Notice of Pre-AIA or AIA Status
This action is made in response to the amendments/remarks filed on 10/21/2025. This action is made FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 10/21/2025 has been entered. Claims 1-6, 8-17, and 19-20 remain pending in the application. Claims 7 and 18 have been cancelled.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-16, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cave et al. (US 2021/0082072) (Hereinafter Cave) in view of Albert (US 2018/0240547).
Regarding Claim 1, Cave teaches the following:
A system for identifying actions for medical providers ([0010], [0041] a computer system for identifying medical care providers; the health plan, physician group, or any other organization can take action (i.e., implement strategies that fit each health plan, physician group, or any other organization's philosophies for reducing practice patterns variation) to improve efficiency), comprising:
a computing device including memory storing program instructions for a data visualization tool and program instructions for a medical provider quality algorithm, the computing device including at least one processor programmed or configured to ([0010], [0038], Fig. 11: The computer system includes at least one processor and a memory coupled to the processor containing computer executable instructions that, when executed, cause the at least one processor to perform steps; The present invention provides software, methods, and algorithms that automate this process):
execute the data visualization tool ([0010] the steps include aggregating the statuses across the marker-condition pairs of the set of medical conditions to obtain an overall score for the first medical care provider, and causing an output to be displayed in a viewable format.);
receive data associated with medical providers ([0010], [0012]: When executed by a processor, the computer instructions cause the processor to perform steps that include retrieving claim line item information including at least 1,000 claim line item records for episodes of care attributable to a first medical care provider), wherein the data associated with medical providers includes data related to medical services performed by a plurality of medical providers ([0047]-[0054]: The data associated with the medical providers may be in the form of MedMarkers and service codes that correspond to the various procedures that are performed by the providers. For example, Endoscopies, biopsies, “shave skin lesions”, etc.); and
execute the medical provider quality algorithm to generate a plurality of projected medical parameters ([0010], [0011], [0012], Fig. 3, Fig. 7: The claim line item information includes, in aggregate, at least 40 codes each used to report a corresponding one of a medical, surgical, or diagnostic procedure or service.) and medical provider quality ratings based on the data associated with the medical providers ([0010] the steps include aggregating the statuses across the marker-condition pairs of the set of medical conditions to obtain an overall score for the first medical care provider);
a database configured to store the data associated with medical providers (Cave, Claim 12: storing, in the database, a plurality of predefined sets of medical conditions, each of the predefined sets associated with a corresponding medical practice category), the plurality of projected medical parameters, and the medical provider quality ratings ([0171], Fig. 11: Computer software such operating systems, utilities, user programs, and software to implement the present invention and data files can be stored in a Computer Software Storage Medium); and
the data visualization tool being configured to generate a graphical display and transmit the graphical display to a display device, the graphical display including a visual object corresponding to a parameter prioritization list based on a set of evaluation factors associated with a set of projected medical parameters for a selected medical provider of the plurality of medical providers ([0230] Using providers summary display tier 1402, the user reviews the overall score 1406 (e.g., the simple overall score 1408 and/or the weighted overall score 1410) and the corresponding overall status (e.g., pass/fail) for each medical care provider, based on the criteria selected in configuration display tier 1201. To drill down into a particular medical care provider's evaluation, the user selects (e.g., clicks on) that medical care provider in list 1404 on providers summary display tier 1402 to view provider detail display tier), wherein the parameter prioritization list includes prioritized projected medical parameters of the set of projected medical parameters ([0044] identifying only those procedures and services (e.g., CPT-4 and HCPCS codes) that are most associated with the health care provider efficiency score), such that the visual object provides an indication of an identified action for [a] medical provider ([0230] the decision-maker may be able to present the medical care provider with an objective, concise, clinically supported set of objective goals (i.e., a set of marker-condition pairs and target points) that the medical care provider needs to meet or improve upon in order to qualify for relaxation of prior authorization).
However, Cave does not teach the following that is met by Albert:
wherein a medical provider quality rating provides a realtime measure of performance of a medical provider (Albert [0091]: the system may generate various reports, charts, and graphs which demonstrate the value of a given healthcare provider’s day, week, month, year, etc. The average score of the medical provider may be updated in real time);
an indication of an identified action for at least a highest priority projected medical parameter in the parameter prioritization list for the selected medical provider (Albert [0076], [0092]: the list includes ranked optimization activities that correspond to visit value factors. The tasks may be queued for prioritization)
wherein the at least one processor is programmed or configured to:
identify a contact computing system associated with the highest priority projected medical parameter based on a type of the highest priority projected medical parameter (Albert [0091]-[0092]: the system includes a healthcare provider screen on an identified healthcare provider device, which generates various reports, charts, and graphs which demonstrate the value of a given healthcare provider. The screen displays the list of ranked activities for the provider to do based on the various parameters set forth); and
transmit a signal to the contact computing system to cause an alert at the contact computing system, the alert relating to the identified action (Albert [0073], [0078], Claim 1: alerts and notifications are created automatically in this embodiment by a rules engine, which is a module of the system that examines the analyzed data set(s) via various probabilistic statistical and deterministic approaches. The notifications are sent to a healthcare provider and may be displayed via a graphical user interface, text message, SMS message, email, phone call, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system for identifying actions for medical providers, as taught by Cave, with the real time measurement of providers and the prioritization of actions given to the provider, as taught by Albert. By updating the data in real time, the actions for the provider to perform can be prioritized better to improve the value of a provider’s score and to maximize the potential for future scores (See Albert [0090], [0092]).
Regarding Claim 2, the combination of Cave and Albert teaches the limitations of claim 1, and Cave further teaches:
The system of claim 1, wherein the indication of an identified action for the medical provider comprises an indication that at least one projected medical parameter of the set of projected medical parameters is associated with a least evaluation factor of the set of evaluation factors ([0112] to identify the CPT-4 code most associated with efficiency score. In FIG. 8, CPT-4 procedure 11100 has a correlation of 0.289, 11101 has a correlation of 0.218, 11401 has a correlation of 0.302, and 11402 has a correlation coefficient of 0.221. These all have a correlation coefficient greater than 0.2, which is an exemplary cutoff in one implementation of the present invention).
Regarding Claim 3, the combination of Cave and Albert teaches the limitations of claim 1, and Albert further teaches the following:
wherein the data associated with medical providers is received from a medical provider data source (Albert [0014] “obtain data transmitted by health care providers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Cave with the data source, as taught by Albert, since such data may be useful to generate advanced predictive analytics which help practices not only predict issues, but analyze and resolve them in the most efficient manner possible (see Albert [0014])
Regarding Claim 4, the combination of Cave and Albert teaches the limitations of claim 1, and Cave further teaches:
The system of claim 1 , wherein each evaluation factor of the set of evaluation factors is a standardized score ([0086] For a specific medical condition, each health care provider's service code per episode rate is divided by the corresponding peer group condition-specific service code per episode rate to calculate a score. For example, a dermatologist's benign neoplasm of the skin biopsy rate per episode may be 0.500 services per episode. The peer group benign neoplasm of the skin biopsy rate per episode may be 0.250, resulting in a CPT-4 score for the dermatologist of a 0.500/0.250=2.00).
Regarding Claim 5, the combination of Cave and Albert teaches the limitations of claim 1, and Cave further teaches:
The system of claim 1 , wherein the at least one processor (([0010], [0038], Fig. 11: The computer system includes at least one processor) is further programmed or configured to:
aggregate each projected medical parameter and each evaluation factor of a plurality of evaluation factors, wherein the set of projected medical providers and the set of evaluation factors is associated with the selected medical provider ([0188] aggregate the comparisons across marker-condition pairs to determine an overall score for the respective medical care provider); and
However, Cave does not disclose the following that is met by Albert:
rank each projected medical parameter for each medical provider based on the parameter value associated with the projected medical parameter to provide ranked medical parameters for each medical provider ([0011], [0092]: a rules engine which ranks specific opportunities to improve the clinical encounter for a medical provider of a plurality of providers).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the aggregation of parameters, as taught by Cave, with the ranking of each parameter for each medical provider, as taught by Albert, because by prioritizing the medical parameters for each provider, healthcare providers can select specific interventions to implement to maximize expected financial and clinical outcomes (See Albert [0011]).
Regarding Claim 6, the combination of Cave and Albert teaches the limitations of claim 1, and Cave further teaches:
The system of claim 1, wherein the at least one processor is further programmed or configured to: determine an average parameter value ([0074] For each health care provider, calculate an overall weighted average service and sub-service category score) based on averaging each parameter value of a type of a projected medical parameter across the plurality of medical providers ([0084] The present invention uses this information at the overall weighted average level to calculate a unique procedure or service code score… For example, a dermatologist's overall skin biopsy rate per episode may be 0.477 services per episode. The peer group skin biopsy per episode rate is 0.175).
Regarding Claim 8, the combination of Cave and Albert teaches the limitations of claim 5, and Cave further teaches:
The system of claim 5, wherein the at least one processor is further programmed or configured to: assign a ranked medical parameter a higher priority value ([0012] “pass status”) where the evaluation factor associated with the ranked medical parameter is less than all remaining evaluation factors of the set of evaluation factors ([0012] “in response to the actual rate of utilization not exceeding the target rate of utilization”); and
assign the ranked medical parameter a lower priority value ([0012] “fail status”) where the evaluation factor associated with the ranked medical parameter is greater than all the remaining evaluation factors of the set of evaluation factors ([0012] “in response to the actual rate of utilization exceeding a target rate of utilization”).
Regarding Claim 9, the combination of Cave and Albert teaches the limitations of claim 5, and Cave further teaches:
The system of claim 5, wherein each evaluation factor is a value equal to a difference between the projected medical parameter and an average parameter value associated with the projected medical parameter ([0037] to calculate [the provider’s] efficiency score… the present invention uses a statistical measure, such as a Pearson's Correlation (a statistic that associates two variables - in this case it is typically the health care provider's efficiency score from EfficiencyCare system (other statistical tools, models, and distributions are also within the scope of this invention)), to a procedure or service (e.g., CPT-4 or HCPCS code) score).
Regarding Claim 10, Cave teaches:
A computer-implemented method for identifying actions for medical providers ([0038] The present invention provides software, methods, and algorithms that automate this process), comprising:
receiving, with at least one processor, data associated with medical providers ([0010], [0012]: When executed by a processor, the computer instructions cause the processor to perform steps that include retrieving claim line item information including at least 1,000 claim line item records for episodes of care attributable to a first medical care provider), wherein the data associated with medical providers includes data related to medical services performed by a plurality of medical providers ([0047]-[0054]: The data associated with the medical providers may be in the form of MedMarkers and service codes that correspond to the various procedures that are performed by the providers. For example, Endoscopies, biopsies, “shave skin lesions”, etc.);
generating, with the at least one processor, a plurality of projected medical parameters including a parameter value based on the data associated with medical providers, wherein a set of projected medical parameters of the plurality of projected medical parameters is associated with a medical provider ([0010], [0011], [0012], Fig. 3, Fig. 7: The claim line item information includes, in aggregate, at least 40 codes each used to report a corresponding one of a medical, surgical, or diagnostic procedure or service);
determining, with the at least one processor, a medical provider quality rating associated with the medical provider based on the set of projected medical parameters ([0010] the steps include aggregating the statuses across the marker-condition pairs of the set of medical conditions to obtain an overall score for the first medical care provider) and a plurality of action groups, each projected medical parameter of the set of projected medical parameters being assigned to an action group of the plurality of action groups ([0093] determine the strength of the relationship between the health care provider efficiency score and health care provider service category, sub-service category, and service code score. This coefficient provides a numeric measure of the strength of the linear relationship between these two variables.), each projected medical parameter having a weight value based on a number of action groups that apply to the medical provider ([0074] For each health care provider, calculate an overall weighted average service and sub-service category score.);
prioritizing, with the at least one processor, each projected medical parameter based on an evaluation factor of a set of evaluation factors associated with the set of projected medical parameters and based on the parameter value (plan-percentile outlier table 1354 summarizes performance across medical providers represented in the CLI file for the medical specialty 1206 that was selected using specialty control 1204 (shown in FIG. 12)… plan-percentile outlier table 1354 includes columns 1356 representing the potential target points defined with reference to percentile ranking within a range of actual rates of utilization of the marker-condition pair by medical care providers in the health plan) to provide a parameter prioritization list, the parameter prioritization list representing a generated recommendation of the set of projected medical parameters for the medical provider to improve to increase the medical provider quality rating ([0230] Using providers summary display tier 1402, the user reviews the overall score 1406 (e.g., the simple overall score 1408 and/or the weighted overall score 1410) and the corresponding overall status (e.g., pass/fail) for each medical care provider, based on the criteria selected in configuration display tier 1201. To drill down into a particular medical care provider's evaluation, the user selects (e.g., clicks on) that medical care provider in list 1404 on providers summary display tier 1402 to view provider detail display tier; [0230] the decision-maker may be able to present the medical care provider with an objective, concise, clinically supported set of objective goals (i.e., a set of marker-condition pairs and target points) that the medical care provider needs to meet or improve upon in order to qualify for relaxation of prior authorization);
generating, with the at least one processor, a graphical display associated with the medical provider for transmitting to a client display device, the graphical display including a visual object corresponding to the parameter prioritization list, the visual object providing an indication of an identified action for the medical provider (Using providers summary display tier 1402, the user reviews the overall score 1406 (e.g., the simple overall score 1408 and/or the weighted overall score 1410) and the corresponding overall status (e.g., pass/fail) for each medical care provider, based on the criteria selected in configuration display tier 1201. To drill down into a particular medical care provider's evaluation, the user selects (e.g., clicks on) that medical care provider in list 1404 on providers summary display tier 1402 to view provider detail display tier; [0230] the decision-maker may be able to present the medical care provider with an objective, concise, clinically supported set of objective goals (i.e., a set of marker-condition pairs and target points) that the medical care provider needs to meet or improve upon in order to qualify for relaxation of prior authorization);
However, Cave does not teach the following that is met by Albert:
wherein the medical provider quality rating provides a realtime measure of performance of a medical provider (Albert [0091]: the system may generate various reports, charts, and graphs which demonstrate the value of a given healthcare provider’s day, week, month, year, etc. The average score of the medical provider may be updated in real time);
identifying, with the at least one processor, a contact computing system associated with the set of projected medical parameters based on the generated recommendation (Albert [0091]-[0092]: the system includes a healthcare provider screen on an identified healthcare provider device, which generates various reports, charts, and graphs which demonstrate the value of a given healthcare provider. The screen displays the list of ranked activities for the provider to do based on the various parameters set forth); and
transmitting, with the at least one processor, a signal to the contact computing system to request an action from the contact computing system relating to the generated recommendation (Albert [0073], [0078], Claim 1: alerts and notifications are created automatically in this embodiment by a rules engine, which is a module of the system that examines the analyzed data set(s) via various probabilistic statistical and deterministic approaches. The notifications are sent to a healthcare provider and may be displayed via a graphical user interface, text message, SMS message, email, phone call, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system for identifying actions for medical providers, as taught by Cave, with the real time measurement of providers and the prioritization of actions given to the provider, as taught by Albert. By updating the data in real time, the actions for the provider to perform can be prioritized better to improve the value of a provider’s score and to maximize the potential for future scores (See Albert [0090], [0092]).
Regarding Claim 11, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, wherein the indication of an identified action for the medical provider comprises an indication that at least one projected medical parameter of the set of projected medical parameters is associated with a least evaluation factor of the set of evaluation factors ([0112] to identify the CPT-4 code most associated with efficiency score. In FIG. 8, CPT-4 procedure 11100 has a correlation of 0.289, 11101 has a correlation of 0.218, 11401 has a correlation of 0.302, and 11402 has a correlation coefficient of 0.221. These all have a correlation coefficient greater than 0.2, which is an exemplary cutoff in one implementation of the present invention).
Regarding Claim 12, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, further comprising:
transmitting the graphical display to at least one client device including a client display device ([0174] processor may cause GUI 1200 to display on a display screen of client computing device via Internet communication with client computing device).
Regarding Claim 13, the combination of Cave and Albert teaches the limitations of Claim 10, and Albert further teaches:
The computer-implemented method of claim 10, wherein the data associated with medical providers is received from a medical provider data source (Albert [0014] “obtain data transmitted by health care providers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Cave with the data source, as taught by Albert, since such data may be useful to generate advanced predictive analytics which help practices not only predict issues, but analyze and resolve them in the most efficient manner possible (see Albert [0014]).
Regarding Claim 14, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, wherein a plurality of medical providers includes the medical provider, each other medical provider of the plurality of medical providers being associated with a different set of projected medical parameters of the plurality of projected medical parameters (Fig. 6, Fig. 7, [0037], [0230]: efficiency scoring is preferably done on a specialty by specialty basis, so cardiologists are evaluated separately from general internists and separately from pediatricians; the overall score 1406 (e.g., the simple overall score 1408 and/or the weighted overall score 1410) and the corresponding overall status (e.g., pass/fail) for each medical care provider).
Regarding Claim 15, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, wherein each evaluation factor of the set of evaluation factors is a standardized score ([0086] For a specific medical condition, each health care provider's service code per episode rate is divided by the corresponding peer group condition-specific service code per episode rate to calculate a score. For example, a dermatologist's benign neoplasm of the skin biopsy rate per episode may be 0.500 services per episode. The peer group benign neoplasm of the skin biopsy rate per episode may be 0.250, resulting in a CPT-4 score for the dermatologist of a 0.500/0.250=2.00).
Regarding Claim 16, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, wherein prioritizing each projected medical parameter comprises: aggregating each projected medical parameter and each evaluation factor ([0188] aggregate the comparisons across marker-condition pairs to determine an overall score for the respective medical care provider); and
ranking each projected medical parameter for the medical provider based on the evaluation factor associated with the projected medical parameter to provide ranked medical parameters ([0203] percentile ranking within a range of actual rates of utilization of the marker-condition pair by medical care providers in the health plan).
Regarding Claim 19, the combination of Cave and Albert teaches the limitations of Claim 16, and Cave further teaches:
The computer-implemented method of claim 16, wherein prioritizing each ranked medical parameter further comprises: assigning a ranked medical parameter a higher priority value ([0012] “pass status”) where the evaluation factor associated with the ranked medical parameter is less than all remaining evaluation factors of the set of evaluation factors ([0012] “in response to the actual rate of utilization not exceeding the target rate of utilization”); and
assigning the ranked medical parameter a lower priority value ([0012] “fail status”) where the evaluation factor associated with the ranked medical parameter is greater than all the remaining evaluation factors of the set of evaluation factors ([0012] “in response to the actual rate of utilization exceeding a target rate of utilization”).
Regarding Claim 20, the combination of Cave and Albert teaches the limitations of Claim 10, and Cave further teaches:
The computer-implemented method of claim 10, wherein each evaluation factor is a value equal to a difference between the projected medical parameter and an average parameter value associated with the projected medical parameter ([0037] to calculate [the provider’s] efficiency score… the present invention uses a statistical measure, such as a Pearson's Correlation (a statistic that associates two variables - in this case it is typically the health care provider's efficiency score from EfficiencyCare system (other statistical tools, models, and distributions are also within the scope of this invention)), to a procedure or service (e.g., CPT-4 or HCPCS code) score)..
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cave et al. (US 2021/0082072) (Hereinafter Cave) in view of Albert (US 2018/0240547), further in view of Ridgeway (US 2019/0287039).
Regarding Claim 17, the combination of Cave and Albert teaches the limitations of Claim 11, and Cave further teaches the following:
The computer-implemented method of claim 11, [wherein the] average parameter value being determined based on averaging each parameter value of a type of the projected medical parameter across a plurality of medical providers (Cave examples 1.1-2.2 (Pg. 14-18) discloses calculating the mean of episode durations for each medical condition and parameter across a plurality of providers).
However, Cave does not disclose the following that is met by Ridgeway:
wherein the least evaluation factor includes a value representing a number of standard deviations from an average parameter value of a projected medical parameter (Ridgeway [0051]: The z-statistic is a measure of how much evidence there is that a particular service provider deviates from their benchmark for an identified outcome. The benchmark is aggregate weighted data for patients including several medical parameters (See Ridgeway [0041] and [0048]). The z-statistic can be calculated for each service provider and for a plurality of identified effects.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method for calculating average parameter values, as taught by Cave, with the number representing a standard deviation from the average parameter value, as taught by Ridgeway, because more detailed or accurate comparisons of service providers than comparisons that use only averages (such as national averages) to compare service providers to each other without accounting for the unique mix of cases, services, patients, or clients of the particular service providers (See Ridgeway para. [0022]).
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Deno et al. (WO2020021973) discusses evaluating a hospital or a doctor by means of evaluation criteria including at least a consultation time and a waiting time per patient, based on content described in electronic medical records collected from an electronic medical record network
Torres (WO 2010/083050) discusses a service provider evaluation and rating system that quantifies the quality of service in the care industry.
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks Pg. 8-13, filed 10/21/2025, with respect to Claims 1-6, 8-17, and 19-20 have been fully considered and are persuasive. Regarding independent claims 1 and 10, each claim as a whole integrates the abstract ideas into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by providing a real time measure of performance of a medical provider. Thus, the claim is eligible because it is not directed to the recited judicial exception (See Example 42, Claim 1). The rejection under 35 U.S.C. 101 of Claims 1-6, 8-17, and 19-20 has been withdrawn.
Applicant’s arguments, see Applicant’s Remarks Pg. 13-14, filed 10/21/2025, with respect to the rejections of claims 1, 2, 4-6, 8-12, 14-16, and 19-20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Albert.
Applicant's arguments with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argues neither Cave nor Albert, alone or in combination, disclose, teach, or suggest the limitations of amended independent claims 1 and 10 and thus, neither Cave nor Albert discloses, teaches, or suggests dependent claims 3 and 13. Examiner Respectfully disagrees. The combination of Cave and Albert discloses systems for identifying and providing actions for providers to complete in real time, and uses several parameters to calculate the metrics for rating the providers’ performance.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
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/A.K.V./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681