64 are analogous to claims 11 and 13, thus claims 30 and 64 are similarly analyzed and rejected in a manner consistent with the rejection of claims 11 and 13.
Claim 31 is analogous to claims 13-15, thus claim 31 is similarly analyzed and rejected in a manner consistent with the rejection of claims 13-15.
Claims 33 and 67 are analogous to claim 16, thus claims 33 and 67 are similarly analyzed and rejected in a manner consistent with the rejection of claim 16.
Claim 53 is analogous to claims 2 and 3, thus claim 53 is similarly analyzed and rejected in a manner consistent with the rejection of claims 2 and 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arena et al. (U.S. Patent Publication US 2018/0150604 A1) teaches a system and method for assigning nursing scores to patient related tasks based on healthcare needs to optimize patient distribution and workload among care workers.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685