DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 05/25/2026 has been entered.
Status of Claims
The following is a Final Office Action in response to applicant’s request for continued examination (RCE) received on 05/25/2026.
Claims 1 and 12 are amended. Claim 21 is newly amended. Claims 2, 10, 11, 14-16, and 20 are cancelled. Claims 1, 3-9, 12, 13, 17-19, and 21 are being considered in this Office Action. Claims 1, 3-9, 12, 13, 17-19, and 21 are currently pending.
Response to Amendments
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office Action.
Applicant’s amendments and arguments have been considered; however, they are primarily raised in light of applicant’s amendments. An updated 35 USC §101 rejection will address applicant’s amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-9, 12, 13, 17-19, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1, 3-9, 12, 13, 17-19, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance”.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1 and 3-9), the device (claim 12-13 and 17-19), and method (claim 21) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea of predicting double-booking appointments based on no-show probability of patients by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “mental process” group within the enumerated groupings of abstract ideas, wherein the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. (See MPEP 2106.04(a)(2)). The claims further recite methods of organizing human activity such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which falls into the “Certain Methods of Organizing Human Activity” group within the enumerated groupings of abstract ideas. The claims further recite methods of mathematical concepts such as applying a logistic regression learning technique, which is claimed broadly and falls into the “mathematical concept” group within the enumerated groupings of abstract ideas. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are: A method, comprising: extracting, at a Managed File Transfer (MFT) module, a set of attributes predictive of a probability that one or more of a plurality of appointment time slots associated with a time interval will be classified as a no-show, wherein the MFT module extracts the set of attributes among other information from one or more electronic health record (EHR) systems that include one or more appointment scheduling systems and EHR data for a plurality of patients, wherein an access control module is included to prevent unauthorized entry or access to the extracted information, and wherein the access control module comprises a single sign-on (SSO) service, and wherein the extracted set of attributes are organized as a data processing cluster at a device, the data processing cluster comprises a Hadoop cluster; cleaning and standardizing, by a data transformation module, the extracted set of attributes to ensure that predictive features are interpreted properly; generating a plurality of risk scores defining individual slot risks including an individual slot risk for each of the plurality of appointment time slots based on the extracted attribute data processing cluster, including modeling a probability that each of the plurality of appointment time slots will be classified as no-show based on risk factors associated with the set of attributes by applying a logistic regression learning technique to the set of attributes associated with each appointment time slot; generating a cumulative no-show risk that at least one patient will no-show and that at least one appointment time slot across the time interval will be classified as a no-show by aggregation of the plurality of risk scores to partial-day level corresponding to the time interval using binomial probability logic, wherein generating the plurality of risk scores and the cumulative no-show risk comprises executing a Structured Query Language (SQL) script to run intermediate parts including the data transformation and aggregation; timestamping and storing, in a lookup table, the plurality of risk scores and the cumulative no-show risk; comparing the cumulative no-show risk for the time interval with a predetermined threshold value; identifying a double-book opportunity for the time interval based on a comparison between the cumulative no-show risk and the predetermined threshold value; generating, by a processor, the double book opportunity for one or more of the plurality of appointment time slots via a scheduling report for the time interval based on the cumulative no-show risk for the time interval to intentionally book an additional patient on an already full schedule for the time interval with expectation that at least one patient will not show, wherein the scheduling report is generated at a report generation module by retrieving at least one of the plurality of risk scores and the cumulative no-show risk from the lookup table, and wherein the risk scores are translated into actionable statements of the double-book opportunity using a scheduling tool having a graphical user interface including a provider schedule view; and double-booking an appointment within the time interval based on the double-booking opportunity via the scheduling tool integrated within the appointment scheduling system to allow booking of the additional patient based on the cumulative no-show risk. Claims 12 and 21 recite substantially the same limitations as claim 1, and therefore subject to the same rationale.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to Managed File Transfer (MFT) module, the MFT module extracts the set of attributes among other information from one or more electronic health record (EHR) systems (recited at high level of generality and amounts to means to gather information and amounts to insignificant extra-solution activity), information, and wherein the access control module comprises a single sign-on (SSO) service (recited at high level of generality), and wherein the extracted set of attributes are organized as a data processing cluster at a device, the data processing cluster comprises a Hadoop cluster (recited at high level of generality), extracting, at a managed file transfer (MFT) module, scheduling-related attribute data from an electronic health record (EHR) data device and an appointment scheduling system over a communication network, reporting tool, a data transformation module, executing a Structured Query Language (SQL) script to run intermediate parts including the data transformation and aggregation(recited at high level of generality); timestamping and storing, in a lookup table, the plurality of risk scores and the cumulative no-show risk (recited at high level of generality and amounts to insignificant extra-solution activity), processor, a report generation module, using a scheduling tool having a graphical user interface including a provider schedule view, the scheduling tool integrated within the appointment scheduling system, A device, comprising: one or more network interfaces to communicate over a network, a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory configured to store a process executable by the processor, the process, when executed, is operable to functions/steps to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification [0019] describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. The examiner notes the step of extracting a set of attributes is considered abstract with accordance to MPEP 2106.04(a)(2). The section states that claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Furthermore, the extracting a set of attributes predictive of a probability that one or more of a plurality of appointment time slots associated with a time interval will be classified as a no-show, falls under insignificant extra-solution activity and “apply it”, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
The examiner further notes Managed File Transfer (MFT) module, the MFT module extracts the set of attributes among other information from one or more electronic health record (EHR) systems (recited at high level of generality), and wherein the access control module comprises a single sign-on (SSO) service (recited at high level of generality), and wherein the extracted set of attributes are organized as a data processing cluster at a device, the data processing cluster comprises a Hadoop cluster (recited at high level of generality), a data transformation module, executing a Structured Query Language (SQL) script to run intermediate parts including the data transformation and aggregation(recited at high level of generality) are recited at high level of generality and fail to yield a technical improvement or otherwise integrate the abstract idea into a practical application. The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words "apply it" (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the extracting, generating, modeling, timestamping at storing at a lookup table, comparing, identifying, generating, and scheduling activities are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: Managed File Transfer (MFT) module, the MFT module extracts the set of attributes among other information from one or more electronic health record (EHR) systems (recited at high level of generality and amounts to means to gather information and amounts to insignificant extra-solution activity), information, and wherein the access control module comprises a single sign-on (SSO) service (recited at high level of generality), and wherein the extracted set of attributes are organized as a data processing cluster at a device, the data processing cluster comprises a Hadoop cluster (recited at high level of generality), a data transformation module, executing a Structured Query Language (SQL) script to run intermediate parts including the data transformation and aggregation(recited at high level of generality); timestamping and storing, in a lookup table, the plurality of risk scores and the cumulative no-show risk (recited at high level of generality and amounts to insignificant extra-solution activity), processor, a report generation module, using a scheduling tool having a graphical user interface including a provider schedule view, the scheduling tool integrated within the appointment scheduling system, extracting, at a managed file transfer (MFT) module, scheduling-related attribute data from an electronic health record (EHR) data device and an appointment scheduling system over a communication network, reporting tool, a device, comprising: one or more network interfaces to communicate over a network, a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory configured to store a process executable by the processor, the process, when executed, is operable to functions/steps to implement the abstract idea. These elements have been considered but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification ([0019]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. With respect to Managed File Transfer module, applicant’s specification describes/discloses the module in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). With respect to the additional elements Managed File Transfer (MFT) module, the MFT module extracts the set of attributes among other information from one or more electronic health record (EHR) systems (recited at high level of generality), and wherein the access control module comprises a single sign-on (SSO) service (recited at high level of generality), and wherein the extracted set of attributes are organized as a data processing cluster at a device, the data processing cluster comprises a Hadoop cluster (recited at high level of generality), a data transformation module, executing a Structured Query Language (SQL) script to run intermediate parts including the data transformation and aggregation(recited at high level of generality), applicant’s specification describes the additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a). For example, but not limited, the following: “[[0019] As shown, communication network 105 includes a geographically distributed collection of client devices, such as devices 110a and 110b (collectively, "devices 110") along with at least one electronic health record (EHR) data device 102, at least one appointment scheduling system 104 and a web application host 106. Devices 110 and (EHR) data device 102, appointment scheduling system 104 and web application host 106 are interconnected by communication links and/or network segments and exchange or transport data such as data packets 140 to/from a predictive double-booking system 120. Here, devices 110 include main computing device 110a and a client device 110b. Further, devices 110 can include data processing clusters such as a Hadoop cluster. The illustrated client devices represent specific types of electronic devices, but it is appreciated that devices 110 in the broader sense are not limited to such specific devices. For example, devices 110 can include any number of electronic devices such as desktop computers, laptops, smart watches, wearable smart devices, personal digital assistants (PDAs), smart glasses, smart home devices, accessibility devices, other wearables, and so on. In addition, those skilled in the art will understand that any number of devices and links may be used in communication network 105, and that the views shown by FIG. 1 are for simplicity and discussion.” “[0030]…. In some embodiments, slot risk assessment module 230 is implemented in Structured Query Language (SQL).” “[0032] In some embodiments, a master script for predictive model engine 204 is built using Structured Query Language (SQL) and runs each of the intermediate parts, including data transformation module 202 to ensure that predictive features are interpreted properly from the patient data 221, clinic data 222 and appointment data 223, clinic classification scoring, universal scoring, and time interval aggregation and distribution. The aggregated predictions are timestamped and stored in a lookup table 250 to be referenced by scheduling module 206 of the predictive double booking system 200.” “[0034] Referring to FIG. 5, a simplified diagram 300 is illustrated showing an example implementation of aspects of the predictive double booking system 200. The predictive model engine 204 of the predictive double booking system 200 receives attributes 220 for each appointment slot of a plurality of appointment slots within a time interval. In some embodiments, a Managed File Transfer (MFT) module 304 extracts the attributes 220 among other information from EHR systems 302 that include various appointment scheduling systems and EHR data for a plurality of patients. An access control module 306 is further included to prevent unauthorized entry or access to the data. In one embodiment, the access control module 306 is a single sign-on (SSO) service. In some embodiments, the data is organized at a device 110 such as cluster 308, which is in some embodiments a Hadoop cluster”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well (i.e., claim 4 recites storing at least one of the cumulative no-show risks and the plurality of individual slot risks in a lookup table, claim 5 recites retrieving at least one of the cumulative no-show risks and the plurality of individual slot risks from the lookup table upon report generation. The additional elements are directed to the use of generic computing elements (Applicant’s Specification [0019] describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the 2019 PEG) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification ([0019]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter ), however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of certain method of organizing human activity, mathematical concept, and a mental process, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Fitih Cinner (US 20200151634 A1, hereinafter “Cinner”) in view of Brian Lennstrom (US 20130346522 A1, hereinafter “Lennstrom”) in view of Jock Lawrie (WO 2018058189 A1, hereinafter “Lawrie”).
Claim 21
Cinnor teaches:
A method, comprising: extracting scheduling-related attribute data from an electronic health record (EHR) data device and an appointment scheduling system over a communication network([0034] gathering and characterizing historical scheduling, clinical, demographic, administrative and other data about appointment status trends for a given practice; defining, storing, and tracking quantifiable performance indicators over time showing trends and patterns in no-shows, cancelations, delays, and other similar undesirable outcomes; application programming interface (API) and/or other forms of integration schemes into existing electronic health records, practice management, and other software and systems; ]0060] the communications means of the system may be, for instance, any means for communicating data over one or more networks or to one or more peripheral devices attached to the system),
the attribute data being predictive of whether one or more appointment time slots within a time interval will be classified as a no-show([0022] Demand/Supply Matching Engine (DSME) configured to execute a process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores, where [0020] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of no-shows, delays, or cancellations; practice resource utilization/allocation preferences; demand forecasting; and, other related factors); organizing the extracted attribute data as a data processing cluster([0023] the processor 305 creating an N×M matrix representing N providers and M time slots. Then, the method continues at step 410 with the processor 305 computing a probability score for each entry (N.sub.i, M.sub.j) for no-shows, delays, or cancellation. [0024] Demand/Supply Matching Engine (DSME) automatically adapting demand and supply matched as functions of service consumer scores, service provider scores, and service time slot scores);
generating, using the data processing cluster, a respective no-show risk score for each of a plurality of appointment time slots within the time interval by applying a learning technique to the extracted attribute data([0024] Demand/Supply Matching Engine (DSME) automatically adapting demand and supply matched as functions of service consumer scores, service provider scores, and service time slot scores. The processor 305 selecting an N×M matrix consisting of probability scores for no-show, delay, or cancellation. Then, the method continues at step 510 with the processor 305 assigning an occupancy state representing whether each provider/time slot combination in the selected N×M matrix is filled or not, while [0034] characterizing demand patterns, trends, and cycles for the specific population served by a given practice to build a demand forecasting model based on influential variables; modeling the interaction between variables of interest (first-order, second-order, etc.) and assigning influence scores on each interaction and the broader outcome of interest using concepts and tools including, but not limited to, regression analysis, decision trees, neural networks, clustering, linear algebra, and any other mathematical methods and analyses; aggregating variable scores to generate cumulative scores for predicting of a client's no-show probability, a provider's performance, and probability of no-show, delay, and cancellation for a given time slot; classification of outcomes (for example, show versus no-show) based on an adjustable threshold parameter, and using such classification to characterize and make predictions about client and broad population level behaviors);
generating, by the reporting tool, a scheduling report that identifies at least one double-book opportunity for the time interval based on the cumulative no- show risk([0027] risk heatmap exemplary of an embodiment Demand/Supply Matching Engine (DSME) automatically adapting demand and supply matched as functions of service consumer scores, service provider scores, and service time slot scores; risk heatmap 800 highlights actionable insights for service providers. In the depicted embodiment, the color coded risk heatmap 800 depicts the percentage risk for a given day based on predicted no-shows, delays and cancellations. [0036] updating client score based on input variables; gathering a provider's disposition of client's request; presenting a select list of available slots based on algorithmic matching of client, provider, and time slot scores; booking and confirming a schedule; overbooking additional clients for slots with higher likelihood of no-show or cancellation; keeping an updated wait list of clients sorted by probability score for no-show, delay, or cancellation; keeping an updated list of provider/time slot availability sorted by probability score for no-show, delay, or cancellation; matching clients with available slots on events of cancellation based on probability scores for no-show, delay, or cancellation).
While Cinner teaches [0034] gathering and characterizing historical scheduling, clinical, demographic, administrative and other data about appointment status trends for a given practice; defining, storing, and tracking quantifiable performance indicators over time showing trends and patterns in no-shows, cancelations, delays, and other similar undesirable outcomes; application programming interface (API) and/or other forms of integration schemes into existing electronic health records, practice management, and other software and systems; ]0060] the communications means of the system may be, for instance, any means for communicating data over one or more networks or to one or more peripheral devices attached to the system. Cinner does not explicitly teach the following, however analogues reference Lennstrom teaches:
at a managed file transfer (MFT) module ([0074] Managed File Transfer (MFT) forwarder module 242).
It would have been obvious for a person having ordinary skill in the art at the time of invention to modify the teachings of Cinnor to include those of Lennstrom, to include MFT module as part of the scheduling and booking system taught in Cinnor, because doing so would provides secure, automated, and trackable data exchange between patient and providers [0098]-[0100].
While Cinner teaches: [0024] Demand/Supply Matching Engine (DSME) automatically adapting demand and supply matched as functions of service consumer scores, service provider scores, and service time slot scores. The processor 305 selecting an N×M matrix consisting of probability scores for no-show, delay, or cancellation. Then, the method continues at step 510 with the processor 305 assigning an occupancy state representing whether each provider/time slot combination in the selected N×M matrix is filled or not, while [0034] characterizing demand patterns, trends, and cycles for the specific population served by a given practice to build a demand forecasting model based on influential variables; modeling the interaction between variables of interest (first-order, second-order, etc.) and assigning influence scores on each interaction and the broader outcome of interest using concepts and tools including, but not limited to, regression analysis, decision trees, neural networks, clustering, linear algebra, and any other mathematical methods and analyses; aggregating variable scores to generate cumulative scores for predicting of a client's no-show probability, a provider's performance, and probability of no-show, delay, and cancellation for a given time slot; classification of outcomes (for example, show versus no-show) based on an adjustable threshold parameter, and using such classification to characterize and make predictions about client and broad population level behaviors. [0027] risk heatmap exemplary of an embodiment Demand/Supply Matching Engine (DSME) automatically adapting demand and supply matched as functions of service consumer scores, service provider scores, and service time slot scores; risk heatmap 800 highlights actionable insights for service providers. In the depicted embodiment, the color coded risk heatmap 800 depicts the percentage risk for a given day based on predicted no-shows, delays and cancellations. [0036] updating client score based on input variables; gathering a provider's disposition of client's request; presenting a select list of available slots based on algorithmic matching of client, provider, and time slot scores; booking and confirming a schedule; overbooking additional clients for slots with higher likelihood of no-show or cancellation. Cinnor does not explicitly teach the following, however analogues reference Lawrie teaches:
aggregating the respective no-show risk scores to generate a cumulative no- show risk for the time interval([106] Now, once having been trained, the trained artificial neural network is utilised to calculate the expected/predicted attendance failure probability 213. [107] The output expected attendance failure probability 213 may either be specific to a particular clinic or to a group of clinics. For the former, the output attendance failure probability 213 may represent, for example, that for Dr John Smith, the expected attendance failure probability 213 for a particular clinic day/period would be 10% for the specific patient or clinic.);
storing, in a lookup table, at least one of the respective no-show risk scores and the cumulative no-show risk, wherein the lookup table is referenced by a reporting tool when relevant([057]the web server application 110, upon receiving a web requests, is able to dynamically generate webpage responses utilising the hypertext preprocessor 106 and a database server 111. [59] For the specific outpatient clinic attendance optimisation computer processes as described herein, there is shown the memory device 114 of the server 101 comprising a plurality of software modules 103 - 105 and respective database tables 115 - 118. [60] As is shown, the software modules may comprise a trained machine module 104. As will be described in further detail below, the trained machine module 104 has as input patient specific data and clinic specific data 116 and is configured for calculating attendance failure probabilities 117 accordingly. [64] elatedly, the client terminal 102 may have a scheduler module 120 and store patient and clinic data 121 and attendance data 122 for the respective clinic schedules. [71] Specifically, the machine learning module 103 trains the trained machine module 100 for using historical training data 201 which may be obtained via database interface 205.[72] The historical training data 201 comprises patient specific training data 202 representing a plurality of patients, and clinic specific training data 203 representing a plurality of clinics. [73] Furthermore, the historical training data 201 comprises attendance training data 204 representing attendance by the plurality of patients of the respective historical clinics).
and providing the scheduling report to a scheduling tool to enable access to appointment booking information including eligible double-booking slots([113] As such, the scheduler module 105 would then update the outpatient schedule 118. In embodiments, the server 101 would update the schedule of the client terminal 102 remotely such as by having access to the schedule. In alternative embodiments, the server 101 may send the number of overbookings 217 to the client terminal 102 such that the client terminal 102 is able to update the schedule itself).
It would have been obvious for a person having ordinary skill in the art at the time of invention to modify the teachings of Cinnor and Lennstrom to include those of Lawrie, to include aggregating the respective no-show risk scores to generate a cumulative no- show risk for the time interval, storing, in a lookup table, at least one of the respective no-show risk scores and the cumulative no-show risk, wherein the lookup table is referenced by a reporting tool when relevant, and providing the scheduling report to a scheduling tool to enable access to appointment booking information including eligible double-booking slots, because doing so would help improving patient scheduling inefficiencies.
Examiner Notes
Claims 1, 3-9, 12, 13, and 17-19 are objected, but would be allowable, if they were amended in such a way to overcome the 35 USC 101 rejection set forth in the action.
Independent claims 1 and 12 are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art combination of the record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particularenvironment of the claims.
The closet prior art of record is Christopher Moses (US 2015/0242819 A1, hereinafter “Moses”), Fitih Cinnor (US 2020/0151634 A1, hereinafter “Cinnor”), Jock Lawrie (WO 2018058189 A1, hereinafter “Lawrie”), Leon Cui (NPL: “Reminder Systems for Reducing No-shows in General Practices”, published 2012, hereinafter “Cui”), Anup Lakare (US 2016/0292369 A1, hereinafter “Lakare”), and Xiang Zhong (US 2016/0253462 A1, hereinafter “Zhong”).
Moses is directed to scheduling appointments efficiently by generating predictive models using historical appointment data and using these models to predict in advance whether an appointment will be a no-show or a cancellation. The predictive models may be based on logistic regression methods, support vector machines, or neural networks. If the model predicts that an appointment in a particular time-slot will probably be a no-show/cancellation, a scheduling system may decide to double-book that time-slot in order to reduce scheduling inefficiency. Cinner is directed to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. In an illustrative example, the service consumer may be a client. The service provider may be, for example, a professional offering service in an available time slot. In some examples, individual client, provider, and time slot scores may be calculated as functions of predictive variables associated with a client population and the provider practice environment. In some embodiments, scores may be probability estimates of show, no-show, delay, or cancellation. Various embodiments may advantageously determine schedules with maximum likelihood of full occupancy to optimize resource utilization and revenue expenditure, based on collectively optimizing client, provider, and time slot probability estimates. Lawrie is directed to a supervised machine learning module for optimising outpatient clinic attendance accordingly that dynamically overbooks a clinic schedule according to the patient specific and clinic specific parameters to optimise outpatient clinic attendance. The system comprises a trained machine module. The trained machine module is configured for having as input patient specific data and clinic specific data and calculating an attendance failure probability accordingly. The system further comprises a machine learning module configured for training the trained machine module. The machine learning module trains the trained machine module using historical training data comprising patient specific training data representing a plurality of patients, clinic specific training data representing a plurality of clinics and attendance training data representing attendance by the plurality of patients for each of the historical clinics. Once the trained machine has been optimised in this way, in use, for a plurality of future clinics, the trained machine is configured for calculating an attendance probability (or probabilities) for the future clinics. Then, the future clinics are overbooked by a number of patients according to the calculated attendance failure probabilities to generate attendance probability optimised future clinics. Cui is directed to a automated appointment reminder systems significantly reduce patient no-show rates in general practices by converting passive notifications into active engagements. Lakare is directed to creating and scheduling an appointment is described that patients can use themselves for booking a doctor appointment, cancellation, rebooking, and the like. The approaches described herein make a large step in the direction of completely automating scheduling appointments of service by service users with service providers thereby eliminating most manual intervention. Zhong is directed to patient appointment schedule is generated for an open access time window (typically a single day). A no show likelihood is assigned for each time slot based on past patient no show (missed appointment) information, and time slots whose no show likelihood exceeds a threshold are designated as open access time slots. The patient appointment schedule is displayed. During scheduling, an unfilled time slot may be allocated to a patient so as to be converted to a tilled time slot, or conversely a filled time slot may be deallocated so that the filled time slot is converted to an unfilled time slot.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625
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