DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claims filed on November 19, 2025, for the application filed on September 25, 2023, which is a national stage of International Application No. PCT/EP2022/057561 filed on March 23, 2022, which claims the benefit of U.S. Provisional Application No. 63/168,327 filed on March 31, 2021. Claims 1 – 4, 7 – 14, 16 – 18, and 20 are currently pending and have been examined as discussed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application on January 2, 2026 after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 19, 2025 has been entered.
Claim Objections
Claim 11 is objected to because of the following informalities: the limitation “an initially assigned remote experts” in line 22 should be replaced with “an initially assigned remote expert”. Appropriate correction is required.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 8 – 11, 13, and 17 – 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Conry (U.S. Pub. No. 2006/0143044 A1) in view of Doyle (U.S. Pat. No. 10,528,329 B1), Prokle (International Pub. No. WO 2020016451 A1), and Deaven (U.S. Pub. No. 2005/0111711 A1).
Regarding independent claims 1, 11, and 18, Conry teaches the limitations of representative claim 11 in bold as:
A non-transitory computer readable medium storing instructions executable by at least one electronic processor to perform a remote assistance method (Paragraph [0034] of Conry, In a typical application, the request handler 42 may include communications hardware and software, such as a router and a server that may interact with the initiators, such as to acknowledge receipt of a scheduling request. The logic engine 44 may reside on the same program computer as the request handler 42, or on a connected system. The logic engine 44 will essentially consist of software for drawing upon resource and schedule data, such as performance knowledge that may be compiled in an integrated knowledge base (IKB) 46, as well as upon specific scheduling rules 50 and other data 52. In the instant application, the broadest reasonable interpretation of “a non-transitory computer readable medium storing instructions” reads on the integrated knowledge base (IKB) 46, specific scheduling rules 50 and/or other data 52 of Conry (Paragraph [0034]) and/or the machine readable medium of Conry (Claim 21) including instructions for accessing a knowledge base including time-based parameters for a plurality of resources for predetermined health care procedures, accessing schedules for a plurality of the resources, and adjusting the schedules based upon the time-based parameters and a plurality of requested health care procedures), comprising:
receiving an examination schedule comprising scheduled medical imaging examinations including information on the scheduled medical imaging examinations (Paragraph [0016] of Conry, [T]he scheduling system will receive requests for procedures and tasks, and schedule the procedures and tasks, along with the personnel and resources required, based upon the established knowledge of the performance information and the procedures. Paragraph [0038] of Conry, [T]he logic engine 44 may draw upon an IKB 46 which may be considered to include one or more knowledge bases, relational databases or any other data structure or associated data which compiles known characteristics and performance information, and, where desired, schedule data as well. Paragraph [0039] of Conry, The performance indications will provide a guide for times required for the various resources needed for the scheduled tasks and procedures. In conjunction with the schedules of the various entities (…equipment, facilities, supplies…, etc.), then, the logic engine 44 may implement the rules for scheduling the resources. Paragraph [0040] of Conry, It should be noted that, as used herein, the terms “performance” and “performance data” are intended to relate to a wide variety of information. As discussed herein, the information may be indicative of durations for procedures and durations of lead times, typically determined based upon historical data for the procedures... Similarly, the performance information may account for known abilities or limitations of facilities and equipment, such as imaging protocols, software versions, speeds of equipment, and so forth. In the instant application, the broadest reasonable interpretation of “receiving an examination schedule comprising scheduled medical imaging examinations including information on the scheduled medical imaging examinations” reads on the step of receiving in Conry (Paragraphs [0038] through [0040]) multiple examples of characteristics, performance information, and schedule data used by the logic engine 44 to implement the rules for scheduling the resources.);
receiving information on remote experts (Paragraph [0016] of Conry, [T]he scheduling system will receive requests for procedures and tasks, and schedule the procedures and tasks, along with the personnel and resources required, based upon the established knowledge of the performance information and the procedures, and the schedules of the personnel and components needed. Paragraph [0038] of Conry, [T]he logic engine 44 may draw upon an IKB 46 which may be considered to include one or more knowledge bases, relational databases or any other data structure or associated data which compiles known characteristics and performance information, and, where desired, schedule data as well. Paragraph [0039] of Conry, The performance indications will provide a guide for times required for the various resources needed for the scheduled tasks and procedures. In conjunction with the schedules of the various entities (personnel, … etc.), then, the logic engine 44 may implement the rules for scheduling the resources. Paragraph [0040] of Conry, It should be noted that, as used herein, the terms “performance” and “performance data” are intended to relate to a wide variety of information. As discussed herein, the information may be indicative of durations for procedures and durations of lead times, typically determined based upon historical data for the procedures, expert estimates, preferences provided by physicians and others, and so forth. However, terms also include such factors as skills of persons involved in the procedures, training levels, and so forth. In the instant application, the broadest reasonable interpretation of “receiving information on remote experts” reads on the step of receiving in Conry (Paragraphs [0038] through [0040]) multiple examples of characteristics, performance information, and schedule data specifically related to personnel, experts, physicians, service providers, and persons involved in the procedures and used by the logic engine 44 to implement the rules for scheduling the resources.);
iteratively, for training a likelihood estimation model for one or more scheduled medical imaging examinations, applying a rules-based model to the information on the scheduled medical imaging examination to determine a likelihood of needing remote expert assistance for the one or more scheduled medical imaging examinations (Paragraph [0020] of Conry, In general, the patient 22 will be serviced by specialists 24... In addition, a range of support staff 26... play a key role in the provision of health care. Such staff may include staff specialized in ... imaging procedures, ... and so forth. All of these contributors to the health care system will be required to be scheduled for the necessary procedures and care provided to the patient 22. Paragraph [0032] of Conry, [S]chedules, particularly for appointments and procedures, may be based upon various characteristics either of the request/initiator or of the resources needed to be scheduled based upon the request, or both. Moreover, various types of classification may be implemented by the request handler 42 and subsequently by a logic engine 44 and other components of system 10... Characteristics of and classification by the entities involved include an indication of professional entities, support staff, equipment, and so forth... Such characteristics and classification may further include, for example, a modality and type of system (e.g., an imaging modality such as MRI, CT, PET, Ultrasound, X-Ray, tomosynthesis, etc.), the manufacturer of the system, the model of the system, the age of the system, the condition of the system, and so forth. Such characteristics and classifications may be used, as described in greater detail below, to identify the resources required for fulfilling the scheduling request and performing the desired tasks and procedures. In the instant application, the broadest reasonable interpretation of “determine a likelihood of needing remote expert assistance for the one or more scheduled medical imaging examinations” reads on applying the activity in Conroy (Paragraphs [0020] and [0032]), of determining schedules, particularly for appointments and procedures, based upon various characteristics either of the request/initiator or of the resources needed to be scheduled based upon the request, or both (e.g., the entities involved include an indication of professional entities, support staff, equipment, and so forth; the resources required for fulfilling the scheduling request and performing the desired tasks and procedures.);
applying the likelihood estimation model to determine likelihoods of needing remote expert assistance for the scheduled medical imaging examinations based on the information on the scheduled medical imaging examinations (Paragraph [0020] of Conry, In general, the patient 22 will be serviced by specialists 24... In addition, a range of support staff 26... play a key role in the provision of health care. Such staff may include staff specialized in ... imaging procedures, ... and so forth. All of these contributors to the health care system will be required to be scheduled for the necessary procedures and care provided to the patient 22. Paragraph [0032] of Conry, [S]chedules, particularly for appointments and procedures, may be based upon various characteristics either of the request/initiator or of the resources needed to be scheduled based upon the request, or both. Moreover, various types of classification may be implemented by the request handler 42 and subsequently by a logic engine 44 and other components of system 10... Characteristics of and classification by the entities involved include an indication of professional entities, support staff, equipment, and so forth... Such characteristics and classification may further include, for example, a modality and type of system (e.g., an imaging modality such as MRI, CT, PET, Ultrasound, X-Ray, tomosynthesis, etc.), the manufacturer of the system, the model of the system, the age of the system, the condition of the system, and so forth. Such characteristics and classifications may be used, as described in greater detail below, to identify the resources required for fulfilling the scheduling request and performing the desired tasks and procedures. In the instant application, the broadest reasonable interpretation of “applying a likelihood estimation model to determine likelihoods of needing remote expert assistance for the scheduled medical imaging examinations based on the information on the scheduled medical imaging examinations” reads on applying the logic engine 44 of Conroy (Paragraph [0032]) to identify, via the characteristics and classification of the request, the resources required for fulfilling the scheduling request and performing the desired tasks and procedures (i.e., identifying the “professional entities” involved for a modality and type of system, e.g., an imaging modality such as MRI, CT, PET, Ultrasound, X-Ray, tomosynthesis, etc.).), wherein the likelihood estimation model utilizes a reinforcement learning (RL) model to dynamically adapt as new training information is received;
applying a load-balancing optimization model to assign remote experts to the scheduled medical imaging examinations of the examination schedule based on the determined likelihoods of needing remote expert assistance and the information on the remote experts (Paragraph [0041] of Conry, As described in greater detail below, the data accessed and analyzed for use by the logic engine 44, and that may be included in the IKB 46, may include any data related to performance of any one of the components of the health care system. For example, historic records indicative of appointment times, procedure durations, and so forth may be accessed for any one or all of the components and analyzed to determine an appropriate time for the scheduled procedure or task. Appointment times for each of the activities may be calculated based upon a combination of factors, moreover. Such factors may include, for example, estimated time for a particular type of appointment independent of specific professionals involved, or independent of specific equipment involved. Likewise, an average appointment time could be personalized for each individual contributor or interaction of specific contributors. Moreover, appointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the IKB. In a present implementation, once analyzed as described below, the information with regards to such persons and equipment may be stored in the IKB for reference by the logic engine in scheduling the times for the resources and the durations for procedures and tasks. Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors, equipment, facilities and supplies needed for the activities.) by:
simulating a work shift schedule of an initially assigned remote experts handling the examination schedule to simulate demands on the remote expert time (Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors, equipment, facilities and supplies needed for the activities. A reconciler module 54 identifies any conflicts that may exist, and may resolve such conflicts with or without human intervention. In a typical application, the reconciler module 54 will include software designed to operate on the schedules or adjustments to the schedules determined by the logic engine 44.);
calculating one or more key performance indicators (KPls) from results of the simulating, wherein the KPls include one or more of patient wait times and scanner utilization (Paragraph [0050] of Conry, The reconciler module 54 may include its own rules, or may draw upon rules 50, such as for resolving conflicts based upon priority levels. The reconciler module may access additional schedules, such as for alternative resources to resolve such conflicts. In the presently complemented implementations, the reconciliation is at least partially based upon human intervention, or on a first come-first served basis following times of receipt of the scheduling requests.); and
optimizing the assignments of the remote experts to the scheduled medical imaging examinations based on the one or more KPls (Paragraph [0051] of Conry, When all scheduling issues have been reconciled, the process advances to a synchronization module 56 where the various schedules are updated. The synchronization module 56 may thus altar the schedules as indicated in FIG. 2, adding or adjusting time slots that may be represented in user-viewable presentations in a conventional manner. The process also preferably generates notices 58 for apprising the individual contributors and managers of facilities and equipment of the schedules. These notices may be sent in any suitable manner, just as electronically. In certain cases, the notices will be output for staff personnel who will contact patients, physicians, and other staff to apprise them of new scheduling of appointments and procedures, as well as changes in the scheduling. Finally, the process may output orders or commands for specific resources, supplies, field replaceable units, and so forth as indicated at reference numeral 16 in FIG. 2.);
providing a remote assistance interface via which a local operator performing a scheduled medical imaging examination can receive remote assistance from a remote expert; and
initiating a remote assistance session via the remote assistance interface for the scheduled medical imaging examination being performed by automatically connecting the local operator with the remote expert assigned to the scheduled medical imaging examination being performed.
Conry discloses that medical diagnostic imaging equipment may remotely accessed and further that the experience level, the training level, and so forth of all persons involved in the specific scheduled procedure or task may be evaluated and taken into account when determining appointment times. (Paragraph [0005] of Conry, [M]edical diagnostic imaging, monitoring, treatment and other equipment are often maintained in a good working state by remotely accessing the equipment and providing remote service, where possible, and by on-site service where needed. Paragraph [0041] of Conry, Appointment times for each of the activities may be calculated based upon a combination of factors…. [A]ppointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the IKB. Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors… needed for the activities.).
Conry does not appear to explicitly disclose, but Doyle teaches the limitations identified in bold identified as “iteratively, for training a likelihood estimation model for one or more scheduled medical imaging examinations, applying a rules-based model to the information on the scheduled medical imaging examination to determine a likelihood of needing remote expert assistance for the one or more scheduled medical imaging examinations” (Col. 49, Lines 48 – 58 of Doyle. In the instant application, the broadest reasonable interpretation of “iteratively, for training a likelihood estimation model for one or more scheduled medical imaging examinations, applying a rules-based model to the information on the scheduled medical imaging examination” reads on the activity in Doyle (Col. 49, Lines 48 – 58) of iteratively applying the corresponding sets of rules associated with the parent classes the parent hierarchies of this lowest hierarchy).
Conry does not appear to explicitly disclose, but Doyle teaches the limitations identified in bold as “applying the likelihood estimation model to determine likelihoods of needing remote expert assistance for the scheduled medical imaging examinations based on the information on the scheduled medical imaging examinations, wherein the likelihood estimation model utilizes a reinforcement learning (RL) model to dynamically adapt as new training information is received” (Col.40, Line 60 to Col.41, Line 4 of Doyle. In the instant application, the broadest reasonable interpretation of “the likelihood estimation model utilizes a reinforcement learning (RL) model to dynamically adapt as new training information is received” reads on the training instance in Doyle (Col.40, Line 60 to Col.41, Line 4) including one or more analogical reasoning tasks that include words, phrases, etc. as well as vector arithmetic and/or additive compositionality to iteratively calibrate the word embedding or term embedding module in a … reinforcement learning environment.).
Conry does not appear to explicitly disclose, but Prokle teaches the limitations identified in bold identified as “simulating a work shift schedule of an initially assigned remote experts handling the examination schedule to simulate demands on the remote expert time” (Paragraphs [0035] – [0037], and [0041] – [0042] of Prokle. In the instant application, the broadest reasonable interpretation of “simulate demands on the remote expert time” reads on the simulation software of Prokle (Paragraphs [0035] – [0037], and [0041] – [0042]) used to create a digital model of a planned workflow and simulate potential schedules as “what-if’ scenarios, taking into account available situational awareness information (such as medical personnel availability based on whether they have clocked in for work, more finely grained locational information provided by a Real Time Locating Service (RTLS), location of outpatients via GPS (when available and authorized by the patient), status of imaging systems obtained from the Radiology Information System (RIS), and/or so forth). The workflow schedule optimizer can adjust aspects of the simulated workflow schedule in accordance with a set of business constraints/restrictions/priorities in order to generate schedule adjustments.).
Conry does not appear to explicitly disclose, but Prokle teaches the limitations identified in bold identified as “calculating one or more key performance indicators (KPls) from results of the simulating, wherein the KPls include one or more of patient wait times and scanner utilization” (Paragraph [0039] of Prokle. In the instant application, the broadest reasonable interpretation of “the KPls include one or more of patient wait times and scanner utilization” reads on the suitable KPIs of Prokle (Paragraph [0039]) including staff utilization, room utilization, total wait time, last patient exit-elapsed time (corresponding to the total length of the imaging work shift), and so forth.).
Conry does not appear to explicitly disclose, but Deaven teaches the limitations identified in bold identified as “providing a remote assistance interface via which a local operator performing a scheduled medical imaging examination can receive remote assistance from a remote expert” (Paragraphs [0014], [0017], and [0024] – [0025] of Deaven. In the instant application, the broadest reasonable interpretation of “providing a remote assistance interface via which a local operator performing a scheduled medical imaging examination can receive remote assistance from a remote expert” reads on the scanner console 24, the remote console 26, the network 28, and/or the voice or text communications of Deaven (Paragraphs [0014], [0017], and [0024] – [0025]) via which the local technologist performing the scheduled medical imaging can receive remote assistance from the expert (i.e., expert in the particular anatomical region or of the particular imaging modality outside the expertise of the local technologist) for expert processing.).
Conry does not appear to explicitly disclose, but Deaven teaches the limitations identified in bold identified as “initiating a remote assistance session via the remote assistance interface for the scheduled medical imaging examination being performed by automatically connecting the local operator with the remote expert assigned to the scheduled medical imaging examination being performed” (Paragraphs [0014], [0017], and [0024] – [0025] of Deaven. In the instant application, the broadest reasonable interpretation of “initiating a remote assistance session via the remote assistance interface for the scheduled medical imaging examination being performed by automatically connecting the local operator with the remote expert assigned to the scheduled medical imaging examination being performed” reads on the scanner console 24, the remote console 26, and/or the voice or text communications of Deaven (Paragraphs [0014], [0017], and [0024] – [0025]) connecting the local technologist with the remote expert. The Office has determined that automating a manual activity of connecting the local technologist with the remote expert is a common practice requiring only ordinary skill in the art and hence is considered a routine expedient. See MPEP 2144.04(III). Nothing in the claims in the claims or specification establishes criticality of automatically connecting, as opposed to manually connecting.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical imaging diagnostics and scheduling to modify the method and system of Conry to: implement the activity of iteratively, for training a likelihood estimation model for one or more scheduled medical imaging examinations, applying a rules-based model to the information on the scheduled medical imaging examination to determine a likelihood of needing remote expert assistance for the one or more scheduled medical imaging examinations, and implement the activity of applying the likelihood estimation model to determine likelihoods of needing remote expert assistance for the scheduled medical imaging examinations based on the information on the scheduled medical imaging examinations, wherein the likelihood estimation model utilizes a reinforcement learning (RL) model to dynamically adapt as new training information is received, as taught by Doyle (Col. 49, Lines 48 – 58; Col.40, Line 60 to Col.41, Line 4) in order to provide a method, system, and computer program product for classifying digital data using real-time computing techniques (Col.2, Lines 45 – 47 of Doyle); implement the activity of simulating a work shift schedule of an initially assigned remote experts handling the examination schedule to simulate demands on the remote expert time and implement the activity of calculating one or more key performance indicators (KPls) from results of the simulating, wherein the KPls include one or more of patient wait times and scanner utilization, as taught by Prokle (Paragraphs [0035] – [0037], [0039], and [0041] – [0042]) in order to provide information for consideration by a Radiology Department manager in allocating departmental resources and/or advocating for increased departmental resources (Paragraph [0038] of Prokle); and implement the activity of providing a remote assistance interface via which a local operator performing a scheduled medical imaging examination can receive remote assistance from a remote expert and implement the activity of initiating a remote assistance session via the remote assistance interface for the scheduled medical imaging examination being performed by automatically connecting the local operator with the remote expert assigned to the scheduled medical imaging examination being performed, as taught by Deaven (Paragraphs [0014], [0017], and [0024] – [0025]) in order to find techniques to facilitate or share the processing of the image data (Paragraph [0003] of Deaven).
Regarding claims 3 and 13, Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1 and 11 teaches the limitations of representative claim 3 in bold as “for each scheduled medical imaging examination, applying a machine-learning (ML) model to the information on the scheduled medical imaging examination to determine a likelihood of needing remote expert assistance for the scheduled medical imaging examination” (Paragraphs [0005], [0037], [0041], and [0050]. [M]edical diagnostic imaging, monitoring, treatment and other equipment are often maintained in a good working state by remotely accessing the equipment and providing remote service, where possible, and by on-site service where needed. Paragraph [0037] of Conry, The logic engine 44 may perform such scheduling based upon any suitable type and structure of processing, such as neural networks. Paragraph [0041] of Conry, [T]he the data accessed and analyzed for use by the logic engine 44, and that may be included in the IKB 46, may include any data related to performance of any one of the components of the health care system… Appointment times for each of the activities may be calculated based upon a combination of factors… [A]ppointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the integrated knowledge base (IKB). Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors… needed for the activities. In the instant application, the broadest reasonable interpretation of “machine-learning (ML) model” reads on the logic engine of Conry (Paragraphs [0005], [0037], [0041], and [0050]) performing scheduling based upon any suitable type and structure of processing, such as neural networks, to identify the necessary resources, their schedules, and determine when and for how long such resources must be scheduled to accommodate those requests.).
Regarding claims 8 and 17, Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1 and 11 teaches the limitations of representative claim 8 in bold as “the information on the remote experts includes historical remote assistance performance data related to the remote expert” (Paragraph [0040] of Conry, It should be noted that, as used herein, the terms “performance” and “performance data” are intended to relate to a wide variety of information. As discussed herein, the information may be indicative of durations for procedures and durations of lead times, typically determined based upon historical data for… expert estimates, preferences provided by physicians and others, and so forth. Paragraph [0041] of Conry, [H]istoric records indicative of appointment times, procedure durations, and so forth may be accessed for any one or all of the components and analyzed to determine an appropriate time for the scheduled procedure or task… [A]ppointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the integrated knowledge base (IKB).).
Regarding claim 9, Conry as modified by Doyle, Prokle, and Deaven and applied to claim 1 teaches the limitations in bold as “the applying of the load-balancing optimization model occurs before a workshift in which the scheduled medical imaging examinations of the examination schedule are performed” (Paragraph [0018] of Deaven, In general, the collaborative imaging system 10 provides for collaborative sessions to initiated or joined from the various nodes on the network, such as at the scanner console 24, PACS Server 22, and/or remote consoles 26. In practice, the collaborative environment may be established using various techniques that allow for the concurrent review and/or operation of all or part of a common screen or user interface from each participating node. For example, the software rendering and visualization tools used by the technologist and/or radiologist may be provided to the various participating nodes, allowing operators at those nodes to concurrently view, modify, or process an image data set during the collaborative session. Furthermore, operators at the different nodes joined in a collaborative session may be in communication with one another during the session, such as over a separate voice line, over the network via text-based messaging, or over the network using an audio protocol, such as Voice-over-Internet (VOI). In addition, participants to a collaborative session may attach or share multimedia objects that may be retrieved and/or played by other session participants. In the instant application, the broadest reasonable interpretation of “the applying of the load-balancing optimization model occurs before a workshift in which the scheduled medical imaging examinations of the examination schedule are performed” reads on the collaborative environment of Conry (Paragraph [0018]) established (i.e., the load-balancing module is applied) before a workshift in which the scheduled medical imaging examination is performed. Even assuming only for argument sake, which the Office does not concede, that the collaborative environment of Conry does not occur before the workshift, but rather during the workshift, it would have been obvious to one of ordinary skill in the art of medical imagining diagnostics and scheduling to change the sequence of Conry such that the collaborative environment is established (i.e., the load-balancing module is applied) before the workshift because “selection of any order of performing process steps is prima facie obviousness in the absence of new or unexpected results.” See MPEP 2144.04(III).).
Regarding claim 10, Conry as modified by Doyle, Prokle, and Deaven and applied to claim 1 teaches the limitations in bold as “the applying of the load-balancing optimization model occurs during a workshift in which the scheduled medical imaging examinations of the examination schedule are performed” (Paragraph [0018] of Deaven, In general, the collaborative imaging system 10 provides for collaborative sessions to initiated or joined from the various nodes on the network, such as at the scanner console 24, PACS Server 22, and/or remote consoles 26. In practice, the collaborative environment may be established using various techniques that allow for the concurrent review and/or operation of all or part of a common screen or user interface from each participating node. For example, the software rendering and visualization tools used by the technologist and/or radiologist may be provided to the various participating nodes, allowing operators at those nodes to concurrently view, modify, or process an image data set during the collaborative session. Furthermore, operators at the different nodes joined in a collaborative session may be in communication with one another during the session, such as over a separate voice line, over the network via text-based messaging, or over the network using an audio protocol, such as Voice-over-Internet (VOI). In addition, participants to a collaborative session may attach or share multimedia objects that may be retrieved and/or played by other session participants. In the instant application, the collaborative environment of Conry (Paragraph [0018]) established (i.e., the load-balancing module is applied) before a workshift in which the scheduled medical imaging examination is performed. However, it would have been obvious to one of ordinary skill in the art of medical imagining diagnostics and scheduling to change the sequence of Conry such that the collaborative environment is established (i.e., the load-balancing module is applied) during the workshift because “selection of any order of performing process steps is prima facie obviousness in the absence of new or unexpected results.” See MPEP 2144.04(III).).
Claims 2 and 12 are rejected under 35 U.S.C. 103(a) as being unpatentable over Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1 and 11, and further in view of Verner (U.S. Pub. No. 2021/0030502 A1).
Regarding claims 2 and 12, Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1 and 11 teaches the limitations of representative claim 2 in bold as “the load-balancing optimization model employs weighted averaging where one or more assigned examinations are weighted by the determined likelihoods of needing remote expert assistance and adjusting the examination assignments amongst the remote experts using a load metric for one or more of the remote experts, wherein the load metric is minimized for the remote experts based at least in part on the determined likelihoods of needing remote expert assistance” (Paragraph [0041] of Conry, As described in greater detail below, the data accessed and analyzed for use by the logic engine 44, and that may be included in the IKB 46, may include any data related to performance of any one of the components of the health care system. For example, historic records indicative of appointment times, procedure durations, and so forth may be accessed for any one or all of the components and analyzed to determine an appropriate time for the scheduled procedure or task. Appointment times for each of the activities may be calculated based upon a combination of factors, moreover. Such factors may include, for example, estimated time for a particular type of appointment independent of specific professionals involved, or independent of specific equipment involved. Likewise, an average appointment time could be personalized for each individual contributor or interaction of specific contributors. Moreover, appointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the IKB. In a present implementation, once analyzed as described below, the information with regards to such persons and equipment may be stored in the IKB for reference by the logic engine in scheduling the times for the resources and the durations for procedures and tasks. Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors, equipment, facilities and supplies needed for the activities.).
Conry as modified by Doyle, Prokle, and Deaven and applied to claims 1 and 11 does not appear to explicitly disclose, but Verner teaches the limitations identified in bold as “the load-balancing optimization model employs weighted averaging where one or more assigned examinations are weighted by the determined likelihoods of needing remote expert assistance and adjusting the examination assignments amongst the remote experts using a load metric for one or more of the remote experts, wherein the load metric is minimized for the remote experts based at least in part on the determined likelihoods of needing remote expert assistance” (Second and Third Paragraphs on age 2881 of NPL Agor, The goal of this research is to propose a workload score that can be calculated for each provider team in real-time and test it using simulation. The score should accurately represent the amount of work each team is currently experiencing, so that assignment decisions can be made accordingly. By assigning incoming patients to the provider team with the lowest workload score instead of the lowest patient census, we hypothesize that patient workload will be more equitably balanced across HIM provider teams. First Paragraph on age 2885 of NPL Agor, These steps resulted in a list of 1000 possible situations (i.e. ten-factor combinations), each with an associated utility value representing the workload that the situation might imply. Situations with higher utility values were considered to have heavier workloads, and those with lower utility values were considered to have lighter workloads. Second Paragraph on age 2885 of NPL Agor, A linear optimization model was created to find the optimal weights for each of the ten factors included in the workload score. Essentially, the optimization model aimed to (i) minimize the number of situations that deviated from the ordered grouping obtained from the conjoint analysis results and (ii) keep the total of the factor weights in each broad category as close as possible to the relative category rankings from the Delphi survey results. First Paragraph on age 2886 of NPL Agor, The constraints in (1c) ensure that the sum of the weights of the elements within each broad category (e.g. patient churn, patient complexity, work interruptions, and work inefficiency) are in order with respect to their average rankings from the Delphi survey. In the instant application, the broadest reasonable interpretation of “the load-balancing optimization model employs weighted averaging … and adjusting the examination assignments … using a load metric for one or more of the remote experts, wherein the load metric is minimized” reads on the linear optimization model in NPL Agor (Second and Third Paragraphs on age 2881 of NPL Agor and Second Paragraph on age 2885) sum of the weights of elements in order with respect to average ranking to calculate a workload score for each provider team in real-time to accurately represent the amount of work each team is currently experiencing, so that assignment decisions can be made accordingly.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical imaging diagnostics and scheduling to modify the system of Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1 and 11 to implement the load-balancing optimization model employing weighted averaging where one or more assigned examinations are weighted by the determined likelihoods of needing remote expert assistance and adjusting the examination assignments amongst the remote experts using a load metric for one or more of the remote experts, wherein the load metric is minimized, as taught by NPL Agor (Second and Third Paragraphs on age 2881 of NPL Agor and Second Paragraph on age 2885) in order to more equitably balance patient workload across HIM provider teams (Second and Third Paragraphs on age 2881 of NPL Agor).
Claims 4 and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 3 and 13, and further in view of Amble (U.S. Pub. No. 2020/0221951 A1).
Regarding claims 4 and 14, Conry discloses that the neural network (i.e., machine learning model) may evaluate and take into account the experience level, the training level, and so forth of all persons involved in the specific scheduled procedure or task (i.e., historical data related to all persons involved as stored in the integrated knowledge base (IKB)) when determining appointment times (Paragraph [0005] of Conry, [M]edical diagnostic imaging, monitoring, treatment and other equipment are often maintained in a good working state by remotely accessing the equipment and providing remote service, where possible, and by on-site service where needed. Paragraph [0037] of Conry, The logic engine 44 may perform such scheduling based upon any suitable type and structure of processing, such as neural networks. Paragraph [0041] of Conry, [T]he the data accessed and analyzed for use by the logic engine 44, and that may be included in the IKB 46, may include any data related to performance of any one of the components of the health care system… Appointment times for each of the activities may be calculated based upon a combination of factors… [A]ppointment times may be trended according to subgroups of individual contributors, such as by reference to the actual person involved, the person's experience level, the training level, and so forth. All persons involved in the specific scheduled procedure or task may thus be evaluated and such data taken into account in the integrated knowledge base (IKB). Paragraph [0050] of Conry, The logic engine thus assigns time slots for the activities and for the contributors… needed for the activities.).
Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 3 and 13 does not appear to explicitly disclose, but Amble teaches the limitation of representative claim 4 in bold as “training the ML model on historical data related to the remote expert and retrieved from a database” (Paragraph [0065] of Amble, Machine learning can be used to study and construct algorithms that can learn from and make predictions on data... The model is initially fit on a training dataset, that is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the logic engine (e.g., machine readable medium) of Conry as modified by Deaver to further implement the activity of training, as taught by Amble (Paragraph [0065]), the neural networking (Paragraph [0037] and machine learning model in claim 21 of Conry) using the historical data of the persons involved (Paragraph [0041] of Conry).
Therefore, it would have been obvious to one of ordinary skill in the art of medical imaging diagnostics and scheduling to modify the system of Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 3 and 13 to implement the activity of training the ML model on historical data related to the remote expert and retrieved from a database, as taught by Amble (Paragraph [0065]) in order to provide telemedicine data that can be acted upon immediately for real-time and near real time analysis, and/or be saved for later use for big-data type analysis in order to improve future telemedicine practices (Paragraph [0002] of Amble).
Claims 7, 16, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1, 11, and 18, and further in view of Bollapragada (U.S. Pub. No. 2011/0125539 A1).
Regarding claims 7, 16, and 20, Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1, 11, and 18 does not appear to explicitly disclose, but Bollapragada teaches the limitations of representative claim 7 in bold as “the simulating is performed with a Discrete Event Simulation (DES) simulator” (Paragraph [0052] of Bollapragada, In certain embodiments, multiple simulation modalities are employed including a critical path method coupled to discrete event…simulation. Using this coupling, one or more objectives of the process may be assessed.)
Therefore, it would have been obvious to one of ordinary skill in the art of medical imaging diagnostics and scheduling to further modify the system of Conry as modified by Doyle, Prokle, and Deaven and applied to an associated one of claims 1, 11, and 18 to implement the simulating being performed with a Discrete Event Simulation (DES) simulator, as taught by Bollapragada (Paragraph [0052]) in order to provide one or more methodologies to schedule resources that are available for radiology exams to help ensure that the exams can be performed as scheduled (Paragraph [0028] of Bollapragada).
Response to Amendment
Applicant's arguments (Second Paragraph on Page 9 to First Paragraph on Page 13 of the Amendment filed November 19, 2025) regarding the rejections of claims 1, 3, 6, 8 – 11, 13, and 17 – 19 under 35 U.S.C. § 103 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
Conclusion
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686