TASK ASSIGNMENT IN CLINICAL CARE ENVIRONMENT

Application No. 18/300,146

• Filed: Apr 13, 2023
• Priority: May 06, 2022 — provisional 63/364,273
• Examiner: WEBB III, JAMES L
• Art Unit: 3624
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: Hill-Rom Services Inc.
• OA Round: 4 (Final)

Office Action

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 .

Notice for all US Patent Applications filed on or after March 16, 2013
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 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.

Status of the Claims
This communication is in response to communications received on 2/11/26. Claim(s) 1, 10-11, 13, and 17 is/are amended, claim(s) none is/are cancelled, claim(s) 21 is/are new, and applicant states support can be found at instant specification [0034, 0047-0048, 0050, 0056]. Therefore, Claims 1-6 and 8-21 is/are pending and have been addressed below.

Response to Arguments

Applicant’s arguments, see applicant’s remarks, filed 2/11/26, with respect to rejections under 35 USC 101 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 101 rejection(s) below.

Applicant respectfully traversed the rejection on pg. 7-12.
The Examiner respectfully disagrees because while the amendment furthers the 101 argument, the claims are still directed to task assignment.

Applicant is relying on 2106.05(d) “well understood, routine, and conventional” however Examiner is relying on 2106.05(f) “apply it.” Examiner relied on “apply it” because of item (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process of 2106.05(f).

Thus, the argument(s) are unpersuasive.

Applicant’s arguments, see applicant’s remarks, filed 2/11/26, with respect to rejections under 35 USC 103 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s) below.

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.

Claim(s) 1-6 and 8-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.

The limitation(s) below for representative claim(s) 1, 13, and 17 that, under its broadest reasonable interpretation, is/are directed to task assignment.

Step 1: The claim(s) as drafted, is/are a process (claim(s) 13-16 recites a series of steps) and system (claim(s) 1-6, 8-12, and 17-21 recites a series of components).

Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s): (emphasis added)
Claim 13: determining a staff shortage in a clinical care environment exists;
categorizing tasks based on skill level, the tasks being categorized into a first group of tasks for completion by a first type of personnel, and into a second group of tasks for completion by a second type of personnel, the second type of personnel having a skill set different from that of the first type of personnel;
routing a task of the second group to a member of the second type of personnel based upon:
proximity of the member to a location of the task determined using location data acquired from one or more antennas of a real-time locating system;
current tasks in queue for the member; and
physical capabilities of the member relative to requirements of the task;
displaying a task request on a device of the member, wherein the task request identifies the task and provides a first option to accept the task request and a second option to decline the task request;
upon acceptance of the task request, monitor, using the one or more antennas, performance of the task; and
dynamically updating a task list based on the performance of the task.
Claim(s) 1 and 17: same analysis as claim(s) 13.
Dependent claims 2-6, 8-12, 14-16, and 18-21 recite the same or similar abstract idea(s) as independent claim(s) 1, 13, and 17 with merely a further narrowing of the abstract idea(s): .

The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with task assignment for a business,
a method of organizing human activity (managing personal behavior or
relationships or interactions between people including social activities, teaching, and following rules or instructions) because the invention is directed to task assignment for a business, and
a mental process (concepts performed in the human mind including an observation, evaluation, judgment, opinion) because the invention is directed to task assignment for a business.

Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include one or more antennas, real-time locating system, automatically (claim(s) 1, 13, 17), the system comprising: at least one processing device; and at least one memory storage device (claim(s) 1) a non-transitory computer readable storage medium, one or more computing devices (claim(s) 17), system (claim(s) 5), a second system (claim(s) 6), antenna (claim(s) 21).

The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.

Thus the additional elements as described above with respect to Step 2A Prong 2 merely amount to (as additionally noted by instant specification [0070]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).

Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 merely amount to (as additionally noted by instant specification [0070]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).

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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.

It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).

Claim(s) 1, 3-4, 8-9, 10-11, 13, 15-16, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al. (US 2020/0411168 A1) in view of Akella et al. (US 2019/0138973 A1), Tsuria et al. (US 2021/0241890 A1), Santarone et al. (US 2020/0250356 A1), and Zebarjadi et al. (US 2015/0371350 A1).

Regarding claim 1, 13, and 17 (currently amended), Thomas teaches a method of assigning nursing tasks, the method comprising:
{the system comprising: at least one processing device; and at least one memory storage device storing instructions which, when executed by the at least one processing device, cause the at least one processing device to: - claim 1}
{A non-transitory computer readable storage medium, comprising instructions stored thereon which, when read and executed by one or more computing devices, cause the one or more computing devices to: - claim 17}
determining a staff shortage in a clinical care environment exists [for the limitations above, see at least [0032] computing system;
[0060] “At 214, the task management module 122 can perform task management analysis based on the prescriptive output data 120. For example, at 216, the task management module 122 can determine whether the predicted demand and/or TATs indicate a shortage in available resources of the dynamic system and/or a potential violation to one or more SLA defined requirements. The task management module 122 can further generate demand/TAT notifications 128 based on a determination that a shortage or violation exists or is probable. In another example, at 216, the task management module 122 can determine an optimal prioritization order for performing the currently pending tasks that minimizes the TATs.”;
[0006] “the respective resources can include employees and the attributes include skill sets respectively associated with the employees”;
[0084] “The resource monitoring component 808 can further determine if the expected demand over an upcoming timeframe indicates the available system resources are deficient. For example, the resource monitoring component 808 can determine if the amount and/or type of resources needed to satisfy the estimated demand exceeds those available by a defined threshold or percentage. For instance, the resource monitoring component 808 can determine if the expected number of upcoming tasks for a particular hospital unit over an upcoming timeframe exceeds the exceeds the capacity of the expected number of available staff members in the upcoming timeframe. In some embodiments, the defined threshold or percentage can be defined by or otherwise based on one or more defined SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X).”];
categorizing tasks based on skill level, the tasks being categorized into a first group of tasks for completion by a first type of personnel, and into a second group of tasks for completion by a second type of personnel, the second type of personnel having a skill set different from that of the first type of personnel [see at least [0082] grouping of workers based on skill “For example, hospitals often have defined service level agreements (SLAs) for certain tasks that set maximum TATs for the respective tasks, among other requirements for the tasks. In another example, the SLA information 802 can also provide rules or regulations regarding qualifications of workers for performing certain tasks (e.g., required skill set).”;
[0084] grouping of workers based on skill and required number of workers “SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X”;
[0039] grouping of workers based on skill “With respect to EVS jobs, the attributes can include but are not limited to: a location of the job (e.g., bed in room number 123 of hospital unit H needs cleaning), one or more characteristics/requirements of the EVS job (e.g., requirements of resources needed for the job, are required skill set or qualification of workers for performing the job), supplies needed for the job, a difficulty level of the job and the like.”;
[0048] grouping of workers based on workgroup and tasks “With reference now to the model application module 118, the model application module 118 can be configured to apply the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142 to predict, based on a current state or operating context of the dynamic system represented by the dynamic system state data 102, one or more of the following: the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.”];
routing a task of the second group to a member of the second type of personnel based upon:
tasks in queue, and
capabilities of the member relative to requirements of the task [for the limitations above, see at least [0054] task assignment “The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).”;
[0060] task assignment includes queueing (order to perform tasks) “At 214, the task management module 122 can perform task management analysis based on the prescriptive output data 120. … In another example, at 216, the task management module 122 can determine an optimal prioritization order for performing the currently pending tasks that minimizes the TATs. The task management module 122 can further generate task prioritization information 130 regarding the optimal prioritization order.”;
[0039, 0041, 0043] “respective attributes associated with the different tasks (e.g., location, priority level, resource requirements, etc. … the TATs of different EVS jobs can vary based on job characteristics, job location”]; and
monitoring, using one or more data, locations of required resources positioned throughout the care facility, wherein the one or more data [see at least [0040] “For example, the staff data can include information regarding current staff activity (e.g., on job or break, specific jobs being completed by specific staff, number of staff members performing a specific task, etc.), staff availability, staff location, staff fatigue level, identities of the staff and the like.”;
[0040] “For example, in some implementations, the operating condition information can also include contextual information regarding time of day, day of week/year, weather, localized events or conditions at the hospital (e.g., emergency or crisis scenarios, disease outbreaks), local events associated high influx of patients, etc.), location and status of ambulatory services and the like.”].

Thomas doesn’t/don’t explicitly teach but Akella discloses
routing a task to a member of the personnel based upon:
physical capabilities of the member relative to requirements of the task [for the limitations above, see at least [0114] physical capabilities such as force sensor data “With regard to FIG. 13, a block diagram and data flow diagram 1300 of an exemplary computer system that automatically assigns processes or actions (e.g., tasks) to actors (e.g., human workers or robots) in real-time based on observed data (e.g., video frames, thermal sensor data, force sensor data, audio sensor data, and/or light sensor data) is depicted according to embodiments of the present invention. In the embodiment of FIG. 13, it is assumed that actors are assigned to a fixed station, and each station performs a fixed task. The computer system stores and/or receives information including process information 1305 and actor information 1310 which may be stored in one or more data structures.”]; and
automatically causing a required resource associated with the task to be sent to a location [see at least [0117] where the functions described below are done without human intervention thus automatic “At step 1425, the identified actions performed by the actor are characterized by the one or more engines to produce characterizations for the identified actions. The characterizations may include ergonomics of the actor, a skill level of the actor, and/or a time required for the actor to perform the identified actions. At step 1430, based on the determined characterizations of the actor performing the actions, an action (e.g., work task) or processes assignment is dynamically determined for the actor in real-time. … Step 1430 may include moving an actor from one station/task to another station/task, and step 1430 may be repeated over-time to automatically optimize the assignment of actors to tasks based on real-time observations of actor performance.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomas with Akela to include the limitation(s) above as disclosed by Akela. Doing so would further define Thomas’s (Thomas) [0002-0003] task management based on operating conditions data (a diverse set of factors) and Akela improves this by expanding how to use the diverse set of factors such as by user or user type constraints [see at least Akela [0003-0005, 0006, 0099, 0109].
Furthermore, all of the claimed elements were known in the prior arts of a) Thomas and b) Akela and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Thomas in view of Akella doesn’t/don’t explicitly teach but Tsuria discloses
routing a task to a member of the personnel based upon:
proximity of the member to a location of the task determined using location data;
current tasks in queue for the member, and
capabilities of the member relative to requirements of the task [see at least [0170] “At step 730 “target” staff, such as agents (e.g. people of the correct type who are for example currently in a shift in the ward where the patient is hospitalized and have the capabilities to best answer the patient's need) are identified using, for example, Rule based criteria.”;
[0172] “In some embodiments, the service requests (of step 740) are assigned to one of the currently available agents using a Logistic Regression method. The Logistic Regression method, in accordance with embodiments, includes characterizing each request (e.g. service request) by one or more attributes such as how often this request is made, how fast its handled, on average, how urgent it is, etc. The available agents are also categorized for example based on their attributes such as how long they have been in the shift, how many requests they have on their current list and their seniority. The method then uses a variant of for example Multinomial logistic regression to assign the service request to the agent best suited to handle it.”;
[0170] further define agent attributes of ([0172]) “At step 730 “target” staff, such as agents (e.g. people of the correct type who are for example currently in a shift in the ward where the patient is hospitalized and have the capabilities to best answer the patient's need) are identified using, for example, Rule based criteria.”;
[0072-0075] “As shown in FIG. 1A patient's location and condition changes, as do his/her needs relative to that change. The needs of a patient can be very different depending on whether the patient is in bed, or whether the patient such as patient 36 is outdoor 57 or in the cafeteria, coming out of x-ray or rehab, and whether he is on the emergency room 53, or the patient is a mother at the gynecological ward 55 or a nurse 34′ or sanitarian 23′ at an internal medicine ward reception 51.
Even in the same location, time can be a major factor, e.g. when a patient is waking up after surgery he has a different set of needs and should have a different set of icons then the person in the neighboring bed who is already a week into post-operative recuperation.
In accordance with embodiments, these changes may be evaluated for example using one or more of the systems and methods modules (e.g. patient analyzer module 402), which allows the system to take into account such changes.
The location also has different assets and capabilities depending on time. A night shift or weekend or holiday staffing might have a smaller staff allocation and therefore certain needs will not be filled by the staff, like shower help when staff is minimal, etc. Rehabilitation equipment might be unavailable, or transportation options may be changed, and these changes need to be reflected in what the patient can choose; hence the methods and systems are further configured using for example the staff analyzer module 412 to collect and evaluate all the relevant changes to staff and site.”];
displaying a task on a device of the member, wherein the task;
dynamically updating a task list based on the performance of the task [for the limitation, see at least [0109] “For example, in accordance with some embodiments, the users' group type categories include patients/staff (agents)/hospital database, and, respectively, for each group the generated matching interface tool may be presented. The generated interface tool may include the following information (which are based on the analysis results) for each category as follows: …
2.For the staff (e.g. agent):
A prioritized task list for each agent, including explicit and derived patient requests that he/she is assigned to, and a recommended order of execution”;
[0179] “Optionally, at step 770 a binary decision can be made regarding whether the target agent of the staff accomplished the Service Request for example in a given time. For example, in some cases, if the agent fails to complete a Service Request after a given time, then at step 772 it will be removed from the agent's task list and reentered into the general task list at step 774. If the agent accomplished the Service Request it will be deleted at step 776 from a general list of service requests and/or marked as ‘DONE’.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomas in view of Akella with Tsuria to include the limitation(s) above as disclosed by Tsuria. Doing so would further define Thomas in view of Akella’s (Thomas) [0002-0003] [0002-0003] task management based on operating conditions data such as types of workers [see at least Tsuria [0004-0011] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Thomas in view of Akella and b) Tsuria and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Thomas in view of Akella and Tsuria doesn’t/don’t explicitly teach but Santarone discloses
proximity of the member to a location determined using location data acquired from one or more antennas of a real-time locating system;
monitoring, using one or more antennas, performance of the task [see at least Fig. 1A and [0036, 0101] monitoring system of resources including workers, where the system includes antennas “According to the present invention, a direction dimension (which may include a direction of interest of an Agent and/or an orientation of a device, may be based upon wireless communications with a single antenna or an antenna array attached to or incorporated into the device.” and “a self-verifying array of Nodes may allow an Augmented Virtual Model (AVM) of a Healthcare Facility and/or a building site to be updated with the locations of an Agent or equipment that is co-located with a wireless Node.”;
Fig. 1A and [0119] location of resources “The physical Healthcare Facility 101A may include Nodes 102 or other type of wireless transceiver that may incorporate, or be co-located with, one or more Sensors that quantify a position or condition(s) in a physical area within the Healthcare Facility 101A, which may be designated, for example, as a resource 102C.”;
Fig. 1A and [0150] location of users “An AVM may indicate that one or more RFID chips are accessible at an ingress into a Healthcare Facility. The user may activate appropriate Sensors to read the RFID chips and determine their location. In another aspect, an AVM 100 may indicate that location identifiers 121A are placed at two or more corners (or other placement) of a physical Healthcare Facility 101A and each of the location identifiers 121A may include a transmitter with a defined location and at a defined height. The user device 106, or other type of controller, may then triangulate with the location identifiers 121A to calculate a precise location and height within the physical Healthcare Facility.”;
[0113] real time data of resources and users “According to the present invention, a Healthcare Facility is provided with wireless Nodes capable of providing real time (without delay) position coordinates enabling succinct organization of healthcare procedures and allocation of healthcare providers and other staff and Agents.”;
[0081] monitor performance of task “The present invention provides for automation that tracks who and what is where, and a relative position and orientation of persons and equipment. The location and orientation of HCPs may be correlated to a healthcare procedure to monitor who is located where and which direction they are facing, before, during and after the healthcare procedure. The present invention also provides for automation to monitor which equipment was involved in a healthcare procedure and who operated such equipment, a chronological order of equipment operation and relative timing of actions taken during a procedure, including wait time during which a person and/or piece of equipment is located and brought to a site of a procedure.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomas in view of Akella and Tsuria with Santarone to include the limitation(s) above as disclosed by Santarone. Doing so would further define Thomas in view of Akella and Tsuria’s (Thomas) [0002-0003] [0002-0003] task management based on operating conditions data via “designation of a position of persons and equipment relative to each other based upon wireless communications amongst multiple wireless transceivers combined with ongoing monitoring of conditions present in a healthcare facility” [see at least Santarone [0003] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Thomas in view of Akella and Tsuria and b) Santarone and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Thomas in view of Akella, Tsuria, and Santarone doesn’t/don’t explicitly teach but Zebarjadi discloses
a task request, wherein the task request identifies the task and provides a first option to accept the task request and a second option to decline the task request;
upon acceptance of the task request, perform an action; and [for the limitations above, see at least [0053] “Meanwhile, a doctor component 530 may provide personal, practice, and/or status information 531 describing the doctor, location information describing the doctor 532, travel information describing the doctor's travel mode or abilities 533, and acceptance/declination component 534 to permit a doctor to accept or decline a patient's request for the provision of medical services, a medical resources component 535 that may provide the doctor with reference information regarding the provision of medical services, such as dosing information or diagnostic guides, etc.”;
[0049-0050] “If the decision of a doctor in step 470 is not to accept a patient, method 400 may proceed to step 475 of notifying a further doctor of a request for medical services or of notifying the patient that medical services will not be provided. …
If the outcome of step 470 is that a doctor chooses to accept a request for the provision of medical services, a doctor may be provided travel directions in step 480 to permit the doctor to reach the patient.”;
[0051] “Information maintained in record component 516 may be used to match doctors with patients who have previously received care from that doctor (and optionally when the patient has provided a positive evaluation or other response to the doctor) or to avoid matching a doctor to a patient if that matching has been unfavorable before.”;
[0053] an increased rating in this example the increased rating is with a specific customer “Meanwhile, a doctor component 530 may provide personal, practice, and/or status information 531 describing the doctor, location information describing the doctor 532, travel information describing the doctor's travel mode or abilities 533, and acceptance/declination component 534 to permit a doctor to accept or decline a patient's request for the provision of medical services, a medical resources component 535 that may provide the doctor with reference information regarding the provision of medical services, such as dosing information or diagnostic guides, etc.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Thomas in view of Akella, Tsuria, and Santarone with Zebarjadi to include the limitation(s) above as disclosed by Zebarjadi. Doing so would further define Thomas in view of Akella, Tsuria, and Santarone’s (Thomas) [0002-0003] task management based on operating conditions data such as award/feedback from workers or customers [see at least Zebarjadi [0002-0003, 0051] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Thomas in view of Akella, Tsuria, and Santarone and b) Zebarjadi and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Regarding claim 3, 15, and 19, modified Thomas teaches the method of claim 13,
and Thomas teaches further comprising: determining the staff shortage exists when average wait times for completion of the tasks exceed a threshold set for the clinical care environment [see at least [0002, 0066, 0074] input data “turnaround times (TATs)” and “For example, the one or more TAT models 140 can generate information that indicates the average TATs of each of the 50 bed cleaning tasks will be 10 minutes.”;
[0027, 0040] input data “In this regard, the disclosed system can employ a microservices architecture to collect historical task data regarding EVS tasks performed and timing of performance (e.g., time task originated, and time fulfilled) in association with historical state information for the hospital regarding the various operating conditions/context of the hospital. For example, the state data can include information regarding occupancy levels, wait times for beds, status of beds, staff availability, supply availability, and the like.” and “For example, in implementations in which the dynamic system is a hospital and the tasks include EVS bed cleaning task, the operating conditions data 106 can include but is not limited to: current occupancy levels of the hospital, status of beds at the hospital (e.g., occupied, assigned, clean, dirty, etc.), number of patient waiting for beds, predicted wait times for occupied beds (e.g., determined based on level/type of care needed, recovery time, procedures scheduled, etc.), estimates of census pressure on source units where patients are waiting for beds, locations of the beds (e.g., by medical unit), and types of the beds.”;
[0080] input data used to determine current status “The input parameters extracted from the operating conditions data 106 can include for example, current operating conditions of the dynamic system (e.g., hospital occupancy levels, bed availability, bed status, bed wait times, staff availability, staff location, supply availability, etc.) and current contextual conditions (e.g., time of day, day of week, weather, externa system events, etc.) associated with the dynamic system. The demand forecasting component 704 can further apply the one or more demand models 140 to the extracted input parameters to generate predictive output data 120 regarding the forecasted task demand.”;
[0060] current status such as resource shortage “At 214, the task management module 122 can perform task management analysis based on the prescriptive output data 120. For example, at 216, the task management module 122 can determine whether the predicted demand and/or TATs indicate a shortage in available resources of the dynamic system and/or a potential violation to one or more SLA defined requirements. The task management module 122 can further generate demand/TAT notifications 128 based on a determination that a shortage or violation exists or is probable. In another example, at 216, the task management module 122 can determine an optimal prioritization order for performing the currently pending tasks that minimizes the TATs.”;
[0006] “the respective resources can include employees and the attributes include skill sets respectively associated with the employees”].

Regarding claim 4, 16, and 20, modified Thomas teaches the method of claim 13,
and Thomas teaches further comprising: determining the staff shortage exists when a ratio of the first type of personnel to patients exceeds a threshold set for the clinical care environment [see at least [0084] “The resource monitoring component 808 can further determine if the expected demand over an upcoming timeframe indicates the available system resources are deficient. For example, the resource monitoring component 808 can determine if the amount and/or type of resources needed to satisfy the estimated demand exceeds those available by a defined threshold or percentage. For instance, the resource monitoring component 808 can determine if the expected number of upcoming tasks for a particular hospital unit over an upcoming timeframe exceeds the exceeds the capacity of the expected number of available staff members in the upcoming timeframe. In some embodiments, the defined threshold or percentage can be defined by or otherwise based on one or more defined SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X).”;
[0040] “For example, in some implementations, the operating condition information can also include contextual information regarding time of day, day of week/year, weather, localized events or conditions at the hospital (e.g., emergency or crisis scenarios, disease outbreaks), local events associated high influx of patients, etc.), location and status of ambulatory services and the like.”;
[0042] “In this regard, in a dynamic system such as a hospital, the predicted distribution of EVS tasks can be based on various complex and conditional factors. For example, the predicted distribution of EVS tasks involving bed cleaning can vary based on different operation conditions of the hospital regarding number of patients currently occupying beds, available clean beds, currently pending beds to be cleaned, number of patients waiting for beds, estimated wait times for the beds, locations of the beds, types of the beds, etc. and the predicted flow of the current patients and new patients throughout the hospital in the near future.”].

Regarding claim 8, modified Thomas teaches the system of claim 1,
and Thomas teaches wherein the task is routed to the member of the second type of personnel based on proximity of the member to a location for completion of the task [see at least [0054] assign tasks based on input data including constraints “The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).”;
[0041] The predicted distribution of the EVS tasks can include the location of the tasks “The predicted distribution of the EVS tasks can include the total number of predicted tasks, the types of the tasks, the location of the tasks, and other potential distinguishing attributes associated with the tasks that can have an impact on the task TATs and/or the resources that are needed (e.g., personnel, supplies, equipment, etc.) to fulfil the tasks in accordance with defined service level requirements for the tasks.”;
[0040] staff and operating conditions include locations of workers and supplies “For example, the staff data can include information regarding current staff activity (e.g., on job or break, specific jobs being completed by specific staff, number of staff members performing a specific task, etc.), staff availability, staff location, staff fatigue level, identities of the staff and the like. The operating conditions data 106 can also include information regarding supplies/equipment used in association with performance of the tasks, such as supply/equipment availability (e.g., available, in-use, out-of-stock), status (e.g., clean/dirty, etc.), supply/equipment location, and the like.”].

Regarding claim 9, modified Thomas teaches the system of claim 1,
and Thomas teaches wherein the task is routed to the member of the second type of personnel based on at least one of an availability of the member, a skill level of the member, one or more preferences of the member, and a location of the member relative to a patient associated with the task in the clinical care environment [see at least [0054] “The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).”].

Regarding claim 10 (currently amended), modified Thomas teaches the system of claim 1,
and Thomas teaches wherein the instructions, when executed by the at least one processing device, further cause the at least one processing device to:
the member of the second type of personnel, another member of the second type of personnel [for the limitations above, see at least [0082] grouping of workers based on skill “For example, hospitals often have defined service level agreements (SLAs) for certain tasks that set maximum TATs for the respective tasks, among other requirements for the tasks. In another example, the SLA information 802 can also provide rules or regulations regarding qualifications of workers for performing certain tasks (e.g., required skill set).”;
[0084] grouping of workers based on skill and required number of workers “SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X”;
[0039] grouping of workers based on skill “With respect to EVS jobs, the attributes can include but are not limited to: a location of the job (e.g., bed in room number 123 of hospital unit H needs cleaning), one or more characteristics/requirements of the EVS job (e.g., requirements of resources needed for the job, are required skill set or qualification of workers for performing the job), supplies needed for the job, a difficulty level of the job and the like.”;
[0048] grouping of workers based on workgroup and tasks “With reference now to the model application module 118, the model application module 118 can be configured to apply the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142 to predict, based on a current state or operating context of the dynamic system represented by the dynamic system state data 102, one or more of the following: the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.”;
[0054] “The task management module 122 can also employ combinatorial optimization to determine resource allocation information 132 that defines an optimal allocation of the available resources for the tasks that facilitates minimizing the TATs, satisfying the expected demand, and meeting defined constraints/requirements for the tasks. In addition, in some embodiments, the task management module 122 can also determine resource assignment information 134 for the tasks that assigns specific resources, (e.g., specific works/staff, specific instruments/equipment, etc.) to specific tasks to facilitate minimizing the TATs, meeting defined SLAs, and ensuring the available resources will satisfy the expected demand With respect to assigning/allocating resources, the task management module 122 can further determine how to assign staff to tasks to maximize the number of tasks fulfilled while balancing staff workload (e.g., toward on equal distribution of the workload amongst the available staff) in view of the number of tasks to be completed and the number of staff available. The task management module 122 can also apply constraints regarding assignment restrictions, shift constraints (e.g., timing of shifts, maximum and minimum job allocation per staff member per shift, etc.) and capacity constraints (e.g., regarding system capacity) in association with task assignment rules (e.g., fair distribution of task rules, SLA rules, zone rules, patient and material transport rules, etc.) to determine how to optimize the assignment of staff members to the tasks (e.g. to optimize the number of tasks fulfilled and balance the distribution of the workload).”].

Modified Thomas doesn’t/don’t explicitly teach but Zebarjadi discloses
provide an option to the member to accept or decline the task of the second group; and
when the member declines the task, reroute the task to another member [for the limitations above, see at least [0053] “Meanwhile, a doctor component 530 may provide personal, practice, and/or status information 531 describing the doctor, location information describing the doctor 532, travel information describing the doctor's travel mode or abilities 533, and acceptance/declination component 534 to permit a doctor to accept or decline a patient's request for the provision of medical services, a medical resources component 535 that may provide the doctor with reference information regarding the provision of medical services, such as dosing information or diagnostic guides, etc.”;
[0051] “Information maintained in record component 516 may be used to match doctors with patients who have previously received care from that doctor (and optionally when the patient has provided a positive evaluation or other response to the doctor) or to avoid matching a doctor to a patient if that matching has been unfavorable before.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Zebarjadi to include the limitation(s) above as disclosed by Zebarjadi. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data such as award/feedback from workers or customers [see at least Zebarjadi [0002-0003, 0051] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Zebarjadi and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Regarding claim 11 (currently amended), modified Thomas teaches the system of claim 1,
and Thomas teaches wherein the instructions, when executed by the at least one processing device, further cause the at least one processing device to:
perform an action based on monitoring performance of the task by the member of the second type of personnel [see at least [0057-0058] “Process 200 begins at 202 wherein dynamic system state data 102 is collected or extracted for the purpose of model building/training and/or model application. In this regard, with respect to model building/training, at 202, the task management model development module 110 can regularly (e.g., every N minutes) collect and combine sets of the dynamic system state data 102 for the dynamic system over time to generate historical state data 204. The historical state data 204 can thus provide historical log of the task operations of the dynamic system under different operating conditions/context of the dynamic system. For example, in implementations in which the dynamic system comprises a hospital, at 202, the historical state data 204 can include sequential sets of task data 104 that identify the currently pending EVS tasks at sequential points or periods (e.g., periods of N minutes, such as 30 minutes for example) in time, information that indicates the TATs for tasks completed over the sequential period of time, and attributes of the tasks (e.g., types of the tasks, locations of the task, etc.). The historical state data 204 can also include sequential sets of operating conditions data 106 that coincide with the sequential sets of the task data 104 and that identify the operating conditions/contexts of the hospital at the sequential points or periods in time. In this regard, each set of the historical state data 204 can provide a snapshot of the state of the hospital at sequential points/periods of time.
At 206, the task management model development module 110 can employ the historical state data 204 to build and/or train the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142. This historical data collection and model training can be a continuous process. In this regard, after initial versions of the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142 are built, the task management model development module 110 can regularly or continuously collect sequential sets of the dynamic system state data 102 over time and add them to the historical state data 204. The task management model development module 110 can further regularly or continuously employ the updated historical state data 204 to retrain the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142 to generate updated versions of the respective models.”].

Modified Thomas doesn’t/don’t explicitly teach but Zebarjadi discloses
issue an award to the member of the second type of personnel based on performance of the tasks by the member [for the limitations above, see at least [0053] an increased rating in this example the increased rating is with a specific customer “Meanwhile, a doctor component 530 may provide personal, practice, and/or status information 531 describing the doctor, location information describing the doctor 532, travel information describing the doctor's travel mode or abilities 533, and acceptance/declination component 534 to permit a doctor to accept or decline a patient's request for the provision of medical services, a medical resources component 535 that may provide the doctor with reference information regarding the provision of medical services, such as dosing information or diagnostic guides, etc.”;
[0051] “Information maintained in record component 516 may be used to match doctors with patients who have previously received care from that doctor (and optionally when the patient has provided a positive evaluation or other response to the doctor) or to avoid matching a doctor to a patient if that matching has been unfavorable before.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Zebarjadi to include the limitation(s) above as disclosed by Zebarjadi. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data such as award/feedback from workers or customers [see at least Zebarjadi [0002-0003, 0051] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Zebarjadi and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Claim(s) 2, 5-6, 14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas in view of Akella, Tsuria, Santarone, and Zebarjadi as applied to claim(s) 1 above and further in view of Haspert et al. (US 2021/0265065 A1).

Regarding claim 2, 14, and 18, modified Thomas teaches the method of claim 13, as well as
determining the staff shortage exists (determining a staff shortage in a clinical care environment exists - claim 13) and
the first type of personnel.

Modified Thomas doesn’t/don’t explicitly teach but Haspert discloses
determining the staff shortage exists by receiving a trigger input from a manager of personnel [Examiner notes applicant has not acted as his or her own lexicographer to specifically define or redefine trigger input in the instant specification [0040] therefore the limitation is interpreted based on broadest reasonable interpretation of instant specification [0040],
then see at least [0052] “For example, if a caregiver is assigned a new patient, the assigned caregiver may not need to be the person logging on to and entering data associated with their workload each time they are assigned a new patient. As another example, a case manager may wish to enter notes regarding a certain patient that will be stored with and/or modify patient data 202. In yet another example, a case manager may edit patient data 202 to indicate a caregiver assigned to the patient may need to have certain requirements (e.g., caregiver needs to be a registered nurse (RN)).”;
[0054] “Upon receipt of patient and caregiver data 210, CA computing device is configured, in some embodiments, to determine an allocation plan indicating a caregiver to be assigned to the patient and the corresponding caregiver's workload if the caregiver is assigned to the patient.”;
[0055] “In some embodiments, caregiver workloads may exceed the caregiver capacity/score because of an insufficient number of caregivers. Accordingly, CA computing device 102 may be configured to monitor and analyze caregiver capacities/scores and recommend when and where it may be optimal/required to hire one or more new caregivers. For example, if a threshold number of caregivers in a certain region have workload (scores) above their caregiver capacities/scores, CA computing device 102 may recommend (e.g., by transmitting an alert to case manager computing device 104) that at least one new caregiver (or any number of caregivers required to adjust the current caregiver workloads/scores to below their capacities) be hired in that region.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Haspert to include the limitation(s) above as disclosed by Haspert. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data via clarifying that operating condition data is from users [see at least Haspert [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Haspert and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Regarding claim 5, modified Thomas teaches the system of claim 4,
and Thomas teaches wherein the ratio of the first type of personnel to patients is calculated by determining a total number of the first type of personnel, and by determining a total number of patients admitted to the clinical care environment using data acquired from an admission, discharge, and transfer system [see at least [0028] “In this regard, the system can receive real-time task data regarding currently pending EVS tasks in association with real-time state data for the hospital (e.g., current occupancy levels, current status of beds, estimates of current wait times for beds, estimates of current wait times for beds, estimates of current demand for beds in respective units of the hospital, estimates of census pressure on source units where patients are waiting for a bed, current staff availability, etc.)”;
[0048] “For example, in implementations in which the dynamic system is a hospital, the current operating conditions/context information can include information regard current occupancy levels, current bed availably, current bed status (e.g., occupied, clean or dirty), number of patient waiting for beds, predicted wait times for occupied beds, estimates of census pressure on source units where patients are waiting for beds, locations of the beds (e.g., by medical unit), staff availability, supply/instrument availability, and the like.”;
[0084] “The resource monitoring component 808 can further determine if the expected demand over an upcoming timeframe indicates the available system resources are deficient. For example, the resource monitoring component 808 can determine if the amount and/or type of resources needed to satisfy the estimated demand exceeds those available by a defined threshold or percentage. For instance, the resource monitoring component 808 can determine if the expected number of upcoming tasks for a particular hospital unit over an upcoming timeframe exceeds the exceeds the capacity of the expected number of available staff members in the upcoming timeframe. In some embodiments, the defined threshold or percentage can be defined by or otherwise based on one or more defined SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X).”;
[0040] “For example, in some implementations, the operating condition information can also include contextual information regarding time of day, day of week/year, weather, localized events or conditions at the hospital (e.g., emergency or crisis scenarios, disease outbreaks), local events associated high influx of patients, etc.), location and status of ambulatory services and the like.”;
[0042] “In this regard, in a dynamic system such as a hospital, the predicted distribution of EVS tasks can be based on various complex and conditional factors. For example, the predicted distribution of EVS tasks involving bed cleaning can vary based on different operation conditions of the hospital regarding number of patients currently occupying beds, available clean beds, currently pending beds to be cleaned, number of patients waiting for beds, estimated wait times for the beds, locations of the beds, types of the beds, etc. and the predicted flow of the current patients and new patients throughout the hospital in the near future.”].

Modified Thomas (Thomas) [0028, 0048] implies a worker is logged in based on real time data of current availability but doesn’t/don’t explicitly teach however Haspert discloses
determining a number of personnel logged into the system [see at least [0050] “In the example embodiment, a caregiver may log in or register with CA computing system 100 by inputting registration data (not shown) that may include, for example, a username, password, biometric data, and/or other information via caregiver computing device 108. After CA computing device 102 confirms the registration data, a caregiver may then input caregiver data 204 to CA computing device 102 via caregiver computing device 108. In the example embodiment, caregiver data 204 includes, among other data, a caregiver name, addresses where the caregiver associated with caregiver data 204 already administers care, a caregiver phone number, a caregiver email address, languages a caregiver speaks, caregiver certifications (e.g., medical certifications), caregiver workload capacity, indicating a workload the associated caregiver can accept, a current caregiver workload, indicating the associated caregiver's current workload, and/or other data associated with a caregiver. CA computing device 102 is configured to receive caregiver data 204 and transmit caregiver data 204 to database 110.”
[0031] “In some embodiments, the CA computing device may provide outputs in real time in order to further optimize assigning caregivers to patients. For example, the CA computing device may output to a caregiver computing device, a schedule for the day wherein the schedule includes at least the patients the caregiver is assigned to on that particular day (and/or any other time period) and the times at which the caregiver should be treating the patients. The CA computing device may further provide navigational tools to the caregiver computing device such that the caregiver can more efficiently travel from patient to patient (e.g., the CA computing device may monitor certain traffic events (e.g., as received from a third party system) in order to route caregivers around traffic events that may delay the caregiver arriving to a particular location).”;
[0032-0033] “For example, the CA computing device may notify a patient (e.g., by transmission of data to a patient computing device) of a time a caregiver is scheduled to provide care to the patient and/or a location at which the care is to be provided. The CA computing device may also notify the patient of a time when the caregiver is en route to the appointment, when the caregiver has almost arrived to the appointment (e.g., when the caregiver is 5 minutes away), and/or when the caregiver has arrived at the appointment location. In some embodiments the CA computing device may provide a real time map to a patient computing device such that the patient can view, at the patient computing device, an approximate location of their assigned caregiver (e.g., location at a particular intersection, on a particular road, etc.).
In some embodiments, the CA computing device may monitor caregivers (e.g., via a caregiver computing device) and may determine that a particular caregiver is running behind schedule. For example, the CA computing device may track the location of a caregiver computing device and determine that a caregiver arrived at a most-recent appointment an hour late.”;
[0077] “Method 600 further includes automatically generating 612, in real time and based at least upon the converted caregiver data and the converted care data, an assignment of a caregiver of the plurality of caregivers to the patient.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Haspert to include the limitation(s) above as disclosed by Haspert. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data via clarifying that operating condition data is from users [see at least Haspert [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Haspert and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Regarding claim 6, modified Thomas teaches the system of claim 4,
and Thomas teaches wherein the ratio of the first type of personnel to patients is calculated by determining a total number of the first type of personnel in the clinical care environment, and determining a total number of patients admitted to the clinical care environment using data acquired from an admission, discharge, and transfer system [see at least [0028] “In this regard, the system can receive real-time task data regarding currently pending EVS tasks in association with real-time state data for the hospital (e.g., current occupancy levels, current status of beds, estimates of current wait times for beds, estimates of current wait times for beds, estimates of current demand for beds in respective units of the hospital, estimates of census pressure on source units where patients are waiting for a bed, current staff availability, etc.)”;
[0048] “For example, in implementations in which the dynamic system is a hospital, the current operating conditions/context information can include information regard current occupancy levels, current bed availably, current bed status (e.g., occupied, clean or dirty), number of patient waiting for beds, predicted wait times for occupied beds, estimates of census pressure on source units where patients are waiting for beds, locations of the beds (e.g., by medical unit), staff availability, supply/instrument availability, and the like.”;
[0084] “The resource monitoring component 808 can further determine if the expected demand over an upcoming timeframe indicates the available system resources are deficient. For example, the resource monitoring component 808 can determine if the amount and/or type of resources needed to satisfy the estimated demand exceeds those available by a defined threshold or percentage. For instance, the resource monitoring component 808 can determine if the expected number of upcoming tasks for a particular hospital unit over an upcoming timeframe exceeds the exceeds the capacity of the expected number of available staff members in the upcoming timeframe. In some embodiments, the defined threshold or percentage can be defined by or otherwise based on one or more defined SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X).”;
[0040] “For example, in some implementations, the operating condition information can also include contextual information regarding time of day, day of week/year, weather, localized events or conditions at the hospital (e.g., emergency or crisis scenarios, disease outbreaks), local events associated high influx of patients, etc.), location and status of ambulatory services and the like.”;
[0042] “In this regard, in a dynamic system such as a hospital, the predicted distribution of EVS tasks can be based on various complex and conditional factors. For example, the predicted distribution of EVS tasks involving bed cleaning can vary based on different operation conditions of the hospital regarding number of patients currently occupying beds, available clean beds, currently pending beds to be cleaned, number of patients waiting for beds, estimated wait times for the beds, locations of the beds, types of the beds, etc. and the predicted flow of the current patients and new patients throughout the hospital in the near future.”].

Modified Thomas doesn’t/don’t explicitly teach but Haspert discloses
a number of the personnel using location data [see at least [0031] “In some embodiments, the CA computing device may provide outputs in real time in order to further optimize assigning caregivers to patients. For example, the CA computing device may output to a caregiver computing device, a schedule for the day wherein the schedule includes at least the patients the caregiver is assigned to on that particular day (and/or any other time period) and the times at which the caregiver should be treating the patients. The CA computing device may further provide navigational tools to the caregiver computing device such that the caregiver can more efficiently travel from patient to patient (e.g., the CA computing device may monitor certain traffic events (e.g., as received from a third party system) in order to route caregivers around traffic events that may delay the caregiver arriving to a particular location).”;
[0032-0033] “For example, the CA computing device may notify a patient (e.g., by transmission of data to a patient computing device) of a time a caregiver is scheduled to provide care to the patient and/or a location at which the care is to be provided. The CA computing device may also notify the patient of a time when the caregiver is en route to the appointment, when the caregiver has almost arrived to the appointment (e.g., when the caregiver is 5 minutes away), and/or when the caregiver has arrived at the appointment location. In some embodiments the CA computing device may provide a real time map to a patient computing device such that the patient can view, at the patient computing device, an approximate location of their assigned caregiver (e.g., location at a particular intersection, on a particular road, etc.).
In some embodiments, the CA computing device may monitor caregivers (e.g., via a caregiver computing device) and may determine that a particular caregiver is running behind schedule. For example, the CA computing device may track the location of a caregiver computing device and determine that a caregiver arrived at a most-recent appointment an hour late.”;
[0077] “Method 600 further includes automatically generating 612, in real time and based at least upon the converted caregiver data and the converted care data, an assignment of a caregiver of the plurality of caregivers to the patient.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Haspert to include the limitation(s) above as disclosed by Haspert. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data via clarifying that operating condition data is from users [see at least Haspert [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Haspert and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas in view of Akella, Tsuria, Santarone, and Zebarjadi as applied to claim(s) 1 above and further in view of Brandt et al. (US 2003/0045958 A1).

Regarding claim 12, modified Thomas teaches the system of claim 1,
and Thomas teaches wherein the first type of personnel includes staff members, and the second type of personnel include data1 and data 2 [see at least [0082] grouping of workers based on skill “For example, hospitals often have defined service level agreements (SLAs) for certain tasks that set maximum TATs for the respective tasks, among other requirements for the tasks. In another example, the SLA information 802 can also provide rules or regulations regarding qualifications of workers for performing certain tasks (e.g., required skill set).”;
[0084] grouping of workers based on skill and required number of workers “SLAs included in the SLA information 802 (e.g., medical unit X requires a minimum of Y workers assigned to the EVS tasks in medical unit X”;
[0039] grouping of workers based on skill “With respect to EVS jobs, the attributes can include but are not limited to: a location of the job (e.g., bed in room number 123 of hospital unit H needs cleaning), one or more characteristics/requirements of the EVS job (e.g., requirements of resources needed for the job, are required skill set or qualification of workers for performing the job), supplies needed for the job, a difficulty level of the job and the like.”;
[0048] grouping of workers based on workgroup and tasks “With reference now to the model application module 118, the model application module 118 can be configured to apply the one or more demand models 138, the one or more TAT models 140, and/or the one or more staffing models 142 to predict, based on a current state or operating context of the dynamic system represented by the dynamic system state data 102, one or more of the following: the demand for tasks of the dynamic system (e.g., total number to tasks, total number of tasks per medical unit, per type, or another grouping criteria), the TATs for the tasks, and the number of available staff to perform the tasks.”].

Modified Thomas doesn’t/don’t explicitly teach but Tsuria discloses
nursing staff members, and volunteers [0087] “For example, in accordance with some embodiments, an icon may trigger a call to some assisting person, such as a ward volunteer, for providing a cup of water, while another icon would trigger a high priority call to a nurse or attending physician. An icon or set of icons can trigger a message assigned routing directly to an appropriate destination using defined connections, such that nurses calls can be routed to a head nurses station display, while calls to other volunteers can be pre-directed to another display or to the nurses display or management station in order to be assigned by a staff person.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Tsuria to include the limitation(s) above as disclosed by Tsuria. Doing so would further define modified Thomas (Thomas) [0002-0003] task management based on operating conditions data such as types of workers [see at least Tsuria [0004-0011] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Tsuria and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Modified Thomas doesn’t/don’t explicitly teach but Brandt discloses
nursing staff members, and administrative staff [see at least [0023] “Additionally, a worker role 26 (FIG. 3) may be represented in the system by a generic code for a “virtual” role 27 (FIG. 3), i.e. a placeholder to be satisfied in real-time by a code representing a specific worker 4. As used herein, a “role” 26, 27 may comprise a task, office, or job description for which a workflow administrator may wish to assign a work item, for example in a healthcare environment comprising nursing roles, administrative roles, physician roles, physician assistant roles, therapist roles, technician roles, and the like, or combinations thereof.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Brandt to include the limitation(s) above as disclosed by Brandt. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data such as types of workers [see at least Brandt [0003-0009] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Brandt and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas in view of Akella, Tsuria, Santarone, and Zebarjadi as applied to claim(s) 1 above and further in view of Govindaswamy (US 2020/0223635 A1).

Regarding claim 21, modified Thomas teaches the system of claim 1, as well as the one or more antennas.

Modified Thomas doesn’t/don’t explicitly teach but Govindaswamy discloses
wherein the instructions, when executed by the at least one processing device, further cause the at least one processing device to:
automatically update the routing in real-time as the location data from the one or more items indicates changes in proximity of available members to the task location as the available members move in the care facility [see at least [0036-0038] computer;
[0019] “Under the system herein, human workers warehouse locations, and the locations of mobile picking robots or equipment, are all continuously tracked using real-time locator technology, such as including “beacons” engaged to human and robotic workers. Such beacons employ wireless broadcasts to communicate their individual identifier which is associated to the worker, and their individual respective locations to a network of beacon receivers throughout a warehouse or distribution center. Thus, the current location of each worker, be they human or robotic, in the warehouse can be continuously determined relative to locations of products to be picked. Then the assigned tasks for picking or replenishment of products can be calculated based on product location, and estimated time for a pick by any respective picking person or robot, to assign or reassign a picking task to a picking person or robot, which can accomplish it the quickest. Further, by monitoring travel routes through the warehouse, picking tasks can be reassigned based on locations and clogged or unclogged routes through the warehouse.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Thomas with Govindaswamy to include the limitation(s) above as disclosed by Govindaswamy. Doing so would further define modified Thomas’s (Thomas) [0002-0003] task management based on operating conditions data via real time location and reassigment [see at least Govindaswamy [0001-0009] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Thomas and b) Govindaswamy and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.

Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.

Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

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/JAMES WEBB/Examiner, Art Unit 3624

Prosecution Timeline

• Apr 13, 2023: Application Filed
• Dec 16, 2024: Non-Final Rejection mailed — §101, §103
• Feb 10, 2025: Interview Requested
• Feb 20, 2025: Examiner Interview Summary
• Feb 20, 2025: Applicant Interview (Telephonic)
• Mar 12, 2025: Response Filed
• May 06, 2025: Final Rejection mailed — §101, §103
• Jul 06, 2025: Interview Requested
• Jul 16, 2025: Applicant Interview (Telephonic)
• Jul 21, 2025: Examiner Interview Summary
• Aug 05, 2025: Request for Continued Examination
• Aug 11, 2025: Response after Non-Final Action
• Nov 25, 2025: Non-Final Rejection mailed — §101, §103
• Feb 11, 2026: Response Filed
• Jun 15, 2026: Final Rejection mailed — §101, §103 (current)