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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d), with respect to parent Application No. EP22159486, filed on 3/1/2022. Further acknowledgment is made of applicant’s provisional Application, filed on 12/3/2021.
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 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/4/2025 has been entered.
Claims 1, 8, 18 and 20 are presently amended.
Claims 2 and 6 are canceled.
Claims 1 and 3-5 and 7-20 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant' s arguments filed 12/4/2025 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, but are not persuasive for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “…the claims are not directed to "methods of organizing human activity," as asserted by the Examiner. Applicant respectfully submits that operations for "applying a machine learning (ML) component to the other schedules, a likelihood of interruption of the medical professional…" as recited in amended claim 1, are not "methods of organizing human activity," as asserted by the Examiner. Such operations do not fall under any of the "methods of organizing human activity," including: "fundamental economic practices" or "fundamental economic principles," "commercial interactions" or "legal interactions," or "managing personal behavior or relationships or interactions between people."
The claims are not directed to managing personal behavior or relationships or interactions between people. There are no social activities, or teaching, or following rules for games or hedging or anything analogous. Instead, the claims are for managing a schedule by coordinating with other schedules. Read as a whole, claims 1, 18 and 20 cannot be properly interpreted as managing behavior of a person, such as by prompting a user for input or to perform any act whatsoever. Instead, substantially all features in the claims are for managing a schedule by coordinating with other schedules. Indeed, the combinations of features in these claims cannot be reasonably interpreted as being performable, for example, in a human mind. There is no personal behavior being managed, and any interaction is not an interaction between people. Nor is there any following of rules by people.” [Arguments, pages 9-10].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and observes that the presence of “a machine learning (ML) component” does not preclude a claim from reciting an abstract idea or more broadly a judicial exception. Examiner observes that, the claims, when considered as a whole, are directly primarily to managing interruptions to the schedules of medical professionals. Examiner thus respectfully maintains that the thrust of the present invention describes steps for managing personal behavior as well as interactions between people and following rules and instructions. Therefore, claims 1, 18 and 20 recite concepts identified as abstract ideas. As such, Examiner remains unpersuaded.
Second, Applicant argues that “The pending claims are an ordered combination of features that provide a technological solution to a technological problem. As should be evident from the claim language itself, the coordination of schedules is a complex undertaking, such as due to electronic schedules being maintained by different parties on their own devices. To the extent that schedules of others can be maintained or received, finding likelihoods of interruptions is itself complex, particularly when these likelihoods may be due to something other than simple direct schedule conflicts. Computing or estimating likelihood(s) of interruptions based on nuanced factors is essentially impossible for a human to do systematically, and application of a machine learning (ML) component to compute or estimate such likelihoods is seemingly the only realistic way to coordinate schedules. Here, the nuanced information is stated in the claims as factors that may be considered. If this type of task were possible for humans to do previously, there would be art describing humans doing it. But there is no anticipatory art describing these features…” [Arguments, page 10].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present invention recites an abstract idea without significantly more.
First, with regard to the argument that the pending claims are an ordered combination of features that provide a technological solution to a technological problem, Examiner respectfully disagrees and maintains that the presently amended claims, when considered in light of the recited additional elements, add nothing that is not already present when the steps are considered separately’" and simply recite intermediated settlement as performed by a generic computer." 573 U.S. at 225 (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1972).
In particular, claims 1, 18 and 20 only recite the following additional elements –
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 1],
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 18],
…a machine learning (ML) component…; …an electronic processing device… [Claim 20].
The dependent claims recite the following new additional elements –
… wherein the ML component comprises one of a time-dependent Markov model, a time-dependent finite element model, a time- dependent finite element modeling (FEM), long-short-term memories (LSTM), or a recurrent neural network (RNN) that is trained… (Claim 8),
The apparatus, machine learning component and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not demonstrate an ordered combination or otherwise integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application. As such, Examiner remains unpersuaded.
Third, regarding dependent claim 8, Applicant argues that “Regarding prong one of Step 2A, Applicant respectfully submits that the dependent claim 8 is not directed to "methods of organizing human activity,"… operations "wherein the ML component comprises a time- dependent Markov model, a time-dependent finite element model, a long-short-term memories (LSTM), or a recurrent neural network (RNN) that is trained based on historical information on interruptions to an owner of an electronic calendar regarding each combination of the type of procedure and protocol scheduled at the imaging modality, the patient's characteristic, or the experience level of a local technologist operating the imaging modality," as recited in amended claim 8, are not "methods of organizing human activity," as asserted by the Examiner. Such operations do not fall under any of the "methods of organizing human activity," including: "fundamental economic practices" or "fundamental economic principles," commercial interactions" or "legal interactions," or "managing personal behavior or relationships or interactions between people." …
Regarding prong two of Step 2A, per MPEP 2106.05(f) "[w]hen determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. ... (3) The particularity or generality of the application of the judicial exception. "
Unlike Example 48 claim 3, the dependent claim 8 as amended provides "details of how a solution to a problem is accomplished" and provided particularity "of the application of the judicial exception."
Here, amended claim 8 does not generally recite a medical professional scheduling model in the abstract. Instead, the medical professional scheduling model is itself dependent on historical information on interruptions to an owner of an electronic calendar regarding each combination of the type of procedure and protocol scheduled at the imaging modality, the patient's characteristic, or the experience level of a local technologist operating the imaging modality. Further, the machine learning component is specified to comprise a time-dependent Markov model, a time-dependent finite element model, a long-short-term memories (LSTM), or a recurrent neural network (RNN).
Furthermore, the claims do not pre-empt medical professional scheduling models, except in conjunction with all the other recited features. See, Diamond v. Diehr, 450 U.S. 175, 187 (finding that the claimed "process admittedly employs a well-known mathematical equation, but they do not seek to pre-empt the use of that equation. Rather, they seek only to foreclose from others the use of that equation in conjunction with all of the other steps in their claimed process."). Thus, the claims are not directed to an abstract idea since the claims do not preempt every possible way of balancing schedules for medical professionals” [Arguments, pages 11-12].
In response, Applicant’s arguments are considered but are not persuasive. With respect to the assertion that “Regarding prong one of Step 2A, Applicant respectfully submits that the dependent claim 8 is not directed to "methods of organizing human activity,", Examiner respectfully disagrees for the same reasons as stated for claim 1. Particularly, Examiner respectfully observes that the presence of “a machine learning (ML) component” does not preclude a claim from reciting an abstract idea or more broadly a judicial exception. Examiner further observes that, claim 8, when considered as a whole and in light on the claims on which it depends, is directly primarily to managing interruptions to the schedules of medical professionals. Examiner thus respectfully maintains that the thrust of the present invention describes steps for managing personal behavior as well as interactions between people and following rules and instructions. Therefore, claim 8 recites concepts identified as abstract ideas. As such, Examiner remains unpersuaded.
Further, with regard to the argument that Unlike Example 48 claim 3, the dependent claim 8 as amended provides "details of how a solution to a problem is accomplished" and provided particularity "of the application of the judicial exception.", Examiner respectfully disagrees and maintains that claim 8 of the present invention does not demonstrate sufficient details with regard to how a solution to a problem is accomplished. In particular, Examiner observes that claim 8 of the present invention states that any one of five different machine learning models is trained using any one of three different types of data. Examiner observes that the claim does not specify how the data is used in the training process, and therefore the claim does not provide sufficient details of how a solution to a problem is accomplished. As such, Examiner remains unpersuaded.
35 USC § 103 Rejections
First, Applicant argues that “…the current grounds for rejection are moot, particularly
in view of the amended claim language. First, the Examiner has not established that the proposed
combination discloses "computing, by applying a machine learning (ML) component to the other
schedules, a likelihood of interruption of the medical professional as a function of time from the maintained or received other schedules of future tasks," as recited in amended claim 1. The Examiner relies on Garber as disclosing such a feature. Applicant cannot agree. Specifically, Garber is directed to determining a "real-time" delay as set forth in operation 604 in FIG. 6 of Garber. Thus, Garber does not disclose "computing… a likelihood of interruption…” … Further, Mashin-Chi does not appear to cure Garber of this failure…” [Arguments, pages 13-15].
In response, Applicant’s arguments are considered but are not persuasive. With respect to the argument suggesting that “Garber is directed to determining a "real-time" delay as set forth in operation 604 in FIG. 6 of Garber. Thus, Garber does not disclose "computing… a likelihood of interruption… as a function of time from the maintained or received other schedules of future tasks”… Examiner respectfully disagrees and directs the Applicant to (Garber, ¶ 116, FIGS. 3A and 3B depict two schematic maps illustrating the planned daily schedule of two field professionals and the updated daily schedule of the two field professionals. As shown in FIG. 3A, the first field professional was assigned to tasks for providing technical service at locations A, B, C, D; and the second field professional was assigned to tasks for providing technical service at locations E, F, G, H. The planned route of the first field professional is illustrated in a dashed line and the planned route of the second field professional is illustrated in a solid line. Assuming that the first field professional was scheduled to be at location “A” at 10:36 and at location “B” at 11:39; and the second field professional was scheduled to be at location “E” at 10:15 and at location “F” at 11:09. In the illustrated example, server 152 received at 9:15, from network interface 206, real-time information for the first and second field professionals. In one embodiment, the real-time information may include current location information derived at least partially from location circuits of field professionals' communication devices 180A. For example, the real-time information may indicate that first field professional is stuck on the road to location “A.” In another embodiment, the real-time information may include task status updates transmitted from field professionals' communication devices 180A. For example, the real-time information may indicate that the second field professional had finished the assignment earlier than the estimated time for the completion of the task associated with location “E.” Based on the real-time progress information, and as shown in FIG. 3B, server 152 may reassign the first field professional to a task associated location “F,” and reassign the second field professional to a task associated location “A.” Thus, the updated schedule of first field professional includes tasks associated with locations F, B, C, and D and the updated schedule of second field professional includes tasks associated with locations E, A, G, H);
Here, Garber discloses a schedule for a first field professional and another schedule of a second field professional, including current and future tasks for each field professional. Garber further discloses the use of information outside of a time block (i.e. commuting before the appointment) for which the potential interruption prediction is computed. Thus, Examiner respectfully maintains that Garber renders the above-argued amended claim limitation obvious. As such, Examiner remain unpersuaded.
Second, Applicant argues that “…the current grounds for rejection are moot, particularly in view of the amended claim language. First, the Examiner has not established that the proposed combination discloses "wherein the computed likelihood of interruption of the medical professional is computed based at least in part on data associated with the other schedules…” as recited in amended claim 1. The Examiner concedes that Garber does not disclose such a feature and relies on Mashin-Chi instead. Applicant cannot agree. Specifically, Mashin-Chi is directed to determining a "incontinence data" for individuals… the "incontinence data" for individuals of Mashin-Chi does not disclose "one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, or an experience level of a local technologist operating the imaging modality," as recited in amended claim 1…” [Arguments, pages 15-17].
In response, Applicant’s arguments are considered but are not persuasive. With respect to the assertion that the "incontinence data" for individuals of Mashin-Chi does not disclose "one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, or an experience level of a local technologist operating the imaging modality,", Examiner respectfully disagrees and observes that the claims do not specify nor provide any detail with regard to what constitutes a patient’s characteristic. Thus, Examiner maintains that incontinence of a patient is a characteristic of a patient. As such, Examiner remain unpersuaded.
Third, Applicant argues that “…the current grounds for rejection are moot, particularly in view of the amended claim language. First, the Examiner has not established that the proposed combination discloses "computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed," as recited in amended claim 1…
In the rejection of dependent claim 6, the Examiner contents that Garber discloses such a feature. Applicant cannot agree. Specifically, Garber is directed to determining a "real-time" delay as set forth in operation 604 in FIG. 6 of Garber. Thus, Garber does not disclose "computing the potential interruption predictions…”
Further, Mashin-Chi does not appear to cure Garber of this failure.
Thus, the proposed combination does not disclose "computing the potential interruption predictions…”” [Arguments, pages 17-19].
In response, Applicant’s arguments are considered but are not persuasive. With respect to the assertion that because Garber is directed to determining a "real-time" delay… Garber does not disclose "computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed,", for the same reasons as stated in the above-argument. In particular, Examiner observes that Garber at ¶ 116 discloses a schedule for a first field professional and another schedule of a second field professional, including current and future tasks for each field professional. Garber further discloses the use of information outside of a time block (i.e. commuting before the appointment) for which the potential interruption prediction is computed. Thus, Examiner respectfully maintains that Garber renders the above-argued amended claim limitation obvious. As such, Examiner remain unpersuaded.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 3-5 and 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1 and 3-20 are directed to statutory categories, namely article of manufactures (claims 1, 3-17 and 18-19), and a process (claim 20).
Step 2A, Prong 1: Claims 1, 18 and 20 in part, recite the following abstract idea:
…perform managing a schedule of a medical professional, comprising: maintaining a schedule of tasks for the medical professional, the schedule including time intervals with planned tasks; maintaining or receiving other schedules of future tasks for others that interact with the medical professional; computing, by applying… to the other schedules, a likelihood of interruption of the medical professional as a function of time from the maintained or received other schedules of future tasks, wherein the computed likelihood of interruption of the medical professional is computed as a function of time based at least in part on data associated with the other schedules including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, or an experience level of a local technologist operating the imaging modality, wherein computing potential interruption predictions from the maintained or received other schedules includes computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed; and generating a representation of the schedule of the medical professional with an indication of the likelihood of interruption as a function of time in the schedule for display on… [Claim 1],
…perform an electronic calendar operation comprising: estimating, by applying… to other schedules, likelihoods of interruption of an owner of an electronic calendar for time blocks of a calendar based on historical information on interruptions of the owner of the electronic calendar, the electronic calendar including tasks assigned for the owner of the electric calendar to time intervals of the electronic calendar, wherein the estimated likelihoods of interruption of the owner of the electronic calendar is estimated based at least in part on data associated with the other schedules of future tasks including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality, wherein estimating the likelihoods of interruption includes computing the potential interruption predictions outside of a time block for which the potential interruption prediction is computed; and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on… [Claim 18],
An electronic calendar method comprising: maintaining an electronic calendar including assigning tasks for an owner of the electric calendar to time intervals of the electronic calendar; estimating, by applying… to other schedules, likelihoods of interruption of the owner of the electronic calendar for time blocks of the calendar based on schedules of future tasks for others that interact with the owner of the electronic calendar, wherein the estimated likelihoods of interruption of the owner of the electronic calendar is estimated based at least in part on data associated with the schedules of tasks for others including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, or an experience level of a local technologist operating the imaging modality, wherein estimating the likelihoods of interruption includes computing the potential interruption predictions outside of a time block for which the potential interruption prediction is computed; and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the computed likelihoods of interruption annotated to the corresponding time blocks [Claim 20].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. Specifically, displaying, to a user, a schedule and likelihoods of interruption for scheduled tasks is considered to describe steps for managing personal behavior as well as interactions between people and following rules and instructions. As such, claims 1, 18 and 20 recite concepts identified as abstract ideas.
Dependent claims 3-17 and 19 recite limitations relative to the independent claims, including, for example:
…wherein the likelihood of interruption as a function of time is computed over a course of a day in the schedule [Claim 3],
…wherein the method further includes: plotting the likelihood of interruption as a function of time as a continuous curve; and outputting the continuous curve on… [Claim 4],
…wherein the method further includes: plotting the likelihood of interruption as a function of time as a discrete time block representation for the time intervals of the schedule; outputting the discrete time block representation on… [Claim 5],
…wherein computing potential interruption predictions from the maintained or received other schedules includes: computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed [Claim 6].
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1, 18 and 20 only recite the following additional elements –
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 1],
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 18],
…a machine learning (ML) component…; …an electronic processing device… [Claim 20].
The dependent claims recite the following new additional elements –
… wherein the ML component comprises one of a time-dependent Markov model, a time-dependent finite element model, a time- dependent finite element modeling (FEM), long-short-term memories (LSTM), or a recurrent neural network (RNN) that is trained… (Claim 8),
The apparatus, machine learning component and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1, 18 and 20 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent claims 1, 18 and 20 only recite the following additional elements –
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 1],
A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to…; …a machine learning (ML) component…; …an electronic processing device… [Claim 18],
…a machine learning (ML) component…; …an electronic processing device… [Claim 20].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, and 3-5 and 7-20 are rejected under 35 U.S.C. 103 as being anticipated by Garber et al., U.S. Publication No. 2020/0210965 [hereinafter Garber] in view of Mashin-Chi et al., U.S. Publication No. 2020/0210965 [hereinafter Mashin-Chi].
Regarding Claim 1, Garber discloses …A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to perform managing a schedule of a medical professional, comprising: maintaining a schedule of tasks for the medical professional, the schedule including time intervals with planned tasks (Garber, ¶ 9, a method for scheduling tasks to field professionals is provided (discloses maintaining a schedule of tasks). The method includes: receiving a set of requests reflecting demand for on-site services, wherein the set of requests is associated with a number of task types; receiving availability data indicative of an availability of a plurality of field professionals to perform on-site services; receiving skills data indicative of capabilities of each of the plurality of field professionals with respect to the task types; obtaining at least one desired scheduling weight for the number of task types; generating a schedule for the plurality of field professionals based on the demand for on-site services, the availability data, and the skills data; and wherein generating the schedule for the plurality of field professionals comprises including a first task in the schedule when the first task conforms with the at least one desired scheduling weight and excluding a second task from the schedule when the second task does not conform with the at least one desired scheduling weight.), (Id., ¶ 269, At step 908, processing device 202 may receive information that may affect the likelihood of the assigned field professional to complete the customer's request of service in a single visit. The information may include real-time information about a condition of object associated with the scheduled service, or the current status of parts (e.g., tools) that the field professional has currently available in his/her inventory. For example, the field professional may be a nurse (discloses medical professional) scheduled to do a home visit to do dialysis and the information may include updates on a health condition of a patient. The information may be received in response to an enquiry triggered by processing device 202 or independently by the customer), (Id., ¶ 36, Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein), (Id., ¶ 45, FIG. 6B is a diagram of example planned and actual schedules of field professionals, consistent with the present disclosure);
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maintaining or receiving other schedules of future tasks for others that interact with the medical professional (Id., ¶ 314, as shown in FIG. 15A, there may be two field professionals, field professional 1510 and second field professional 1520. Both may be qualified to install modems, but only field professional 1510 is qualified to establish virtual private networks remotely. If a hospital emergency room's virtual private network fails, a high urgency notification may indicate a necessary location-agnostic task of repairing the network. Subsequently, as shown in FIG. 15B, the additional location-based task of a modem installation may be reassigned from field professional 1510 to second field professional 1520. Field professional 1510 may then be assigned the location-agnostic task of remotely repairing the emergency room's virtual private network. Alternatively, field professional 1510 may have past experience with the hospital's network or superior past rankings by customers. Any combination of these and other factors could be used to determine that field professional 1510 is more suitable to provide the location-agnostic task), (Id., ¶ 152, FIG. 6B is a diagram of example planned and actual schedules of field professionals (discloses maintain schedules of future tasks for other interacting professionals), consistent with the present disclosure. In FIG. 6B, the first field professional P1 corresponds to the first field professional in FIG. 3A, and each of locations A-D may have a specified or scheduled arrival time for P1. Each row of blocks represents a schedule associated with a field professional in FIG. 6B. White blocks represent the field professional's time durations when performing a task at a specified location or when the field professional is available. Dotted blocks represent the field professional's time durations when driving between the specified locations. A timeline is shown below the blocks, and dash lines are shown to indicate aligned time points of the schedules in the timeline), (Id., ¶ 166, as shown in FIG. 6B, processing device 202 may determine from real-time schedule information that the first field professional P1 will delay arriving at location A at scheduled time 10:36, and the second field professional P2 has finished a task at location E at 10:12, sooner than expected completion time at 10:39. Based on the traffic condition from location E to location A, weather conditions at locations E and A, or the type of the task to be performed at location A, processing device 202 may determine that the second field professional can arrive to location A at 10:36);
computing, by applying a machine learning (ML) component to the other schedules, a likelihood of interruption of the medical professional as a function of time from the maintained or received other schedules of future tasks, wherein the computed likelihood of interruption of the medical professional is computed … wherein computing potential interruption predictions from the maintained or received other schedules includes computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed (Id., ¶ 156, if processing device 202 receives the progress information from network interface 206 at step 602, processing device 202 may determine a delay associated with one or more tasks assigned to the first field professional. In some embodiments, processing device 202 may determine the delay from the progress information), (Id., ¶ 157, At step 606, processing device 202 determines a likelihood that the delay will interfere (disclose determining likelihood of interruption as a function of time) with the first field professional arriving at an identified location associated with an assigned task at a scheduled time. The scheduled time may be a time specified by processing device 202 for the first field professional to arrive at the identified location. For example, the identified location may be any of locations A-D in FIG. 3A. Though processing device 202 determines the existence of the delay at step 604, the delay may not necessarily cause an actual delay of the arrival time at the identified location. For example, the first field professional may find means (e.g., find a shorter route or increase moving speed) to make up for the delay), (Id., ¶ 165, Referring back to FIG. 6A, at step 608, processing device 202 determines from real-time schedule information associated with a second field professional whether the second field professional can arrive to the identified location associated with the task assigned to the first field professional at the scheduled time. The second field professional may be one of field professionals 110. For example, the second field professional may be the field professional assigned to tasks for providing technical service at locations E, F, G, H in FIGS. 3A-3B. In some embodiments, processing device 202 may determine whether the second field professional can arrive at the identified location at the schedule time based on any combination of the traffic conditions, weather conditions, or task performances associated with the second field professional), (Id., ¶ 166, For example, as shown in FIG. 6B, processing device 202 may determine from real-time schedule information that the first field professional P1 will delay arriving at location A at scheduled time 10:36, and the second field professional P2 has finished a task at location E at 10:12, sooner than expected completion time at 10:39. Based on the traffic condition from location E to location A, weather conditions at locations E and A, or the type of the task to be performed at location A, processing device 202 may determine that the second field professional can arrive to location A at 10:36), (Id., ¶ 262, processing device 202 may determine the likelihood using a machine learning mode (discloses computing likelihood of interruption by applying a machine learning component). For example, a neural network model (e.g., a deep learning model) may be created and set with initial parameters. Based on statistics of the field professional completing the type of the new service, locations, and characteristics of the technical service under which the field professional completed the same type of the new service, the neural network model may be trained, and the initial parameters may be updated. Using the trained neural network model, by inputting the characteristics of the same type of the technical service, the location of the technical service, and the name of the field professional, the trained neural network may output a likelihood that the field professional will complete the technical service in a single on-site visit at the location), (Id., ¶ 325, Further illustration of the steps of process 1600 may be understood with reference to the steps of process 1700 in FIG. 17A and FIG. 17B. Process 1700 begins by receiving a request to book a new appointment for a service at step 1702. After the request is received, a multi-route model is executed. For example, at step 1704, a predictive machine learning algorithm may be executed to determine a first booking response. The predictive machine learning algorithm may be implemented through any machine learning technique. In some embodiments, the first scheduling model may use previous proposed times to determine the first proposed time. For example, if previous cable installations occurred at 2:00 on Tuesdays and internet installations occurred at 1:00 on Wednesdays, the first scheduling model may use this information when determining a first booking response for a new request for cable installation. Thus, at step 1706, the predictive machine learning algorithm may determine a first booking response corresponding to the initial request for an appointment), (Id., ¶ 330, At step 1730 of FIG. 17B, the first and second booking responses are compared. In some situations, step 1730 may be No, as the first and second booking responses may provide the same result. The process may terminate at this point. However, if the first and second booking responses differ, multiple options may be available. For instance, process 1700 may proceed to initiate an action to improve the multi-route model at step 1732. For example, the system may provide the second booking response, which may include a second proposed time, to the predictive machine learning algorithm. In this way, the second booking response may be an additional training input to retrain the predictive machine learning algorithm, thereby updating the predictive machine learning algorithm with the second booking response. Furthermore, the predictive machine learning algorithm may be retrained even if the first and second responses are identical, so as to reinforce a correct result in the algorithm. Alternatively, a scheduled assignment of at least one field professional may be changed when the first booking response is different than the second booking response at step 1734. In this way, the first booking response may be retained, providing greater consistency to users such that the first booking response is retained despite initially being invalid. In some embodiments, both step 1732 and step 1734 may be performed, such that both the multi-route model is improved and a schedule assignment is changed), (Id., ¶ 116, FIGS. 3A and 3B depict two schematic maps illustrating the planned daily schedule of two field professionals and the updated daily schedule of the two field professionals. As shown in FIG. 3A, the first field professional was assigned to tasks for providing technical service at locations A, B, C, D; and the second field professional was assigned to tasks for providing technical service at locations E, F, G, H. The planned route of the first field professional is illustrated in a dashed line and the planned route of the second field professional is illustrated in a solid line. Assuming that the first field professional was scheduled to be at location “A” at 10:36 and at location “B” at 11:39; and the second field professional was scheduled to be at location “E” at 10:15 and at location “F” at 11:09. (discloses computing the interruption prediction with information from other schedules of future tasks) In the illustrated example, server 152 received at 9:15, from network interface 206, real-time information for the first and second field professionals. In one embodiment, the real-time information may include current location information derived at least partially from location circuits of field professionals' communication devices 180A. For example, the real-time information may indicate that first field professional is stuck on the road to location “A.” (discloses information outside of a time block (i.e. before the appointment) for which the potential interruption prediction is computed) In another embodiment, the real-time information may include task status updates transmitted from field professionals' communication devices 180A. For example, the real-time information may indicate that the second field professional had finished the assignment earlier than the estimated time for the completion of the task associated with location “E.” Based on the real-time progress information, and as shown in FIG. 3B, server 152 may reassign the first field professional to a task associated location “F,” and reassign the second field professional to a task associated location “A.” Thus, the updated schedule of first field professional includes tasks associated with locations F, B, C, and D and the updated schedule of second field professional includes tasks associated with locations E, A, G, H);
and generating a representation of the schedule of the medical professional with an indication of the likelihood of interruption as a function of time in the schedule for display on an electronic processing device (Id., ¶ 33, The system may provide alerts to draw the user's attention to discrepancies. Optionally, the alerts consist of color-coding of areas in the view (e.g., cells in a displayed table) according to the presence and severity of discrepancies. Optionally, the alerts may include presenting to the user a list of alerts, possibly ranked and color-coded by their severity. Optionally, the alerts may include messages transmitted to users defined as being in charge of reacting and/or resolving each type of alert. Messages may be transmitted by phone, cellular messaging, e-mail, fax, and instant messaging. In addition, the alerts may include of any combination of the above mechanisms, configurable according to the user's personal preferences, user type, alert type, and organizational procedures), (Id., ¶ 137, Presentation layer 502 may include software modules and processes (collectively, “components”) of application programs that use data stored in data layer 506 to perform actions. Example actions may include, without limitation, rendering information for display on a GUI presented to users through a display monitor. It should be appreciated that these actions may relate to any form of data manipulation or processing, and therefore are not limited to rendering data for display on a GUI. Indeed, many components of application programs manipulate data during “background” operations that are not noticeable to users of the computing system... For mobile access, the presentation layer may include applications for most popular mobile operating systems, such as iOS, Android, and Windows, and deployed from corresponding application stores), (Id., ¶ 161, processing device 202 may determine a likelihood that the delay will interfere with a future assigned task. The future assigned task may be a task scheduled later than the current task. For example, in FIG. 3A, if the first field professional is currently working at location A for a first task, a second task at location B is the future assigned task. In some embodiments, processing device 202 may determine a likelihood that the delay in the current task will interfere with the future assigned task using a delay-statistics model. In some embodiments, the delay-statistics model may collect and compile historical delay data and generate correlations between a time duration of the delay at the current task and a likelihood of such delay causing a time duration of a delay at the future task. For example, statistics may show that that a five-minute delay at 10:00 for a first task will cause an 80% likelihood that the first field professional will be 30 minutes late to a second task scheduled at 15:00 (discloses indication of likelihood of interruption as a function of time). The delay-statistics model may establish a corresponding relationship between the 5-minute delay and the 30-minute delay for the two tasks. When the first field professional actually delay for 5 minutes at the first task, by using the delay-statistics model, processing device 202 may determine that the likelihood is 80% for the first field professional to arrive 30 minutes late for the second task), (Id., ¶ 296, The initiated action may include issuance of a warning message. The warning may be displayed, for instance, as part of the service optimization suite architecture shown in FIG. 5. The warning may include data concerning how many requests were not scheduled with tasks expected to be completed within the period of time. Alternatively, the action may include automatically changing a scheduling constraint. For example, the method may result in a constraint on work hours to be changed automatically, such that field professionals work more hours each day in order to accommodate a surge in requests for services).
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose …based at least in part on data associated with the other schedules including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality.
However, Mashin-Chi discloses …based at least in part on data associated with the other schedules including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, or an experience level of a local technologist operating the imaging modality (Mashin-Chi, ¶ 13, The inventive methodology, as disclosed herein, helps to identify repetitive voiding profiles for individuals (observed in isolation or as part of a group of individuals e.g. in a care facility) typically by considering one or more n-hour periods of incontinence data collected for the individuals. Incontinence data may be obtained manually, e.g. by a carer manually checking and changing incontinence aids, weighing the soiled aids and noting the time and relevant details (e.g. void type) of each event/check, as well as fluid and food intakes and other factors that may influence the incontinence behaviour of an individual. Alternatively/additionally, incontinence data (discloses patient characteristics) may be obtained using sensors or other technology and may be supplemented (or supplied) by historical data for the subject including e.g. type of incontinence experienced, level of incontinence and the like. Ideally, each period of incontinence data utilised in the method corresponds to the same, or a similar, period of time in a n-hour block so that relevant events are monitored from which voiding patterns can be computed), (Id., ¶ 168, In a one-to-one relationship, a goal of optimisation procedure 2006 may be to optimize the value/s of a mathematical function by seeking the minimum number of toileting procedures necessary to achieve adequate toileting. In another embodiment, a goal of optimisation procedure 2006 may be to align the number of toileting procedures with a property in a “general information” input such as e.g. allowable number of toiletings according to care guidelines. In yet another embodiment a goal may be to optimize one or more of the other objectives given in Table 1. Inputs 1001 may be used directly or indirectly to influence optimization procedure 2006. For example, information pertaining to intakes sets may be used for deriving more detailed information for events sets when events and intakes are linked e.g. by causal relationship. Subject information such as physical characteristics, non-preferred toileting times, continence holding ability, medical conditions and the like may be considered too).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the patient characteristic and event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting.
The motivation for doing so would have been to improve an ability “to perform an optimisation procedure to recalculate time indicators of the existing schedule to improve optimisation of at least a subset of the received objectives. This enables the “goodness” of the toileting schedule to be improved” (Mashin-Chi, ¶ 16), wherein such improvements would benefit Graber’s system and method which “are directed to providing new and improved ways for scheduling tasks to field professionals that overcome problems and inefficiencies in existing systems” [Mashin-Chi, ¶ 16; Graber, ¶ 5].
Regarding Claim 3, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein the likelihood of interruption as a function of time is computed over a course of a day in the schedule (Garber, ¶ 30, Consistent with disclosed embodiments, systems, methods, and computer-readable media enable assigning tasks based on real-time conditions. For example, consistent with one aspect a disclosed system includes a network interface and a processor. The processor provides a field professional with information about a daily schedule (discloses calculation for daily schedule) of assigned tasks associated with a set of requests for on-site services. The processor also receives real-time information reflecting a likelihood the field professional will complete the daily schedule of assigned tasks. The processor may determine, from the real-time information, existence of an unplanned event likely to interfere with the field professional completing at least one task from the daily schedule. Thereafter, the processor also presents a plurality of optional tasks to the field professional based on the determination. Upon detecting a selection of one of the plurality of optional tasks, the processor assigns the field professional to the selected task and unassign the at least one task).
Regarding Claim 4, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose …wherein the method further includes: plotting the likelihood of interruption as a function of time as a continuous curve; and outputting the continuous curve on a display of the electronic processing device.
However, Mashin-Chi discloses …wherein the method further includes: plotting the likelihood of interruption as a function of time as a continuous curve; and outputting the continuous curve on a display of the electronic processing device (Mashin-Chi, ¶ 91, FIG. 9 represents a probability distribution of a “time type” input for estimating the time of an event activity which may be used to determine time indicator scheduling a procedure such as toileting a subject, calculated according to an embodiment of the invention. Where the value of the probability curve approaches 100%, the likelihood of an event occurring at that time is greater. An input of any type may also have a “certainty” value to indicate how confident the system is in the correctness of the input), (Id., ¶ 158, In a preferred embodiment, a toileting schedule calculated according to the present invention is adapted for display on a display device or printing for a file or bed record. Ideally, the displayed toileting schedule may be configured to show one or both of expected voiding event time indicators and toileting procedure time indicators. Control over the display may be achieved using e.g. a filter option provided via user interface 1400), (Id., Fig. 9, Figure depicts plotting a likelihood of interruption as a function of time).
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It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting for the same reasons as stated for claim 1.
Regarding Claim 5, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein the method further includes: plotting the likelihood of interruption as a function of time as a discrete time block representation for the time intervals of the schedule; outputting the discrete time block representation on a display of the electronic processing device (Garber, ¶ 117, Consistent with the present disclosure, server 152 may determine from real-time information a delay associated with one or more tasks assigned to a first field professional, and that there is a likelihood that the delay will interfere with the first field professional arriving at an identified location associated with an assigned task at a scheduled time. For example, at 9:15, server 152 may determine that the first field professional cannot make it to location “A” before 9:40. Therefore, server 152 may reassign the assigned task to a second field professional based on the real-time information and the determined likelihood that the delay will interfere with the first field professional arriving at location “A” at 10:36. Server 152 may provide the second field professional, using network interface 206, information reflecting the assignment of the task associated with location “A.” In one embodiment, the information reflecting the assignment of the first task includes directional instructions to location “A” (e.g., a location, an address, a driving route). In another embodiment, the information reflecting the assignment of the first task includes details about a customer associated location “A” (e.g., a name, a phone number). In another embodiment, the information reflecting the assignment of the first task includes a description of the first task (e.g., tools, spare parts, existing infrastructure)), (Id., ¶ 306, Further illustration of the steps of process 1300 may be understood by reference to FIG. 14. At the beginning of a day, one or more field professional may be provided a planned field professional schedule 1410. The schedule shown includes driving times between location-specific tasks 1 and 2, tasks 1 and 2, and a location agnostic task 1. As the day proceeds, the one or more field professional completes the scheduled activities of driving and location-specific task 1. However, as shown in the observed traffic 1420, shortly before 11:00, traffic on the route that the one or more field professional would travel to location-specific task 2 becomes unusually heavy, perhaps due to a car accident or roadway construction. If the one or more field professional continued on the previously-planned sequence of tasks, namely, starting to drive at approximately 11:10 and then completing location-specific task 2, the one or more field professional would not be able to start task 2 until 13:00 due to traffic, as shown in resulting field professional schedule 1430. As a result, the field professional's duty day may end before being able to accomplish location-agnostic task 1. If such a situation arose, process 1300 may be used to revise the schedule of the field professional. In this case, after having completed steps 1302, 1304, and 1306, at step 1308, real-time information may be received about the location of the field professional, as well as real-time information about traffic conditions. At step 1310, based on the real-time information of the traffic delay shown in the observed traffic 1420 of FIG. 14, it may be determined that location-agnostic task 1 should be completed in the time that the field professional would be stuck in traffic. The field professional's schedule may be revised, reassigning the field professional to complete location-agnostic task 1 before starting location-specific task 2, as shown in revised filed professional schedule 1440. In this example, the field professional is able to accomplish three tasks during a shift due to process 1300, whereas, without process 1300, the one or more field professional would have been able to complete only two tasks during the shift, with significant time spent being unproductive in traffic).
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Regarding Claim 7, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 6…
Garber further discloses …wherein computing the potential interruption predictions with information from the maintained or received other schedules outside of a time block for which the potential interruption prediction is computed includes: computing the potential interruption prediction for a first time block; computing the potential interruption prediction for a second time block adjacent the first time block (Id., ¶ 117, Consistent with the present disclosure, server 152 may determine from real-time information a delay associated with one or more tasks assigned to a first field professional, and that there is a likelihood that the delay will interfere with the first field professional arriving at an identified location associated with an assigned task at a scheduled time. For example, at 9:15, server 152 may determine that the first field professional cannot make it to location “A” before 9:40. (discloses computing interruption for a first time block) Therefore, server 152 may reassign the assigned task to a second field professional based on the real-time information and the determined likelihood that the delay will interfere with the first field professional arriving at location “A” at 10:36. (discloses computing interruption for a second adjacent time block)Server 152 may provide the second field professional, using network interface 206, information reflecting the assignment of the task associated with location “A.” In one embodiment, the information reflecting the assignment of the first task includes directional instructions to location “A” (e.g., a location, an address, a driving route). In another embodiment, the information reflecting the assignment of the first task includes details about a customer associated location “A” (e.g., a name, a phone number). In another embodiment, the information reflecting the assignment of the first task includes a description of the first task (e.g., tools, spare parts, existing infrastructure).
determining whether the computed potential interruption prediction for the first time block should be used for computing the potential interruption prediction for the second time block (Id., ¶ 132, the actions performed by the manager of service provider 160 while using one view automatically propagated by the software across other views, hierarchy levels, and planning periods. They may also be propagated across organization boundaries, as when a planning-decision in organization A to outsource work to organization B is conveyed to organization B and appears there as a change in forecast demand, requiring re-iteration of the planning process. According to some embodiments, when propagating these actions, the system automatically monitors for discrepancies. The discrepancies may include: (1) Discrepancies between a forecast demand and allocated resources. (2) Discrepancies between different sources of the same information (e.g., forward-looking simulation vs. extrapolation of data using statistical trends analysis). (3) Discrepancies between different propagation directions, as when the planned resources are both dictated by higher management, propagating downwards, and also reported by regional management, propagating upwards. (4) Discrepancies between commitments made to customers and actual ability to deliver: For example, a customer may call with a problem and be told “someone will be with you tomorrow between 13:00 and 17:00,” because there appeared to be enough free resources during that time window, and without committing specific resources. Later there will be more calls are received and the software determines that there will be difficulty meeting this commitment, alerting the manager early enough to act, e.g., by diverting resources from another region. Another example for an even shorter planning-period: identifying the situation in which the service engineer is delayed in traffic or in an earlier task and will probably fail to arrive on time to the next task. (discloses using traffic interruption to predict delay in next adjacent task)).
Regarding Claim 8, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein the ML component comprises one of a time-dependent Markov model, a time-dependent finite element model, a time- dependent finite element modeling (FEM), long-short-term memories (LSTM), or a recurrent neural network (RNN) that is trained based on… (Garber, ¶ 262, In another embodiment, processing device 202 may determine the likelihood using a machine learning mode. For example, a neural network model (e.g., a deep learning model) may be created and set with initial parameters. Based on statistics of the field professional completing the type of the new service, locations, and characteristics of the technical service under which the field professional completed the same type of the new service, the neural network model may be trained, and the initial parameters may be updated. Using the trained neural network model, by inputting the characteristics of the same type of the technical service, the location of the technical service, and the name of the field professional, the trained neural network may output a likelihood that the field professional will complete the technical service in a single on-site visit at the location).
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose …historical information on interruptions to an owner of an electronic calendar regarding each combination of the type of procedure and protocol scheduled at the imaging modality, the patient's characteristic, or the experience level of a local technologist operating the imaging modality
However, Mashin-Chi discloses …historical information on interruptions to an owner of an electronic calendar regarding each combination of the type of procedure and protocol scheduled at the imaging modality, the patient's characteristic, or the experience level of a local technologist operating the imaging modality (Mashin-Chi, ¶ 13, The inventive methodology, as disclosed herein, helps to identify repetitive voiding profiles for individuals (observed in isolation or as part of a group of individuals e.g. in a care facility) typically by considering one or more n-hour periods of incontinence data collected for the individuals. Incontinence data may be obtained manually, e.g. by a carer manually checking and changing incontinence aids, weighing the soiled aids and noting the time and relevant details (e.g. void type) of each event/check, as well as fluid and food intakes and other factors that may influence the incontinence behaviour of an individual. Alternatively/additionally, incontinence data (discloses patient characteristics) may be obtained using sensors or other technology and may be supplemented (or supplied) by historical data for the subject including e.g. type of incontinence experienced, level of incontinence and the like. Ideally, each period of incontinence data utilised in the method corresponds to the same, or a similar, period of time in a n-hour block so that relevant events are monitored from which voiding patterns can be computed), (Id., ¶ 168, In a one-to-one relationship, a goal of optimisation procedure 2006 may be to optimize the value/s of a mathematical function by seeking the minimum number of toileting procedures necessary to achieve adequate toileting. In another embodiment, a goal of optimisation procedure 2006 may be to align the number of toileting procedures with a property in a “general information” input such as e.g. allowable number of toiletings according to care guidelines. In yet another embodiment a goal may be to optimize one or more of the other objectives given in Table 1. Inputs 1001 may be used directly or indirectly to influence optimization procedure 2006. For example, information pertaining to intakes sets may be used for deriving more detailed information for events sets when events and intakes are linked e.g. by causal relationship. Subject information such as physical characteristics, non-preferred toileting times, continence holding ability, medical conditions and the like may be considered too).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the patient characteristic and event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting for the same reasons as stated for claim 1.
Regarding Claim 9, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein the method further includes: estimating an availability score of a medical professional based on the schedule and the computed likelihood of interruption for each time block (Garber, ¶ 171, In some embodiments, to reassign the task assigned to the first field professional to the second field professional, processing device 202 may identify a number of second field professionals who can complete the task assigned to the first field professional, and select the second field professional based on current location information of the number of second field professionals and traffic conditions. In some embodiments, at step 608, processing device 202 may determine that there are multiple second field professionals who can arrive to the identified location associated with the task assigned to the first field professional at the scheduled time. Further, processing device 202 may select the second field professional based on the current location information of the second field professionals and the traffic conditions. For example, processing device 202 may determine the second field professional as the field professional having the shortest arrival time from the multiple second field professionals. In some embodiments, processing device 202 may determine the second field professional based on other criteria, such as skill levels, workload of the day, completed work of the day, future work scheduled, and so forth. For example, processing device 202 may assigned a weight and a score to each of the criteria (discloses availability score based on schedule and interruption). The weight represents the level of importance of that criterion in the determination of the second field professional. The score represents the degree, extent, or scale of the criterion. Processing device 202 may determine a weighted score for each of the multiple second field professionals and select one with its weighted score in a predetermined range).
Regarding Claim 10, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein: the schedule comprises a calendar comprising time blocks (Garber, ¶ 315, one or more field professional may be instructed to initiate a location-agnostic task before driving to a location associated with a second location-based task. The instruction may be provided by a variety of means, including automatically to the field professional communication device 180A, or posted to a publicly viewable calendar. Alternatively, the instruction may cause a dispatch person to personally notify a field professional via a phone call, radio call, email, or text message. Notifying the field professional before driving to the location associated with the second location-based task reduces lost time and maximizes field professional utilization. Alternatively, the instruction may be provided to one or more field professional after driving at least part of the way to a location associated with the second location-based task. For instance, if an accident causes traffic to back up after a field professional departs for the second location-based task, the system may notify the field professional to stop driving and complete a location-agnostic task while waiting for traffic to clear. After completing the location-agnostic task, the field professional may be instructed to perform more location-agnostic tasks if the traffic still caused delays. Alternatively, the field professional may resume driving to the second location-based task upon completion of the location-agnostic task with or without further notification).
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose … the likelihood of interruption as a function of time is computed as likelihood of interruption values for the respective time blocks of the calendar; and the representation of the schedule of the medical professional comprises a representation of the calendar with the time intervals with planned tasks annotated and the time blocks annotated with their respective likelihood of interruption values.
However, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses … the likelihood of interruption as a function of time is computed as likelihood of interruption values for the respective time blocks of the calendar; and the representation of the schedule of the medical professional comprises a representation of the calendar with the time intervals with planned tasks annotated and the time blocks annotated with their respective likelihood of interruption values.
First, Garber discloses calendar scheduling and calculating a likelihood of interruption for scheduled tasks (Garber, ¶ 141, Disclosed and claimed is a system that receives real-time reports (e.g., traffic updates, weather conditions), predicts that a field professional will not be able reach the customer at the scheduled time, and reassigns the customer to a different field professional. In one example, the system predicts that the field professional will miss a future task in his daily schedule. In another example, the system predicts that a delay would cause one or more tasks to be completed after a shift of a field professional is about to end), (Id., ¶ 447, the processing device may transmit information associated with the updated prediction model to the remote server for enabling improvement of the native scheduling engine. Improving the native scheduling engine may include updating the native scheduling engine based on the identified at least one factor. Consistent with the present disclosure, the information transmitted to the remote server may be associated with a plurality of scheduling parameters. For example, the information transmitted to the remote server may include indications of inaccurate estimations driving durations native scheduling engine, indications of inaccurate estimations of task durations, and indications of inaccurate skill requirements per task. The native scheduling engine may assign different task durations for a plurality of task types, different task durations for a plurality of field professionals, different task durations for a plurality of customer types, different task durations for time of day, different task durations for a plurality of areas of task locations, different task durations for a plurality of skills of the field professional, and more. In one embodiment, the local server may update the native scheduling engine by changing at least one value of the plurality of scheduling parameters. For example, originally the native scheduling engine of local server 3330B assumed it will take Bob 68 minutes to complete a certain task type. But using the updated native scheduling engine the local server may determines that there is 80% likelihood that it will take Bob 50 minutes to complete the task and 20% likelihood that it will take Bob 60 minutes to complete the task).
Further, Mashin-Chi discloses annotating a schedule display with a likelihood of an event (Mashin-Chi, ¶ 91, FIG. 9 represents a probability distribution of a “time type” input for estimating the time of an event activity which may be used to determine time indicator scheduling a procedure such as toileting a subject, calculated according to an embodiment of the invention. Where the value of the probability curve approaches 100%, the likelihood of an event occurring at that time is greater. An input of any type may also have a “certainty” value to indicate how confident the system is in the correctness of the input), (Id., ¶ 158, In a preferred embodiment, a toileting schedule calculated according to the present invention is adapted for display on a display device or printing for a file or bed record. Ideally, the displayed toileting schedule may be configured to show one or both of expected voiding event time indicators and toileting procedure time indicators. Control over the display may be achieved using e.g. a filter option provided via user interface 1400), (Id., Fig. 9, Figure depicts plotting a likelihood of interruption as a function of time).
One of ordinary skill in the art would have recognized that applying the known schedule annotation technique of Mashin-Chi would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of annotating a schedule with a likelihood of an event occurring, as in Mashin-Chi, to the teachings of Graber would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such medical professional scheduling features into similar scheduling systems. Further, annotating time block on a calendar with their respective likelihood of interruption values with based on calculated likelihood values, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more optimal scheduling of medical professional resources.
Thus, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses … the likelihood of interruption as a function of time is computed as likelihood of interruption values for the respective time blocks of the calendar; and the representation of the schedule of the medical professional comprises a representation of the calendar with the time intervals with planned tasks annotated and the time blocks annotated with their respective likelihood of interruption values.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting for the same reasons as stated for claim 1.
Regarding Claim 11, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 10…
Garber further discloses… wherein the time blocks are annotated with their respective likelihood of interruption values by color-coding the time blocks in accordance with their respective likelihood of interruption values (Garber, ¶ 132, According to some embodiments, when propagating these actions, the system automatically monitors for discrepancies. The discrepancies may include: (1) Discrepancies between a forecast demand and allocated resources. (2) Discrepancies between different sources of the same information (e.g., forward-looking simulation vs. extrapolation of data using statistical trends analysis). (3) Discrepancies between different propagation directions, as when the planned resources are both dictated by higher management, propagating downwards, and also reported by regional management, propagating upwards. (4) Discrepancies between commitments made to customers and actual ability to deliver: For example, a customer may call with a problem and be told “someone will be with you tomorrow between 13:00 and 17:00,” because there appeared to be enough free resources during that time window, and without committing specific resources. Later there will be more calls are received and the software determines that there will be difficulty meeting this commitment, alerting the manager early enough to act, e.g., by diverting resources from another region. Another example for an even shorter planning-period: identifying the situation in which the service engineer is delayed in traffic or in an earlier task and will probably fail to arrive on time to the next task), (Id., ¶ 133, The system may provide alerts to draw the user's attention to discrepancies. Optionally, the alerts consist of color-coding of areas in the view (e.g., cells in a displayed table) according to the presence and severity of discrepancies. Optionally, the alerts may include presenting to the user a list of alerts, possibly ranked and color-coded by their severity. Optionally, the alerts may include messages transmitted to users defined as being in charge of reacting and/or resolving each type of alert. Messages may be transmitted by phone, cellular messaging, e-mail, fax, and instant messaging. In addition, the alerts may include of any combination of the above mechanisms, configurable according to the user's personal preferences, user type, alert type, and organizational procedures).
Regarding Claim 12, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 9…
Garber further discloses … wherein outputting, on a display device of an electronic processing device operable by a medical professional, a representation of the schedule of the medical professionals with an indication of a likelihood of interruption for the time blocks in the schedule includes one of outputting the representation as a time curve over the day of the schedule; or outputting the representation as the availability score plotted as a function of time for the day of the schedule (Garber, ¶ 33, The system may provide alerts to draw the user's attention to discrepancies. Optionally, the alerts consist of color-coding of areas in the view (e.g., cells in a displayed table) according to the presence and severity of discrepancies. Optionally, the alerts may include presenting to the user a list of alerts, possibly ranked and color-coded by their severity. Optionally, the alerts may include messages transmitted to users defined as being in charge of reacting and/or resolving each type of alert. Messages may be transmitted by phone, cellular messaging, e-mail, fax, and instant messaging. In addition, the alerts may include of any combination of the above mechanisms, configurable according to the user's personal preferences, user type, alert type, and organizational procedures), (Id., ¶ 137, Presentation layer 502 may include software modules and processes (collectively, “components”) of application programs that use data stored in data layer 506 to perform actions. Example actions may include, without limitation, rendering information for display on a GUI presented to users through a display monitor. It should be appreciated that these actions may relate to any form of data manipulation or processing, and therefore are not limited to rendering data for display on a GUI. Indeed, many components of application programs manipulate data during “background” operations that are not noticeable to users of the computing system... For mobile access, the presentation layer may include applications for most popular mobile operating systems, such as iOS, Android, and Windows, and deployed from corresponding application stores), (Id., ¶ 171, In some embodiments, to reassign the task assigned to the first field professional to the second field professional, processing device 202 may identify a number of second field professionals who can complete the task assigned to the first field professional, and select the second field professional based on current location information of the number of second field professionals and traffic conditions. In some embodiments, at step 608, processing device 202 may determine that there are multiple second field professionals who can arrive to the identified location associated with the task assigned to the first field professional at the scheduled time. Further, processing device 202 may select the second field professional based on the current location information of the second field professionals and the traffic conditions. For example, processing device 202 may determine the second field professional as the field professional having the shortest arrival time from the multiple second field professionals. In some embodiments, processing device 202 may determine the second field professional based on other criteria, such as skill levels, workload of the day, completed work of the day, future work scheduled, and so forth. For example, processing device 202 may assigned a weight and a score to each of the criteria (discloses availability score based on schedule and interruption). The weight represents the level of importance of that criterion in the determination of the second field professional. The score represents the degree, extent, or scale of the criterion. Processing device 202 may determine a weighted score for each of the multiple second field professionals and select one with its weighted score in a predetermined range).
Regarding Claim 13, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …further storing instructions executable by at least one electronic processor to perform a remote assistance method including: providing two-way communication between a remote medical professional workstation and an electronic device disposed with a medical device during a planned task in the schedule (Garber, ¶ 314, as shown in FIG. 15A, there may be two field professionals, field professional 1510 and second field professional 1520. Both may be qualified to install modems, but only field professional 1510 is qualified to establish virtual private networks remotely. If a hospital emergency room's virtual private network fails, a high urgency notification may indicate a necessary location-agnostic task of repairing the network. Subsequently, as shown in FIG. 15B, the additional location-based task of a modem installation may be reassigned from field professional 1510 to second field professional 1520. Field professional 1510 may then be assigned the location-agnostic task of remotely repairing the emergency room's virtual private network. Alternatively, field professional 1510 may have past experience with the hospital's network or superior past rankings by customers. Any combination of these and other factors could be used to determine that field professional 1510 is more suitable to provide the location-agnostic task), (Id., ¶ 318, Assigning a location-agnostic task to one or more field professional may include sending a link to a remote assistance session to a mobile device associated with the field professional. The mobile device may be the field professional communication device 180A, or may be another device. The remote assistance session may be, for example, a remote desktop access tool, a virtual private network, or access to an administrator website. The link may enable the field professional to complete the location agnostic task. Alternatively, assigning a location-agnostic task to one or more field professional may include transferring a call to a mobile device associated with the one or more field professional. The video call may connect a field professional with a user, with another field professional, or with any other employee. The mobile device may be the field professional communication device 180A, or may be another device. The video call may enable a field professional to provide remote assistance or obtain further information and guidance related to the location-agnostic task), (Id., ¶ 488, A connected device may have a passive communication interface, such as a quick response (QR) code, a radio-frequency identification (RFID) tag, an NFC tag, or the like, or an active communication interface, such as a modem, a transceiver, a transmitter-receiver, or the like. The connected device may have a particular set of attributes (e.g., a device state or status, such as whether the connected device is on or off, open or closed, idle or active, available for task execution or busy, a cooling or heating function, an environmental monitoring or recording function, a light-emitting function, a sound-emitting function, etc.) that can be embedded in and/or controlled/monitored by a central processing unit (CPU), microprocessor, or the like. Consistent with the present disclosure, a connected device can encompass the range from the simplest IoT devices to the most robust legacy Internet accessible devices. For example, connected devices may include, but are not limited to, refrigerators, toasters, ovens, microwaves, freezers, dishwashers, dishes, hand tools, clothes washers, clothes dryers, furnaces, air conditioners, thermostats, televisions, light fixtures, vacuum cleaners, sprinklers, electricity meters, gas meters, thermometers, humidity sensors, soil sensors, security cameras, motion detection lights, traffic sensors, wearable devices, fitness bracelets, continuous glucose monitor devices, connected inhalers, an ingestible sensors, coagulation testing devices, asthma monitor devices, cell phones, desktop computers, laptop computers, tablet computers, personal digital assistants (PDAs), etc.).
Regarding Claim 14, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …wherein the likelihood of interruption comprises a likelihood of an unplanned task that is not on the schedule of tasks (Garber, ¶ 312, To aid in the determination, there may be an indication of an urgency level of the location-agnostic task associated with the second request. Based on the urgency level of the location-agnostic task, the additional location-based task may be reassigned to one or more second field professional and the location-agnostic task may be assigned to the one or more field professional. The urgency notifications may have multiple levels, such as low, medium and high, and may be based on a variety of factors, including user importance, paying higher fees for higher priority service, and the like. The threshold at which the system reassigns tasks may be independent of other considerations, such that, for example, high urgency tasks always are reassigned, or may take into account other considerations, such as other delays that may be introduced by altering the planned schedule), (Id., ¶ 132, the actions performed by the manager of service provider 160 while using one view automatically propagated by the software across other views, hierarchy levels, and planning periods. They may also be propagated across organization boundaries, as when a planning-decision in organization A to outsource work to organization B is conveyed to organization B and appears there as a change in forecast demand, requiring re-iteration of the planning process. According to some embodiments, when propagating these actions, the system automatically monitors for discrepancies. The discrepancies may include: (1) Discrepancies between a forecast demand and allocated resources. (2) Discrepancies between different sources of the same information (e.g., forward-looking simulation vs. extrapolation of data using statistical trends analysis). (3) Discrepancies between different propagation directions, as when the planned resources are both dictated by higher management, propagating downwards, and also reported by regional management, propagating upwards. (4) Discrepancies between commitments made to customers and actual ability to deliver: For example, a customer may call with a problem and be told “someone will be with you tomorrow between 13:00 and 17:00,” because there appeared to be enough free resources during that time window, and without committing specific resources. Later there will be more calls are received and the software determines that there will be difficulty meeting this commitment, alerting the manager early enough to act, e.g., by diverting resources from another region. Another example for an even shorter planning-period: identifying the situation in which the service engineer is delayed in traffic or in an earlier task and will probably fail to arrive on time to the next task).
Regarding Claim 15, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …further storing instructions to: determine an amount of likely interruptions (Garber, ¶ 141, Disclosed and claimed is a system that receives real-time reports (e.g., traffic updates, weather conditions), predicts that a field professional will not be able reach the customer at the scheduled time, and reassigns the customer to a different field professional. In one example, the system predicts that the field professional will miss a future task in his daily schedule. In another example, the system predicts that a delay would cause one or more tasks to be completed after a shift of a field professional is about to end), (Id., ¶ 426, The present disclosure discloses a system (e.g., system 100) with a optimization engine that takes into consideration different factors for predicting the task duration and other scheduling parameters for a specific task. Consistent with the present disclosure, the system may constantly or periodically receive information for calculating the scheduling parameters. In one example, the received information may be associated with past task durations (e.g., last month when Bob was assigned to this type of task it took him 70 minutes on average, but this month his average is about 57 minutes). In another example, the information may include traffic data associated with travel duration predictions (e.g., last week it was estimated to take 20 minutes on average to travel from point A to point B between 10:00 and 15:00; this week it is estimated to take 15 minutes to travel from point A to point B between 10:00 and 15:00) (discloses amount of likely interruptions));
and determine a schedule of one or more remote experts (RE) based on the determined amount of likely interruptions (Garber, ¶ 432, scheduling data 3311 may include information associated with previously completed tasks scheduled by at least one scheduling engine other than native scheduling engine 3341A. For example, scheduling data 3311 may include information associated with tasks scheduled by native scheduling engine 3341B. The information may include specific details about the scheduled tasks and/or statistics about the scheduled tasks. In another embodiment, scheduling data 3311 may be associated with traffic predictions. For example, scheduling data 3311 may include estimated travel durations, weather forecasts, details regarding public events that may cause atypical traffic conditions (e.g., parades, marathons, and demonstrations), and more. In another embodiment, scheduling data 3311 may be associated with task duration predictions. In this embodiment, scheduling data 3311 may be associated with a task type, a time of day, a customer type, a task location, skills of field professionals, and more. Scheduling data 3311 may include that collection of data be used for determining a prediction model 3312), (Id., ¶ 452, At step 3802, a processing device (e.g., processor 3340A) may receive a plurality of requests for on-site service from a plurality of users (e.g., users 130), wherein each request is associated with a different location. At step 3804, the processing device may schedule a set of tasks from the plurality of requests using a native scheduling engine (e.g., native scheduling engine 3341A)). In one embodiment, the processing device may estimate travel durations between locations associated with the set of tasks. The travel durations of the field professionals may depend on accurate estimates of prevailing and emerging traffic conditions. As such, the processing device may utilize advanced traffic models to analyze traffic data, including real-time traffic data and historical traffic data to estimate the travel durations. Consistent with the present disclosure, different versions of native scheduling engine may use different scheduling data. When different versions of native scheduling engine are being used different estimations of travel durations may be determined. For example, when using first scheduling data, native scheduling engine 3341A may estimate that a specific ride will take 37 minutes, but when using second scheduling data, native scheduling engine 3341A may estimate that a specific ride will take 52 minutes).
Regarding Claim 16, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …further storing instructions to: generate the representation of the schedule of the medical professional based on availabilities and an expertise level for each remote expert (RE) (Garber, ¶ 453, the processing device may estimate task durations associated with the set of tasks. As mentioned above, the task durations may be determined based on at least one of: the task type, the field professional, the customer type, the time of day, and the task area. Consistent with the present disclosure, when the native scheduling engine uses different scheduling data, different estimations of task durations may be determined. For example, a first version of native scheduling engine 3341A may estimate that a specific task will be completed in 25 minutes, but an updated second version of native scheduling engine 3341A may estimate that the specific task will be completed in 38 minutes. In other embodiments, the processing device may determine the requirements to complete each of the set of tasks and assign field professionals accordingly. The requirements may include the skills required to complete a task, the list of tools required to complete, and/or a list of parts required to complete. The requirements may also change when the native scheduling engine uses different scheduling data), (Id., ¶ 98, The term “scheduling tasks” is used herein to refer, for example, to a process for determining an order (e.g., chronological order) for a set of tasks a field professional performs. The tasks may be associated with requested services and require a field professional to travel to different locations. There are different types of scheduled tasks, for example, installing, replacing, or repairing objects, and each task type may require a different skill set. In addition, some scheduled tasks may be location-based tasks that require the field professional to visit a customer's location, for example, business or residence, and some tasks may be location-agnostic tasks that do not require the field professional to visit a customer's location. Location-agnostic tasks may be viewed as support sessions that a technician can perform remote from the customer place), (Id., ¶ 119, Consistent with the present disclosure, server 152 may extract information, and convey management decisions, to other units, including: human resources 431 for interacting with information about available staff, their calendars (i.e., vacation, training, overtime, etc.) and their mix of skills (which may be affected by changes in training plans); finance 432 for examining, and reporting, the implications of decisions such as authorizing overtime or subcontracting some work; and customer relationship management 433 for interacting with past and current data of detailed and aggregated customer demands. FIG. 4 also shows how two or more organizations using the same system may make their operations and cooperation much more effective by automatically transmitting relevant information between their servers. One example is outsourcing, in which a planning decision in organization A (marked as 410) to outsource work to organization B (marked as 420) may be conveyed to organization B, and appears there as a change in forecast demand, requiring re-iteration of the planning process. Such an arrangement may enable a large service organization to form customer-facing portals and subcontractor-facing portals to streamline and optimize its operations. This subcontractor-facing portal is also known as B2B (Business-to-Business) application, as well as “private marketplace” or “public marketplace” depending on their openness).
Regarding Claim 17, the combination of Graber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 1…
Garber further discloses …further storing instructions to: collect data related to one or more of an issue with the medical device, a state of the medical device, or an imaging protocol; and transmit the collected data to a selected remote expert (RE) (Id., ¶ 488, A connected device may have a passive communication interface, such as a quick response (QR) code, a radio-frequency identification (RFID) tag, an NFC tag, or the like, or an active communication interface, such as a modem, a transceiver, a transmitter-receiver, or the like. The connected device may have a particular set of attributes (e.g., a device state or status, such as whether the connected device is on or off, open or closed, idle or active, available for task execution or busy, a cooling or heating function, an environmental monitoring or recording function, a light-emitting function, a sound-emitting function, etc.) that can be embedded in and/or controlled/monitored by a central processing unit (CPU), microprocessor, or the like. Consistent with the present disclosure, a connected device can encompass the range from the simplest IoT devices to the most robust legacy Internet accessible devices. For example, connected devices may include, but are not limited to, refrigerators, toasters, ovens, microwaves, freezers, dishwashers, dishes, hand tools, clothes washers, clothes dryers, furnaces, air conditioners, thermostats, televisions, light fixtures, vacuum cleaners, sprinklers, electricity meters, gas meters, thermometers, humidity sensors, soil sensors, security cameras, motion detection lights, traffic sensors, wearable devices, fitness bracelets, continuous glucose monitor devices, connected inhalers, an ingestible sensors, coagulation testing devices, asthma monitor devices, cell phones, desktop computers, laptop computers, tablet computers, personal digital assistants (PDAs), etc.), (Id., ¶ 489, FIG. 44 is a diagram showing a timeline 4400 illustrating the chronological events that happen when a processing device (e.g., processing device 202) schedules a first request from a human customer and a second request from a connected device to at least one field professional, according to disclosed embodiments. As illustrated, timeline 4400 includes a splitting point 4414 to two optional timelines (First Time Period and Second Time Period) that correspond with the urgency level of the second request of the connected device. The manner and order in which events are shown in timeline 4400 are chosen for convenience and clarity of presentation and is not intended to limit the disclosed embodiments).
Regarding Claim 18, Garber discloses …A non-transitory computer readable medium storing instructions readable and executable by at least one electronic processor to perform an electronic calendar operation comprising: estimating, by applying a machine learning (ML) component to other schedules, likelihoods of interruption of an owner of an electronic calendar for time blocks of a calendar based on historical information on interruptions of the owner of the electronic calendar, the electronic calendar including tasks assigned for the owner of the electric calendar to time intervals of the electronic calendar, wherein the estimated likelihoods of interruption of the owner of the electronic calendar is estimated based at least in part on data associated with the other schedules of future tasks… wherein estimating the likelihoods of interruption includes computing the potential interruption predictions outside of a time block for which the potential interruption prediction is computed (Garber, ¶ 315, one or more field professional may be instructed to initiate a location-agnostic task before driving to a location associated with a second location-based task. The instruction may be provided by a variety of means, including automatically to the field professional communication device 180A, or posted to a publicly viewable calendar. Alternatively, the instruction may cause a dispatch person to personally notify a field professional via a phone call, radio call, email, or text message. Notifying the field professional before driving to the location associated with the second location-based task reduces lost time and maximizes field professional utilization. Alternatively, the instruction may be provided to one or more field professional after driving at least part of the way to a location associated with the second location-based task. For instance, if an accident causes traffic to back up after a field professional departs for the second location-based task, the system may notify the field professional to stop driving and complete a location-agnostic task while waiting for traffic to clear. After completing the location-agnostic task, the field professional may be instructed to perform more location-agnostic tasks if the traffic still caused delays. Alternatively, the field professional may resume driving to the second location-based task upon completion of the location-agnostic task with or without further notification), (Id., ¶ 9, a method for scheduling tasks to field professionals is provided (discloses maintaining a schedule of tasks). The method includes: receiving a set of requests reflecting demand for on-site services, wherein the set of requests is associated with a number of task types; receiving availability data indicative of an availability of a plurality of field professionals to perform on-site services; receiving skills data indicative of capabilities of each of the plurality of field professionals with respect to the task types; obtaining at least one desired scheduling weight for the number of task types; generating a schedule for the plurality of field professionals based on the demand for on-site services, the availability data, and the skills data; and wherein generating the schedule for the plurality of field professionals comprises including a first task in the schedule when the first task conforms with the at least one desired scheduling weight and excluding a second task from the schedule when the second task does not conform with the at least one desired scheduling weight.), (Id., ¶ 269, At step 908, processing device 202 may receive information that may affect the likelihood of the assigned field professional to complete the customer's request of service in a single visit. The information may include real-time information about a condition of object associated with the scheduled service, or the current status of parts (e.g., tools) that the field professional has currently available in his/her inventory. For example, the field professional may be a nurse (discloses medical professional) scheduled to do a home visit to do dialysis and the information may include updates on a health condition of a patient. The information may be received in response to an enquiry triggered by processing device 202 or independently by the customer), (Id., ¶ 36, Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein), (Id., ¶ 45, FIG. 6B is a diagram of example planned and actual schedules of field professionals, consistent with the present disclosure), (Id., ¶ 262, processing device 202 may determine the likelihood using a machine learning mode (discloses computing likelihood of interruption by applying a machine learning component). For example, a neural network model (e.g., a deep learning model) may be created and set with initial parameters. Based on statistics of the field professional completing the type of the new service, locations, and characteristics of the technical service under which the field professional completed the same type of the new service, the neural network model may be trained, and the initial parameters may be updated. Using the trained neural network model, by inputting the characteristics of the same type of the technical service, the location of the technical service, and the name of the field professional, the trained neural network may output a likelihood that the field professional will complete the technical service in a single on-site visit at the location), (Id., ¶ 325, Further illustration of the steps of process 1600 may be understood with reference to the steps of process 1700 in FIG. 17A and FIG. 17B. Process 1700 begins by receiving a request to book a new appointment for a service at step 1702. After the request is received, a multi-route model is executed. For example, at step 1704, a predictive machine learning algorithm may be executed to determine a first booking response. The predictive machine learning algorithm may be implemented through any machine learning technique. In some embodiments, the first scheduling model may use previous proposed times to determine the first proposed time. For example, if previous cable installations occurred at 2:00 on Tuesdays and internet installations occurred at 1:00 on Wednesdays, the first scheduling model may use this information when determining a first booking response for a new request for cable installation. Thus, at step 1706, the predictive machine learning algorithm may determine a first booking response corresponding to the initial request for an appointment), (Id., ¶ 330, At step 1730 of FIG. 17B, the first and second booking responses are compared. In some situations, step 1730 may be No, as the first and second booking responses may provide the same result. The process may terminate at this point. However, if the first and second booking responses differ, multiple options may be available. For instance, process 1700 may proceed to initiate an action to improve the multi-route model at step 1732. For example, the system may provide the second booking response, which may include a second proposed time, to the predictive machine learning algorithm. In this way, the second booking response may be an additional training input to retrain the predictive machine learning algorithm, thereby updating the predictive machine learning algorithm with the second booking response. Furthermore, the predictive machine learning algorithm may be retrained even if the first and second responses are identical, so as to reinforce a correct result in the algorithm. Alternatively, a scheduled assignment of at least one field professional may be changed when the first booking response is different than the second booking response at step 1734. In this way, the first booking response may be retained, providing greater consistency to users such that the first booking response is retained despite initially being invalid. In some embodiments, both step 1732 and step 1734 may be performed, such that both the multi-route model is improved and a schedule assignment is changed), (Id., ¶ 116, FIGS. 3A and 3B depict two schematic maps illustrating the planned daily schedule of two field professionals and the updated daily schedule of the two field professionals. As shown in FIG. 3A, the first field professional was assigned to tasks for providing technical service at locations A, B, C, D; and the second field professional was assigned to tasks for providing technical service at locations E, F, G, H. The planned route of the first field professional is illustrated in a dashed line and the planned route of the second field professional is illustrated in a solid line. Assuming that the first field professional was scheduled to be at location “A” at 10:36 and at location “B” at 11:39; and the second field professional was scheduled to be at location “E” at 10:15 and at location “F” at 11:09. (discloses computing the interruption prediction with information from other schedules of future tasks) In the illustrated example, server 152 received at 9:15, from network interface 206, real-time information for the first and second field professionals. In one embodiment, the real-time information may include current location information derived at least partially from location circuits of field professionals' communication devices 180A. For example, the real-time information may indicate that first field professional is stuck on the road to location “A.” (discloses information outside of a time block (i.e. before the appointment) for which the potential interruption prediction is computed) In another embodiment, the real-time information may include task status updates transmitted from field professionals' communication devices 180A. For example, the real-time information may indicate that the second field professional had finished the assignment earlier than the estimated time for the completion of the task associated with location “E.” Based on the real-time progress information, and as shown in FIG. 3B, server 152 may reassign the first field professional to a task associated location “F,” and reassign the second field professional to a task associated location “A.” Thus, the updated schedule of first field professional includes tasks associated with locations F, B, C, and D and the updated schedule of second field professional includes tasks associated with locations E, A, G, H);
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose … including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality; and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
However, Mashin-Chi discloses …including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality (Mashin-Chi, ¶ 13, The inventive methodology, as disclosed herein, helps to identify repetitive voiding profiles for individuals (observed in isolation or as part of a group of individuals e.g. in a care facility) typically by considering one or more n-hour periods of incontinence data collected for the individuals. Incontinence data may be obtained manually, e.g. by a carer manually checking and changing incontinence aids, weighing the soiled aids and noting the time and relevant details (e.g. void type) of each event/check, as well as fluid and food intakes and other factors that may influence the incontinence behaviour of an individual. Alternatively/additionally, incontinence data may be obtained using sensors or other technology and may be supplemented (or supplied) by historical data for the subject including e.g. type of incontinence experienced, level of incontinence and the like. Ideally, each period of incontinence data utilised in the method corresponds to the same, or a similar, period of time in a n-hour block (discloses patient characteristics/medical history) so that relevant events are monitored from which voiding patterns can be computed), (Id., ¶ 168, In a one-to-one relationship, a goal of optimisation procedure 2006 may be to optimize the value/s of a mathematical function by seeking the minimum number of toileting procedures necessary to achieve adequate toileting. In another embodiment, a goal of optimisation procedure 2006 may be to align the number of toileting procedures with a property in a “general information” input such as e.g. allowable number of toiletings according to care guidelines. In yet another embodiment a goal may be to optimize one or more of the other objectives given in Table 1. Inputs 1001 may be used directly or indirectly to influence optimization procedure 2006. For example, information pertaining to intakes sets may be used for deriving more detailed information for events sets when events and intakes are linked e.g. by causal relationship. Subject information such as physical characteristics, non-preferred toileting times, continence holding ability, medical conditions and the like may be considered too).
Through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses … and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
First, Garber discloses calendar scheduling and calculating a likelihood of interruption for scheduled tasks (Garber, ¶ 141, Disclosed and claimed is a system that receives real-time reports (e.g., traffic updates, weather conditions), predicts that a field professional will not be able reach the customer at the scheduled time, and reassigns the customer to a different field professional. In one example, the system predicts that the field professional will miss a future task in his daily schedule. In another example, the system predicts that a delay would cause one or more tasks to be completed after a shift of a field professional is about to end), (Id., ¶ 447, the processing device may transmit information associated with the updated prediction model to the remote server for enabling improvement of the native scheduling engine. Improving the native scheduling engine may include updating the native scheduling engine based on the identified at least one factor. Consistent with the present disclosure, the information transmitted to the remote server may be associated with a plurality of scheduling parameters. For example, the information transmitted to the remote server may include indications of inaccurate estimations driving durations native scheduling engine, indications of inaccurate estimations of task durations, and indications of inaccurate skill requirements per task. The native scheduling engine may assign different task durations for a plurality of task types, different task durations for a plurality of field professionals, different task durations for a plurality of customer types, different task durations for time of day, different task durations for a plurality of areas of task locations, different task durations for a plurality of skills of the field professional, and more. In one embodiment, the local server may update the native scheduling engine by changing at least one value of the plurality of scheduling parameters. For example, originally the native scheduling engine of local server 3330B assumed it will take Bob 68 minutes to complete a certain task type. But using the updated native scheduling engine the local server may determines that there is 80% likelihood that it will take Bob 50 minutes to complete the task and 20% likelihood that it will take Bob 60 minutes to complete the task), (Id., ¶ 137, Presentation layer 502 may include software modules and processes (collectively, “components”) of application programs that use data stored in data layer 506 to perform actions. Example actions may include, without limitation, rendering information for display on a GUI presented to users through a display monitor. It should be appreciated that these actions may relate to any form of data manipulation or processing, and therefore are not limited to rendering data for display on a GUI. Indeed, many components of application programs manipulate data during “background” operations that are not noticeable to users of the computing system).
Further, Mashin-Chi discloses annotating a schedule display with a likelihood of an event (Mashin-Chi, ¶ 91, FIG. 9 represents a probability distribution of a “time type” input for estimating the time of an event activity which may be used to determine time indicator scheduling a procedure such as toileting a subject, calculated according to an embodiment of the invention. Where the value of the probability curve approaches 100%, the likelihood of an event occurring at that time is greater. An input of any type may also have a “certainty” value to indicate how confident the system is in the correctness of the input), (Id., ¶ 158, In a preferred embodiment, a toileting schedule calculated according to the present invention is adapted for display on a display device or printing for a file or bed record. Ideally, the displayed toileting schedule may be configured to show one or both of expected voiding event time indicators and toileting procedure time indicators. Control over the display may be achieved using e.g. a filter option provided via user interface 1400), (Id., Fig. 9, Figure depicts plotting a likelihood of interruption as a function of time).
One of ordinary skill in the art would have recognized that applying the known schedule annotation technique of Mashin-Chi would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of annotating a schedule with a likelihood of an event occurring, as in Mashin-Chi, to the teachings of Graber would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such medical professional scheduling features into similar scheduling systems. Further, annotating time block on a calendar with their respective likelihood of interruption values with based on calculated likelihood values, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more optimal scheduling of medical professional resources.
Thus, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses … and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting for the same reasons as stated for claim 1.
Regarding Claim 19, the combination of Garber and Mashin-Chi discloses …The non-transitory computer readable medium of claim 18…
Garber further discloses …wherein the indications of the estimated likelihoods of interruption annotated to the corresponding time blocks comprise color coding of the time blocks or icons added to the time blocks in accordance with the corresponding computed likelihoods of interruption. (Garber, ¶ 132, According to some embodiments, when propagating these actions, the system automatically monitors for discrepancies. The discrepancies may include: (1) Discrepancies between a forecast demand and allocated resources. (2) Discrepancies between different sources of the same information (e.g., forward-looking simulation vs. extrapolation of data using statistical trends analysis). (3) Discrepancies between different propagation directions, as when the planned resources are both dictated by higher management, propagating downwards, and also reported by regional management, propagating upwards. (4) Discrepancies between commitments made to customers and actual ability to deliver: For example, a customer may call with a problem and be told “someone will be with you tomorrow between 13:00 and 17:00,” because there appeared to be enough free resources during that time window, and without committing specific resources. Later there will be more calls are received and the software determines that there will be difficulty meeting this commitment, alerting the manager early enough to act, e.g., by diverting resources from another region. Another example for an even shorter planning-period: identifying the situation in which the service engineer is delayed in traffic or in an earlier task and will probably fail to arrive on time to the next task), (Id., ¶ 133, The system may provide alerts to draw the user's attention to discrepancies. Optionally, the alerts consist of color-coding of areas in the view (e.g., cells in a displayed table) according to the presence and severity of discrepancies. Optionally, the alerts may include presenting to the user a list of alerts, possibly ranked and color-coded by their severity. Optionally, the alerts may include messages transmitted to users defined as being in charge of reacting and/or resolving each type of alert. Messages may be transmitted by phone, cellular messaging, e-mail, fax, and instant messaging. In addition, the alerts may include of any combination of the above mechanisms, configurable according to the user's personal preferences, user type, alert type, and organizational procedures).
Regarding Claim 20, Garber discloses …An electronic calendar method comprising: maintaining an electronic calendar including assigning tasks for an owner of the electric calendar to time intervals of the electronic calendar; estimating, by applying a machine learning (ML) component to other schedules, likelihoods of interruption of the owner of the electronic calendar for time blocks of the calendar based on schedules of future tasks for others that interact with the owner of the electronic calendar, wherein the estimated likelihoods of interruption of the owner of the electronic calendar is estimated… wherein estimating the likelihoods of interruption includes computing the potential interruption predictions outside of a time block for which the potential interruption prediction is computed (Garber, ¶ 315, one or more field professional may be instructed to initiate a location-agnostic task before driving to a location associated with a second location-based task. The instruction may be provided by a variety of means, including automatically to the field professional communication device 180A, or posted to a publicly viewable calendar. Alternatively, the instruction may cause a dispatch person to personally notify a field professional via a phone call, radio call, email, or text message. Notifying the field professional before driving to the location associated with the second location-based task reduces lost time and maximizes field professional utilization. Alternatively, the instruction may be provided to one or more field professional after driving at least part of the way to a location associated with the second location-based task. For instance, if an accident causes traffic to back up after a field professional departs for the second location-based task, the system may notify the field professional to stop driving and complete a location-agnostic task while waiting for traffic to clear. After completing the location-agnostic task, the field professional may be instructed to perform more location-agnostic tasks if the traffic still caused delays. Alternatively, the field professional may resume driving to the second location-based task upon completion of the location-agnostic task with or without further notification), (Id., ¶ 9, a method for scheduling tasks to field professionals is provided (discloses maintaining a schedule of tasks). The method includes: receiving a set of requests reflecting demand for on-site services, wherein the set of requests is associated with a number of task types; receiving availability data indicative of an availability of a plurality of field professionals to perform on-site services; receiving skills data indicative of capabilities of each of the plurality of field professionals with respect to the task types; obtaining at least one desired scheduling weight for the number of task types; generating a schedule for the plurality of field professionals based on the demand for on-site services, the availability data, and the skills data; and wherein generating the schedule for the plurality of field professionals comprises including a first task in the schedule when the first task conforms with the at least one desired scheduling weight and excluding a second task from the schedule when the second task does not conform with the at least one desired scheduling weight.), (Id., ¶ 269, At step 908, processing device 202 may receive information that may affect the likelihood of the assigned field professional to complete the customer's request of service in a single visit. The information may include real-time information about a condition of object associated with the scheduled service, or the current status of parts (e.g., tools) that the field professional has currently available in his/her inventory. For example, the field professional may be a nurse (discloses medical professional) scheduled to do a home visit to do dialysis and the information may include updates on a health condition of a patient. The information may be received in response to an enquiry triggered by processing device 202 or independently by the customer), (Id., ¶ 36, Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein), (Id., ¶ 45, FIG. 6B is a diagram of example planned and actual schedules of field professionals, consistent with the present disclosure), (Id., ¶ 262, processing device 202 may determine the likelihood using a machine learning mode (discloses computing likelihood of interruption by applying a machine learning component). For example, a neural network model (e.g., a deep learning model) may be created and set with initial parameters. Based on statistics of the field professional completing the type of the new service, locations, and characteristics of the technical service under which the field professional completed the same type of the new service, the neural network model may be trained, and the initial parameters may be updated. Using the trained neural network model, by inputting the characteristics of the same type of the technical service, the location of the technical service, and the name of the field professional, the trained neural network may output a likelihood that the field professional will complete the technical service in a single on-site visit at the location), (Id., ¶ 325, Further illustration of the steps of process 1600 may be understood with reference to the steps of process 1700 in FIG. 17A and FIG. 17B. Process 1700 begins by receiving a request to book a new appointment for a service at step 1702. After the request is received, a multi-route model is executed. For example, at step 1704, a predictive machine learning algorithm may be executed to determine a first booking response. The predictive machine learning algorithm may be implemented through any machine learning technique. In some embodiments, the first scheduling model may use previous proposed times to determine the first proposed time. For example, if previous cable installations occurred at 2:00 on Tuesdays and internet installations occurred at 1:00 on Wednesdays, the first scheduling model may use this information when determining a first booking response for a new request for cable installation. Thus, at step 1706, the predictive machine learning algorithm may determine a first booking response corresponding to the initial request for an appointment), (Id., ¶ 330, At step 1730 of FIG. 17B, the first and second booking responses are compared. In some situations, step 1730 may be No, as the first and second booking responses may provide the same result. The process may terminate at this point. However, if the first and second booking responses differ, multiple options may be available. For instance, process 1700 may proceed to initiate an action to improve the multi-route model at step 1732. For example, the system may provide the second booking response, which may include a second proposed time, to the predictive machine learning algorithm. In this way, the second booking response may be an additional training input to retrain the predictive machine learning algorithm, thereby updating the predictive machine learning algorithm with the second booking response. Furthermore, the predictive machine learning algorithm may be retrained even if the first and second responses are identical, so as to reinforce a correct result in the algorithm. Alternatively, a scheduled assignment of at least one field professional may be changed when the first booking response is different than the second booking response at step 1734. In this way, the first booking response may be retained, providing greater consistency to users such that the first booking response is retained despite initially being invalid. In some embodiments, both step 1732 and step 1734 may be performed, such that both the multi-route model is improved and a schedule assignment is changed), (Id., ¶ 314, as shown in FIG. 15A, there may be two field professionals, field professional 1510 and second field professional 1520. Both may be qualified to install modems, but only field professional 1510 is qualified to establish virtual private networks remotely. If a hospital emergency room's virtual private network fails, a high urgency notification may indicate a necessary location-agnostic task of repairing the network. Subsequently, as shown in FIG. 15B, the additional location-based task of a modem installation may be reassigned from field professional 1510 to second field professional 1520. Field professional 1510 may then be assigned the location-agnostic task of remotely repairing the emergency room's virtual private network. Alternatively, field professional 1510 may have past experience with the hospital's network or superior past rankings by customers. Any combination of these and other factors could be used to determine that field professional 1510 is more suitable to provide the location-agnostic task), (Id., ¶ 152, FIG. 6B is a diagram of example planned and actual schedules of field professionals (discloses maintain schedules for other interacting professionals), consistent with the present disclosure. In FIG. 6B, the first field professional P1 corresponds to the first field professional in FIG. 3A, and each of locations A-D may have a specified or scheduled arrival time for P1. Each row of blocks represents a schedule associated with a field professional in FIG. 6B. White blocks represent the field professional's time durations when performing a task at a specified location or when the field professional is available. Dotted blocks represent the field professional's time durations when driving between the specified locations. A timeline is shown below the blocks, and dash lines are shown to indicate aligned time points of the schedules in the timeline), (Id., ¶ 166, as shown in FIG. 6B, processing device 202 may determine from real-time schedule information that the first field professional P1 will delay arriving at location A at scheduled time 10:36, and the second field professional P2 has finished a task at location E at 10:12, sooner than expected completion time at 10:39. Based on the traffic condition from location E to location A, weather conditions at locations E and A, or the type of the task to be performed at location A, processing device 202 may determine that the second field professional can arrive to location A at 10:36), (Id., ¶ 156, if processing device 202 receives the progress information from network interface 206 at step 602, processing device 202 may determine a delay associated with one or more tasks assigned to the first field professional. In some embodiments, processing device 202 may determine the delay from the progress information), (Id., ¶ 157, At step 606, processing device 202 determines a likelihood that the delay will interfere (disclose determining likelihood of interruption as a function of time) with the first field professional arriving at an identified location associated with an assigned task at a scheduled time. The scheduled time may be a time specified by processing device 202 for the first field professional to arrive at the identified location. For example, the identified location may be any of locations A-D in FIG. 3A. Though processing device 202 determines the existence of the delay at step 604, the delay may not necessarily cause an actual delay of the arrival time at the identified location. For example, the first field professional may find means (e.g., find a shorter route or increase moving speed) to make up for the delay), (Id., ¶ 165, Referring back to FIG. 6A, at step 608, processing device 202 determines from real-time schedule information associated with a second field professional whether the second field professional can arrive to the identified location associated with the task assigned to the first field professional at the scheduled time. The second field professional may be one of field professionals 110. For example, the second field professional may be the field professional assigned to tasks for providing technical service at locations E, F, G, H in FIGS. 3A-3B. In some embodiments, processing device 202 may determine whether the second field professional can arrive at the identified location at the schedule time based on any combination of the traffic conditions, weather conditions, or task performances associated with the second field professional), (Id., ¶ 166, For example, as shown in FIG. 6B, processing device 202 may determine from real-time schedule information that the first field professional P1 will delay arriving at location A at scheduled time 10:36, and the second field professional P2 has finished a task at location E at 10:12, sooner than expected completion time at 10:39. Based on the traffic condition from location E to location A, weather conditions at locations E and A, or the type of the task to be performed at location A, processing device 202 may determine that the second field professional can arrive to location A at 10:36), (Id., ¶ 116, FIGS. 3A and 3B depict two schematic maps illustrating the planned daily schedule of two field professionals and the updated daily schedule of the two field professionals. As shown in FIG. 3A, the first field professional was assigned to tasks for providing technical service at locations A, B, C, D; and the second field professional was assigned to tasks for providing technical service at locations E, F, G, H. The planned route of the first field professional is illustrated in a dashed line and the planned route of the second field professional is illustrated in a solid line. Assuming that the first field professional was scheduled to be at location “A” at 10:36 and at location “B” at 11:39; and the second field professional was scheduled to be at location “E” at 10:15 and at location “F” at 11:09. (discloses computing the interruption prediction with information from other schedules of future tasks) In the illustrated example, server 152 received at 9:15, from network interface 206, real-time information for the first and second field professionals. In one embodiment, the real-time information may include current location information derived at least partially from location circuits of field professionals' communication devices 180A. For example, the real-time information may indicate that first field professional is stuck on the road to location “A.” (discloses information outside of a time block (i.e. before the appointment) for which the potential interruption prediction is computed) In another embodiment, the real-time information may include task status updates transmitted from field professionals' communication devices 180A. For example, the real-time information may indicate that the second field professional had finished the assignment earlier than the estimated time for the completion of the task associated with location “E.” Based on the real-time progress information, and as shown in FIG. 3B, server 152 may reassign the first field professional to a task associated location “F,” and reassign the second field professional to a task associated location “A.” Thus, the updated schedule of first field professional includes tasks associated with locations F, B, C, and D and the updated schedule of second field professional includes tasks associated with locations E, A, G, H);
While suggested in at least Fig. 2 and related text, Garber does not explicitly disclose …based at least in part on data associated with the schedules of tasks for others including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality; and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
However, Mashin-Chi discloses …based at least in part on data associated with the other schedules including one or more of a type of procedure and protocol scheduled at an imaging modality, a patient's characteristic, a patient's medical history, or an experience level of a local technologist operating the imaging modality (Mashin-Chi, ¶ 13, The inventive methodology, as disclosed herein, helps to identify repetitive voiding profiles for individuals (observed in isolation or as part of a group of individuals e.g. in a care facility) typically by considering one or more n-hour periods of incontinence data collected for the individuals. Incontinence data may be obtained manually, e.g. by a carer manually checking and changing incontinence aids, weighing the soiled aids and noting the time and relevant details (e.g. void type) of each event/check, as well as fluid and food intakes and other factors that may influence the incontinence behaviour of an individual. Alternatively/additionally, incontinence data may be obtained using sensors or other technology and may be supplemented (or supplied) by historical data for the subject including e.g. type of incontinence experienced, level of incontinence and the like. Ideally, each period of incontinence data utilised in the method corresponds to the same, or a similar, period of time in a n-hour block (discloses patient characteristics/medical history) so that relevant events are monitored from which voiding patterns can be computed), (Id., ¶ 168, In a one-to-one relationship, a goal of optimisation procedure 2006 may be to optimize the value/s of a mathematical function by seeking the minimum number of toileting procedures necessary to achieve adequate toileting. In another embodiment, a goal of optimisation procedure 2006 may be to align the number of toileting procedures with a property in a “general information” input such as e.g. allowable number of toiletings according to care guidelines. In yet another embodiment a goal may be to optimize one or more of the other objectives given in Table 1. Inputs 1001 may be used directly or indirectly to influence optimization procedure 2006. For example, information pertaining to intakes sets may be used for deriving more detailed information for events sets when events and intakes are linked e.g. by causal relationship. Subject information such as physical characteristics, non-preferred toileting times, continence holding ability, medical conditions and the like may be considered too).
Through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses …and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
First, Garber discloses calendar scheduling and calculating a likelihood of interruption for scheduled tasks (Garber, ¶ 141, Disclosed and claimed is a system that receives real-time reports (e.g., traffic updates, weather conditions), predicts that a field professional will not be able reach the customer at the scheduled time, and reassigns the customer to a different field professional. In one example, the system predicts that the field professional will miss a future task in his daily schedule. In another example, the system predicts that a delay would cause one or more tasks to be completed after a shift of a field professional is about to end), (Id., ¶ 447, the processing device may transmit information associated with the updated prediction model to the remote server for enabling improvement of the native scheduling engine. Improving the native scheduling engine may include updating the native scheduling engine based on the identified at least one factor. Consistent with the present disclosure, the information transmitted to the remote server may be associated with a plurality of scheduling parameters. For example, the information transmitted to the remote server may include indications of inaccurate estimations driving durations native scheduling engine, indications of inaccurate estimations of task durations, and indications of inaccurate skill requirements per task. The native scheduling engine may assign different task durations for a plurality of task types, different task durations for a plurality of field professionals, different task durations for a plurality of customer types, different task durations for time of day, different task durations for a plurality of areas of task locations, different task durations for a plurality of skills of the field professional, and more. In one embodiment, the local server may update the native scheduling engine by changing at least one value of the plurality of scheduling parameters. For example, originally the native scheduling engine of local server 3330B assumed it will take Bob 68 minutes to complete a certain task type. But using the updated native scheduling engine the local server may determines that there is 80% likelihood that it will take Bob 50 minutes to complete the task and 20% likelihood that it will take Bob 60 minutes to complete the task), (Id., ¶ 137, Presentation layer 502 may include software modules and processes (collectively, “components”) of application programs that use data stored in data layer 506 to perform actions. Example actions may include, without limitation, rendering information for display on a GUI presented to users through a display monitor. It should be appreciated that these actions may relate to any form of data manipulation or processing, and therefore are not limited to rendering data for display on a GUI. Indeed, many components of application programs manipulate data during “background” operations that are not noticeable to users of the computing system).
Further, Mashin-Chi discloses annotating a schedule display with a likelihood of an event (Mashin-Chi, ¶ 91, FIG. 9 represents a probability distribution of a “time type” input for estimating the time of an event activity which may be used to determine time indicator scheduling a procedure such as toileting a subject, calculated according to an embodiment of the invention. Where the value of the probability curve approaches 100%, the likelihood of an event occurring at that time is greater. An input of any type may also have a “certainty” value to indicate how confident the system is in the correctness of the input), (Id., ¶ 158, In a preferred embodiment, a toileting schedule calculated according to the present invention is adapted for display on a display device or printing for a file or bed record. Ideally, the displayed toileting schedule may be configured to show one or both of expected voiding event time indicators and toileting procedure time indicators. Control over the display may be achieved using e.g. a filter option provided via user interface 1400), (Id., Fig. 9, Figure depicts plotting a likelihood of interruption as a function of time).
One of ordinary skill in the art would have recognized that applying the known schedule annotation technique of Mashin-Chi would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of annotating a schedule with a likelihood of an event occurring, as in Mashin-Chi, to the teachings of Graber would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such medical professional scheduling features into similar scheduling systems. Further, annotating time block on a calendar with their respective likelihood of interruption values with based on calculated likelihood values, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more optimal scheduling of medical professional resources.
Thus, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Garber and Mashin-Chi discloses …and generating a representation of the electronic calendar including the tasks assigned to the owner of the electronic calendar annotated to the corresponding time intervals and further including indications of the estimated likelihoods of interruption annotated to the corresponding time blocks for display on an electronic processing device.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the medical professional scheduling elements of Graber to include the event curve likelihood plotting elements of Mashin-Chi in the analogous art of scheduling procedures such as toileting for the same reasons as stated for claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Spektor et al., U.S. Publication No. 2021/0407657 discloses an office assistant tool.
Raman, U.S. Publication No. 2016/0379167 discloses dynamic resource allocation and scheduling.
Bal et al., U.S. Publication No. 2012/0323599 discloses scheduling of dose calculation tasks including efficient dose calculation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS D BOLEN whose telephone number is (408)918-7631. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM PST.
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/NICHOLAS D BOLEN/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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