DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-12 and 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-12 and 16-23 are within the four statutory categories. However, as will be shown below, claims 1-12 and 16-23 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 19, and 20 are representative of the inventive concept and recite:
Claim 19
A computing device, comprising:
one or more processors;
memory; and
one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions for:
obtaining, via a first electronic device, diagnostic data about a subject via a user interface;
identifying, based on the diagnostic data about the subject, a set of tasks involving the subject;
providing first information to a rules engine, the first information including the diagnostic data and the identified set of tasks to a rules engine
responsive to the first communication, generating, via the rule’s engine, a first set of parameters for the set of tasks based on the first information, the first parameters indicating a set of timing windows associated with respective tasks of the set of tasks;
obtaining additional information from more than one or more databases regarding at least one of the subject and the diagnostic data;
providing, to an artificial intelligence (AI) engine, a vector of inputs corresponding to second information, the second information including the diagnostic data, the identified set of tasks, the additional information from the one or more databases, and the first set of parameters generated by the rules engine;
responsive to receiving the second information, generating, via the AI engine, a second set of parameters for the set of tasks based on the second information, wherein the first set of parameters constrain the second set of parameters, and wherein generating the second set of parameters includes:
identifying a plurality of timing parameters corresponding to respective times within the set of timing windows; and
identifying a plurality of ownership parameters for the set of tasks, the ownership parameters indicating respective entities for performing respective tasks of the set of tasks;
generating a resource allocation schedule for the subject using the set of tasks, first set of parameters, and the second set of parameters, the generating comprising assigning respective tasks of the set of tasks to the respective entities, including assigning a first task of the set of tasks to a first entity, and assigning a second task of the set of tasks to a second entity; and
providing respective notifications about the set of tasks to the respective entities, including providing a first notification to the first entity and providing a second notification to the second entity.
The broadest reasonable interpretation of these steps includes mental processes and/or organizing human activity because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms like “memory”, “processor”, “computing device”, “rule’s engine”, “AI Engine”, or “user interface”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “user interface” language, “obtaining information about a subject” in the context of this claim encompasses a healthcare schedule intaking a patient at a clinic. 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” or “Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Thus, the steps of:
identifying, based on the diagnostic data about the subject, a set of tasks involving the subject;
responsive to the first communication, generating, … a first set of parameters for the set of tasks based on the first information, the first set of parameters indicating a set of timing windows associated with respective tasks of the set of tasks;
responsive to receiving the second information, generating, … a second set of parameters for the set of tasks based on the second information, wherein the first set of parameters constrain the second set of parameters, and wherein generating the second set of parameters includes:
identifying a plurality of timing parameters corresponding to respective times within the set of timing windows; and
identifying a plurality of ownership parameters for the set of tasks, the ownership parameters indicating respective entities for performing respective tasks of the set of tasks;
generating a resource allocation schedule for the subject using the set of tasks, first set of parameters, and the second set of parameters, the generating comprising assigning respective tasks of the set of tasks to the respective entities, including assigning a first task of the set of tasks to a first entity, and assigning a second task of the set of tasks to a second entity; and
as drafted, could lay out a healthcare facility manager managing an influx of patient assessments in their facility based on the time and additional healthcare resources provided by other professionals. Therefore, under the broadest reasonable interpretation, these steps include a mental process as an abstract idea.
Independent claims 1 and 20 cover similar steps of obtaining patient information, identifying a set of tasks, generating parameters for the sets of tasks, allocating resources, and providing information regarding the tasks for a particular entity. These claims fall under the same category of an abstract idea and follows the same rationale as claim 19.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 9, reciting particular aspects of how “in response to a selection of the one or more candidate tasks by the user, generating a revised resource allocation schedule that incorporates the one or more candidate tasks.” may be performed in the mind but for recitation of generic computer components).
Dependent claims 3, 4, 6, 8, 9, and 11 add extra solution activities to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 19’s device recites additional elements such as a computing device, processor, memory, rules engine, and Artificial Intelligence engine. In addition to the generic components and additional elements listed above, independent claims 1 and 20’s apparatus and product also include a user interface and a non-transitory computer readable medium. The user interface, computing device, processor, memory, and a non-transitory computer readable medium will be treated as generic computer components. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amount to mere instructions to apply an exception (such as recitation of “A computing device, comprising: one or more processors; memory; and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions” or “via a rules engine” or “via an AI engine” or “via a user interface” or via a first electronic device” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification “a user device 102 is a personal computer… and/or any other electronic device capable of receiving and responding to user inputs”, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “obtaining, via a first electronic device, diagnostic data about a subject via a user interface” and “providing first information to a rules engine, the first information including the diagnostic data and the identified set of tasks to a rules engine” and “obtaining additional information from more than one or more databases regarding at least one of the subject and the diagnostic data;” and “providing, to an artificial intelligence (AI) engine, a vector of inputs corresponding to second information, the second information including the diagnostic data, the identified set of tasks, the additional information from the one or more databases, and the first parameters generated by the rules engine;” and “providing respective notifications about the set of tasks to the respective entities, including providing a first notification to the first entity and providing a second notification to the second entity.” amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. To elaborate, claim 3 “prior to identifying the set of tasks: identifying a set of options for the subject based on the information about the subject, wherein each option in the set of options has a corresponding set of tasks; and receiving a selection of a first option of the set of options, wherein the set of tasks correspond to the first option.”, and claim 4 “after generating the resource allocation schedule, receiving an update to one or more parameters of the resource allocation schedule; in response to the update, generating a third set of parameters corresponding to at least a subset of the set of tasks, wherein the third set of parameters are generated based on the update to the one or more parameters, a current status of the resource allocation schedule, and calendar information; and updating the resource allocation schedule based on the third set of parameters.” and claim 6 “after generating the resource allocation schedule, receiving information about performance of a task of the set of tasks; and updating the resource allocation schedule based on the received information, wherein updating the resource allocation schedule includes updating a status of the task and one or more other tasks of the set of tasks.” and claim 8 “after generating the resource allocation schedule, receiving additional information about the subject; identifying, via the Al engine, one or more candidate tasks for the resource allocation schedule based on the additional information about the subject; and providing information about the one or more candidate tasks to a user” add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, and claim 9 “in response to a selection of the one or more candidate tasks by the user, generating a revised resource allocation schedule that incorporates the one or more candidate tasks.”, and claim 11 “in accordance with generating the resource allocation schedule, generating, via the Al engine, one or more documents corresponding to the resource allocation schedule based on the information about the subject.” amounts to necessary data outputting, see MPEP 2106.05(g)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims 2, 5, 7, 10, 12, 15-18, and 21-22 do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of an Artificial Intelligence engine. Werbos et al. (US6169981) demonstrates “Go has proven intractable to the conventional brute-force search approaches used by conventional artificial intelligence” that artificial intelligence engines were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of a rule’s engine. Milstein et al. (US5483443) demonstrates “This could be addressed using a very large set of rules with a conventional rule engine.” that a rules engine was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
“obtaining, via a first electronic device, diagnostic data about a subject via a user interface” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“providing first information to a rules engine, the first information including the diagnostic data and the identified set of tasks to a rules engine” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“obtaining additional information from more than one or more databases regarding at least one of the subject and the diagnosis;” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“providing, to an artificial intelligence (AI) engine, second information, the second information including the diagnostic data, the identified set of tasks, the additional information from the one or more databases, and the first parameters generated by the rules engine;” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
“providing respective notifications about the set of tasks to the respective entities, including providing a first notification to the first entity and providing a second notification to the second entity.” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. To elaborate:
claim 3 “prior to identifying the set of tasks: identifying a set of options for the subject based on the information about the subject, wherein each option in the set of options has a corresponding set of tasks”, is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
claim 3 “and receiving a selection of a first option of the set of options, wherein the set of tasks correspond to the first option.”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 4 “after generating the resource allocation schedule, receiving an update to one or more parameters of the resource allocation schedule;” , is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 4 “in response to the update, generating a third set of parameters corresponding to at least a subset of the set of tasks, wherein the third set of parameters are generated based on the update to the one or more parameters, a current status of the resource allocation schedule, and calendar information is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
claim 4 “and updating the resource allocation schedule based on the third set of parameters.” , is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
claim 6 “after generating the resource allocation schedule, receiving information about performance of a task of the set of tasks;”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 6 “updating the resource allocation schedule based on the received information, wherein updating the resource allocation schedule includes updating a status of the task and one or more other tasks of the set of tasks.” , is equivalently, storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv);
claim 8 “after generating the resource allocation schedule, receiving additional information about the subject;”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 8 “identifying, via the Al engine, one or more candidate tasks for the resource allocation schedule based on the additional information about the subject; and providing information about the one or more candidate tasks to a user” is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(vi)
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-12 and 16-23 are rejected under 35 U.S.C. 103 as being unpatentable over Eggena et al. (US20120203589) in view of Boonn et al. (Pat. 12165764).
Regarding claim 1, Eggena teaches.
A method of task management, comprising: obtaining, via a first electronic device, diagnostic data about a subject via a user interface; ([0013] “The workflow system can also include a user interface through which users, typically members of the practice, can define tasks by submitting rule-base task criteria or provide updates to the practice data.”; see also [0014] “data is stored in isolated data silos according to a classification (e.g., administration, financial, EMR, clinical, etc.)” where clinical data comprises diagnostic data; see optionally [Figure 5] “X-Ray reported broken” is a diagnosis)
identifying, based on the diagnostic data about the subject, a set of tasks involving the subject, ([Figure 5] “X-Ray reported broken effecting 13 tasks” identifies a set of tasks involving the subject based on the diagnostic data)
providing first information to a rules engine, the first information including the diagnostic data and the identified set of tasks to a rules engine ([0011] “a workflow management system can manage tasking by obtaining desired practice data even where the data is stored according to different classifications (e.g., administration, financial, clinical, EMR, etc.) in disparate databases” where obtaining clinical data comprises providing first information to a rules engine; see also [Figure 4] where rules engine (430) receives EMR data (477c) and task criteria (477B))
responsive to receiving the first information, generating, via the rules engine, a first set of parameters for the set of tasks based on the first information, ([0013] “A rules engine can also be included with the workflow system to monitor task's state relative to the practice data.” Where a tasks state [i.e., a first set of parameters] are generated via a rules engine;) the first set of parameters indicating a set of timing windows associated with respective tasks of the set of tasks; ([0051] “Example shown illustrates several conditions including a time requirement indicating a patient can be scheduled only after 2:00 p.m. when Dr. Gupta or Operating Room (OR) #4 are available” where scheduling the patient after 2pm comprises a timing window for a task)
obtaining additional information from more than one or more databases regarding at least one of the subject and the diagnostic data; ([0052] A rules engine can continuously monitor task criteria 275 relative to existing information within the practices data, even as a user enters task criteria 275.” Where continuously monitoring task criteria as a user inters in task criteria comprises obtaining additional information regarding a subject or diagnosis(a diagnosis can be considered diagnostic data))
responsive to receiving the second information, generating, via the AI Engine, a second set of parameters for the set of tasks based on the second information, wherein the first set of parameters constrain the second set of parameters ([0027] Tasks can have properties that are dependent on other tasks. A constraint can be considered a dependency.)
generating a resource allocation schedule for the subject using the set of tasks, first set of parameters, and the second set of parameters, the generating comprising assigning respective tasks of the set of tasks to the respective entities, including assigning a first task of the set of tasks to a first entity, and assigning a second task of the set of tasks to a second entity; ([0013] “the tasking module is configured to correlate practice resources (e.g., time, equipment, schedule, individuals, locations, etc.) with one or more rule-based task criteria. If the tasking module finds a resource match to the criteria, the resource can be allocated to the task.” Where allocating resources to a task [i.e., generating a resource allocation schedule] is done via rule-based task criteria [i.e., parameters] to connect a task to an entity)
and providing respective notifications about the set of tasks to the respective entities, including providing a first notification to the first entity and providing a second notification to the second entity. ([0080] “Tasking modules can also present task notifications 540 providing information about tasks, where the information can include a task's name, state, progress, classification or other information.” Where notifications provide information to the entity; see also [0079] “More than one exception task can be generated when an exception has been detected. In the example shown with respect to task recommendations 530, multiple exceptions tasks are generated. A first set of exceptions tasks are automatically executed by the system to identify or present potential solutions to any raised exceptions.” Where competing multiple tasks comprise multiple notifications to the respective entities)
Regarding claim 1, Eggena does not explicitly teach, as taught by Boonn:
providing, to an artificial intelligence (AI) engine, a vector of inputs ([95] “The classification-based model preferably outputs one or more classification outputs. In some examples, the classification output is a vector of likelihood values from the softmax layer. In additional or alternative examples, an argmax function can be applied to the vector of likelihood values to find the argument (e.g., the class) with the maximum likelihood value,” Is a vector of inputs provided to the AI engine) corresponding to second information, the second information including the diagnostic data, the identified set of tasks, the additional information from the one or more databases, and the first set of parameters generated by the rules engine; ([22] “As shown in FIG. 2, a method 200 for resource optimization includes: receiving a set of inputs S210; and processing the set of inputs and/or contextual data with a set of models to produce a set of schedules S230.” Where the set of inputs to a model comprises the diagnostics data, set of tasks, additional information from a database, and first parameters; see also [28] “resources to be allocated may include the imaging devices that generate imaging for various patients and analysis resources (e.g., processors, radiologists, etc.) that ingest the generated imaging and generate diagnoses for various patients” and “optimizing resource allocation includes … performing tasks associated with the appointments (e.g., healthcare professionals examining the patient, technicians operating the data collection devices, etc.), and/or any other features or parameters” which comprises diagnostic data, parameters defined by a system, and identified set of tasks)
generating the second set of parameters include: ([22] “Generally, a schedule defining usage of a first set of resources is received and processed by one or more machine learning models to generate an optimized allocation of a first set of resources and optionally any number of additional resources (e.g., a related second set of resources, an unrelated second set of resources, etc.).” where the additional resources comprise generating a second set of parameters)
identifying a plurality of timing parameters corresponding to respective times within the set of timing windows; and ([28] “the technology confers the benefit of optimizing the scheduling of multiple appointment slots through the processing and/or prediction of various information related to said scheduling.”)
identifying a plurality of ownership parameters for the set of tasks, the ownership parameters indicating respective entities for performing respective tasks of the set of tasks; ([31] “system and/or method utilize contextual information from various points in time and/or time scales, along with a decision-making subsystem (e.g., set of trained models), to optimize and dynamically adjust (e.g., move around, introduce new availability, etc.) schedules (e.g., appointment slots for patients, cases to be reviewed by a radiologist such as according to a worklist, etc.) associated with a set of tasks (e.g., scan, doctor visit, etc.) and/or individuals (e.g., patients, radiologists, etc.) and/or facilities (e.g., imaging center, healthcare facility, hospital, emergency room, etc.).”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 2, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein the set of tasks are identified using at least one of the rules engine and the Al engine. ([0050] “a rules engine monitors the practice data to determine when the conditions are met, at which point the tasking module is informed and a proper action is taken.” is identifying a set of tasks)
Regarding claim 3, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches
prior to identifying the set of tasks: identifying a set of options for the subject based on the diagnostic data, wherein each option in the set of options has a corresponding set of tasks; ([0056] “Recommended tasks can be offered as optional as shown; allowing the user to select which of the recommended tasks should indeed be performed.” Where recommended tasks [i.e., a set of options for the subject] can be chosen by a user)
and receiving a selection of a first option of the set of options, wherein the set of tasks correspond to the first option. ([0079] “In the example shown with respect to task recommendations 530, multiple exceptions tasks are generated. A first set of exceptions tasks are automatically executed by the system to identify or present potential solutions to any raised exceptions. A second set of tasks are generated, but not necessarily executed immediately, where the second set of tasks are presented as optional tasks that can be confirmed by the user.” Where optional tasks denote a user’s ability to receive a selection from a series of options)
Regarding claim 4, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena-Boonn as a combination also teaches:
after generating the resource allocation schedule, receiving an update to one or more parameters of the resource allocation schedule; ([72] “the set of models and/or algorithms discussed herein can dynamically generate updated resource allocations that account for these changes and attempt to maximize resource utilization in the environment (e.g., through iteratively and dynamically updating the schedule of patient appointments at a facility).” And [73] “set of models and/or algorithms can predict delays and excess resource utilization over a defined baseline based on real-time updates (e.g., based on the actual end time of a previous appointment, based on an new appointment request, based on a newly acquired scan, etc.)” where the model provides real-time updates to the practice data [i.e., parameters])
in response to the update, generating a third set of parameters corresponding to at least a subset of the set of tasks, wherein the third set of parameters are generated based on the update to the one or more parameters, a current status of the resource allocation schedule, and calendar information; ([98] “Each model can be run or updated: once; at a predetermined frequency; every time the method is performed; every time new data (e.g., appointment end time, actual appointment duration, etc.) and/or an unanticipated measurement value is received; or at any other suitable frequency.” And [126] “Optimization system 336 can recognize a current state and perform actions, and the actions of the optimization system 336 can influence the next state.” And [139] “prediction system 332, may also take into account personnel-specific preferences to determine who is eligible to be scheduled to work within any given time block, such as preferences on days of the week during which these personnel will work, vacation and time-off preferences, time preferences, and the like”)
and updating the resource allocation schedule based on the third set of parameters. (see [98] above)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 5, Eggena-Boonn as a combination teaches all of the limitations of claim 4. Eggena also teaches:
wherein updating the resource allocation schedule includes increasing a priority classification for one or more tasks of the resource allocation schedule. ([0049] “The user is promoted to enter task criteria 275 defining one or more properties or conditions that are required for the task to take place. Properties can include a wide variety of information describing a task. As indicated properties can include a time, a resource, urgency, a priority, a weighting, or other attributes” where a properties priority can be entered by a user to update the resource allocation)
Regarding claim 6, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
after generating the resource allocation schedule, receiving information about performance of a task of the set of tasks; ([0086] “the contemplated workflow system has an inherent feedback loop. A tasking module can automatically manage tasks or resources based on raised exceptions, which in turn causes exception tasks to be schedule” where the feedback loop is receiving information about performance of a set of tasks)
and updating the resource allocation schedule based on the received information, wherein updating the resource allocation schedule includes updating a status of the task and one or more other tasks of the set of tasks. ([0013] “Should analysis of the task's state reveal an exception to the task's rule-based criteria, an exception can be raised by generating an exception task, possibly a new task causing an action to be taken to handle the exception” where an exception comprises updating the allocation resource and where the task’s state is the status of a task)
Regarding claim 7, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein updating the resource allocation schedule comprises adjusting timing parameters for at least a subset of the set of tasks. ([0027] “Tasks can be stored within practice data 177 as a data structure having one or more data members representing properties of the task. Properties can include resources (e.g., personnel, equipment, locations, time, etc.), metadata, names, pointers to other tasks (e.g., dependencies), classifications, or other desired attributes” where properties [i.e., parameters] include timing and are optionally updated by the user as needed)
Regarding claim 8, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena-Boonn as a combination also teaches:
after generating the resource allocation schedule, receiving additional information about the subject; ([106] The decision-making subsystem can additionally or alternatively receive contextual data 320, which can include information for one or more users (e.g., patients, healthcare professionals such as radiologists and/or physicians and/or technologists, etc.).”)
identifying, via the Al engine, one or more candidate tasks for the resource allocation schedule based on the additional information about the subject; ([38] “The scheduling can be associated with a set of particular data collection devices (e.g., MRI scanners, CT scanners, ultrasound scanners, etc.), computers and/or processors (e.g., for performing any or all of the method 200), individuals performing tasks associated with the appointments (e.g., healthcare professionals examining the patient, technicians operating the data collection devices, etc.)”)
and providing information about the one or more candidate tasks to a user. ([137] “prediction system 332 can also generate, based on the scheduled resource allocation data 310 and contextual data 320, one or more candidate sets of personnel that can provide sufficient resources to perform the amount of work involved in performing the scheduled procedures.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 9, Eggena-Boonn as a combination teaches all of the limitations of claim 8. Eggena also teaches:
in response to a selection of the one or more candidate tasks by the user, generating a revised resource allocation schedule that incorporates the one or more candidate tasks. ([0050] Conditions represent rules that should be satisfied for the task to be scheduled, or for actions to take place when the practice data satisfies the conditions. In a preferred embodiment, a rules engine monitors the practice data to determine when the conditions are met, at which point the tasking module is informed and a proper action is taken.” Where the actions taking place for a rule to be satisfied is incorporating one or more candidate tasks in response to a selection of tasks by a user; see also [0051] “The example shown illustrates several conditions including a time requirement indicating a patient can be scheduled only after 2:00 p.m. when Dr. Gupta or Operating Room (OR) #4 are available, and when Task A is complete (e.g., a task-complete criterion)” where these requirements depict the revised resource allocation)
Regarding claim 10, Eggena-Boonn as a combination teaches all of the limitations of claim 8. Boonn also teaches:
wherein the second set of parameters are generated by a first machine learning model of the Al engine, and wherein the one or more candidate tasks are identified by a second machine learning model of the Al engine. ([107] “a set or subset of patients (e.g., all patients associated with a prior appointment/procedure, all patients associated with a prior appointment/ procedure at that facility, all patients associated with data in a medical record database, etc.) is associated with contextual data, where the contextual data can be utilized in optimizing the scheduling of this set or subset of patients, a different (e.g., distinct) set or subset of patients, a set of healthcare professionals (e.g., radiologists, physicians, technologists, etc.) associated with treatment of any patients, and/or any other individuals.”; see also [69] “The prediction subsystem can include one or more models, the optimization subsystem can include one or more models, and/or any number of models can be used for prediction, optimization, and/or the collective functions of prediction and optimization”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 11, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Boonn also teaches:
in accordance with generating the resource allocation schedule, generating, via the Al engine, one or more documents corresponding to the resource allocation schedule based on the diagnostic data about the subject. ([195] “The optimized allocation may be used to generate messaging that modifies and/or creates new time blocks. The messaging may be, for example, scheduling or rescheduling messaging transmitted to one or more health information systems according to a defined message format (e.g., according to messaging formats defined for the Health Level Seven (HL7) standard or other messaging protocols).” and [155] “The system 100 can optionally include and/or interface with a set of user interfaces 140 (equivalently referred to herein as I/O device interfaces), such as any or all of those described above, which can be utilized for any or all of: receiving inputs of the set of inputs from one or more users, providing outputs (e.g., schedules) to one or more users, and/or otherwise interfacing with the system and/or method. The user interfaces can include input devices (e.g., buttons, keyboards, touch interfaces, etc.), output devices (e.g., displays), and/or any other components.” And [102] “allocation optimization system 300 may be configured to optimize any or all of: the scheduling of patient procedures, such as imaging procedures (MRI scans, CT scans, X-rays, etc.) performed on patients;”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 12, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein the set of tasks correspond to a treatment plan, therapy plan, and/or a medical procedure. ([0055] As an additional example, OR #4 is booked while OR #3 is available. The rules engine has determined in response to the exception raise that OR #3 is an acceptable alternative. OR #3 can be determined to be acceptable through a comparison of properties outlined in task criteria 275, or properties of the resources. For example, the resource OR #4 can include multiple properties (e.g., location, availability, equipment list, etc.). The rules engine can search for other operating room resources having acceptable equivalent properties to that of OR #4, or as a function of requirements dictated by task criteria 275.” Depicts the use of an operating room for a medical procedure as a set of tasks)
Regarding claim 16, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein the additional information comprises medical information and calendar information. ([0058] “For example, a patient's scheduled appointment can be changed automatically upon identification of new clinical data indicating the patient falls within a patient population that might be at risk due a prescribed medication that has recently become suspect. In this example, an exception task (i.e., rescheduling the appointment) is an administrative task that is generated upon detecting an exception to the scheduled appointment based on clinical data (i.e., medical report about medication) and on EMR data (i.e., patient records).”)
Regarding claim 17, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein the additional information comprises information about potential entities able to perform one or more tasks of the set of tasks and future calendar information for the potential entities. ([0056] “Recommended tasks can be offered as optional as shown; allowing the user to select which of the recommended tasks should indeed be performed.” is entities performing recommended tasks associated with the ; see also [0027] “Example tasks can include sending invoices, scheduling patient visits, notifying clients of changes, reschedule events, updating calendars, or any other types of actions that a member of the practice can define.” Where example tasks comprise updating calendars)
Regarding claim 18, Eggena-Boonn as a combination teaches all of the limitations of claim 1. Eggena also teaches:
wherein at least a subset of the set of tasks are dependent on one another. ([0027] “Tasks can be stored within practice data 177 as a data structure having one or more data members representing properties of the task. Properties can include resources (e.g., personnel, equipment, locations, time, etc.), metadata, names, pointers to other tasks (e.g., dependencies),” where these properties have a dependance on one another)
Regarding claim 19, the limitations recited are similar to that of claim 1. Please refer to the claim 1 analysis.
Regarding claim 20, the limitations recited are similar to that of claim 1. Please refer to the claim 1 analysis.
Regarding claim 21, Eggena-Boonn as a combination teaches all of the claims of claim 1. Boonn also teaches:
wherein: the second information comprises unstructured data; ([135] “In some aspects, a complexity metric indicating the complexity of a study or procedure may be generated based on an analysis of previous times similar studies or procedures have been performed. To do so, various techniques can be used to parse complexity information from various inputs” where previous studies as various inputs comprise unstructured data)
and the AI engine comprises: ([24] “a schedule defining usage of a first set of resources is received and processed by one or more machine learning models to generate an optimized allocation of a first set of resources”)
a natural language processing component configured to convert the unstructured information into one or more input vectors; ([135] “various techniques can be used to parse complexity information from various inputs. For example, natural language processing techniques can be applied to narratives associated with previous studies of a given type to identify specific concepts, such as specific clinical concepts or subject matter concepts, associated with specific complexity measures (e.g., a time measure, a computational expense measure (in terms of operations performed over a given period of time), etc.). … resulting determination of the complexity of a study or procedure can be used as contextual information provided as input into a resource allocation optimization system”)
and one or more models configured to receive the one or more input vectors and generate the second set of parameters. ([118] “The second prediction machine learning model can include a machine learning model capable of solving classification problems. For example, the second prediction machine learning model can include logistic regression, decision tree, random forest, gradient-boosted tree, support vector machine K-nearest neighbor, or naïve Bayes.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 22, Eggena-Boonn as a combination teaches all of the claims of claim 1. Boonn also teaches:
wherein the one or more models comprise a large language model and a set of one or more specialized models. ([97] “Additionally or alternatively, the set of models and/or algorithms can include any other suitable types of models (e.g., model architectures) and/or algorithms. For instance, any or all of the set of models and/or algorithms can utilize one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), and any other suitable learning style.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Eggena with the teachings of Boonn, with a reasonable expectation of success, by incorporating a variety of machine learning models into tracking and allocating resources in a hospital setting. This would have reduced the downtime of providers, allowing for the reduction of disease progression by providing quicker care for patients. Boonn is adaptable to Eggena as both inventions utilize computing systems to track task allocations in a hospital setting. Eggena would have found Boonn’s teaching while searching for the unmet need that “various resource allocation techniques … result in a resource allocation that does not fully utilize the available resources and/or results in a scheduling conflict that can have adverse effects on resource utilization in an environment” [para 2].
Regarding claim 23, Eggena-Boonn as a combination teaches all of the claims of claim 1. Eggena also teaches:
one or more additional tasks to supplement the set of tasks based on the diagnostic data and the first set of parameters, wherein the resource allocation schedule incorporates the one or more additional tasks.([0026] Eggena discloses an exception task to be generated in response to a practice task).
Response to Arguments
35 U.S.C. 101
(Page 9-10) Regarding the assertion that the amended claims are not merely an abstract idea that can be performed in the human mind.
Applicant's arguments filed have been fully considered but they are not persuasive. The amendments themselves add no distinction that removes the categorization of the claims as abstract. Please refer to 101 analysis above. The argument relies heavily on the term “vector”, which as claimed and interpreted as claimed under BRI, can be generally considered structured data. If the AI engine is removed from the claim, a human can perform much of the data processing involved using pen and paper. The AI engine and vector terms are generally applied to the claim.
(Page 11) Regarding the assertion that the amended claims integrate the alleged abstract idea into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 19’s device recites additional elements such as a computing device, processor, memory, rules engine, and Artificial Intelligence engine. In addition to the generic components and additional elements listed above, independent claims 1 and 20’s apparatus and product also include a user interface and a non-transitory computer readable medium. The user interface, computing device, processor, memory, and a non-transitory computer readable medium will be treated as generic computer components. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements amount to mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea.
(Page 11) Regarding the assertion that the amended claims recite significantly more.
Applicant's arguments filed have been fully considered but they are not persuasive. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations other than the abstract idea per se (unbolded recitations in Step 1), amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity.
35 U.S.C. 103
(Page 13) Regarding the assertion that Eggena does not teach the generation of first parameters indicating timing windows that constrain subsequent parameter generation by a separate engine.
Applicant's arguments filed have been fully considered but they are not persuasive. Please refer to 103 analysis on the amended claims above.
(Page 13) Regarding the assertion that Eggena does not teach that its machine learning models receive timing windows generated by a separate rules engine as a part of a vector of inputs that constrains their parameter generation.
Applicant's arguments filed have been fully considered but they are not persuasive. Figure 1, #177E, shows resource data which includes times/dates (Para 0044), which is processed by the rules engine to define criteria. Additionally there is no indication in the claims that designate the rules engine as “separate”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dewan et al. (US20230011342) discloses a surgical medical system that retrieves and constructs the requirements of a procedure for a patient. The system discloses artificial intelligence engines containing natural language processing, a scheduling system, diagnosis data, and other features.
Brown et al. (Pat. 11264128) discloses a machine learning framework that optimizes tasks scheduled in real-time. This system uses vector based machine learning models, various classifiers, and more.
Murrish et al. (Pat. 10628553) teaches a health information management system that correlates raw healthcare data into a patient profile.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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