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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/22/2025 has been entered.
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 .
Disposition of Claims
Claims 1-20 are pending in the instant application. No claims have been cancelled. No claims have been added. Claims 1 and 10 have been amended herein. The rejection of the pending claims is hereby made non-final.
Response to Arguments
Applicant’s assertions pertaining to the rejection of the newly amended claims under 35 USC 103 in view of the previously applied prior art has been addressed in the new grounds of rejection presented below.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shannon et al (US 2020/0372986) in view of Louie et al (US 2017/0076063), further in view of Jacobs et al (US 2019/0006037).
Regarding claim 1, the prior art discloses a system for predicting pharmacy order failures, the system comprising:
a pharmacy fulfilment device associated with pharmacy order fulfilment of a high- volume pharmacy, wherein the pharmacy fulfilment device: receives, from an order processing device, instructions to fulfill a pharmacy order; identifies a movable pallet corresponding to a prescription contained of the pharmacy order; controls a loading device to load the prescription container into the pallet; and controls at least one dispensing device to fulfill the pharmacy order into the prescription container on the pallet; one or more sensors associated with pharmacy order fulfilment of the high-volume pharmacy that senses data associated with the pharmacy order; and
a processor; in communication with a memory that includes instructions that, when executed by the processor (see at least paragraph [0045] to Shannon et al “ the predictive analytics device 145 may be housed on a server, e.g., with dedicated, specific processors and memory”), cause the processor to: the predictive model being configured to predict one or more failures in a pharmacy order associated with the at least one pharmacy order package wherein the predictive model is initially trained using at least historical pharmacy order package (see at least paragraph [0047] to Shannon et al The prediction device 145 can obtain the historical data 201 associated with a selection of a patient population in the plan. This selection can be the entire population of members in the plan, a targeted population of the plan (e.g., a user-selected subset of the members in the plan), or a random or pseudorandom subset of members in the plan. In another example embodiment, the historical data 201, when used to train the model, can include medical/pharmaceutical data for persons who are patients of a pharmacy but are not members pharmacy in the pharmacy benefit plan being provided by the PBM, or that is subject to the calculation to determine the prescription/medical event(s). For example, these patients may obtain drug directly from the pharmacy, through a private label service offered by the pharmacy, the high-volume fulfillment center, or otherwise with the prescription drug data being stored in the present system and can be used to train a model. Thus, the models for predicting drug events may be determined using actual data, e.g., the historical data, or actual data combined with some virtual data. The virtual data would represent data, e.g., prescription drug event data and member data from virtual members of the prescription drug plan. In an example, embodiment, the system would create avatars representing statistically possible people and their prescription drug data for the historical data 201 to train a model”); and in response to receiving at least one prediction output from the predictive model , perform at least one corrective action, wherein the at least one failure prediction corresponds to a contradiction between an expected value indicated by the updated data associated with the at least one pharmacy order package and a predicted actual value indicated by the updated data the at least one pharmacy order package (see at least paragraph [0133] to Shannon et al “visualization parts of prescription drug plan data processed using the system and method disclosed herein. Various visualization features 910, 920, and 930 may be displayed and manipulated by a user to extract information and make informed business decisions based on forecasted data produced according to the present processes and systems. These visualizations are flexible enough to enable a user to assess information at the level of each plan member while being robust enough to provide the information needed to evaluate the prescription drug plan as a whole. As can be seen in FIG. 9, any number of features may be added and customized to provide for visualization of that data which is of interest, e.g., by drafting queries to the data, processing the data for visual display, and transmitting the processed data for display on an electronic device, e.g., a mobile display”).
Shannon et al does not appear to explicitly disclose: receiv[ing], in real-time, sensor data from the one or more sensors; Identify[ing] at least one pharmacy order package associated with the pharmacy order using the sensor data; updat[ing], in a database, data associated with the at least one pharmacy order package based on at least some of the sensor data; and indicating at least one failure prediction in the pharmacy order associated with the at least one pharmacy order package; and a pharmacy fulfilment device associated with pharmacy order fulfilment of a high- volume pharmacy, wherein the pharmacy fulfilment device: receives, from an order processing device, instructions to fulfill a pharmacy order; identifies a movable pallet corresponding to a prescription contained of the pharmacy order; controls a loading device to load the prescription container into the pallet; and controls at least one dispensing device to fulfill the pharmacy order into the prescription container on the pallet; one or more sensors associated with pharmacy order fulfilment of the high-volume pharmacy that senses data associated with the pharmacy order.
However, Louie et al discloses systems and methods for predictive data analytics, further comprising:
receiv[ing], in real-time, sensor data from one or more sensors (see at least paragraph [0035]to Louie et al wherein the automated filling machine 30 may include standard filling verification systems such as weigh verification); Identify[ing] at least one pharmacy order package associated with the sensor data (see at least paragraph [0044] to Louie et al wherein the supply container can include a first machine readable tag 50a that is readable by a tag reader 52 in communication with a computer system 22. A second machine readable tag 50b can be operably secured to the individual prescription order 14.); updat[ing], in a database, data associated with the at least one pharmacy order package based on at least some of the sensor data (see at least paragraphs [0048] to Louie et al wherein the system consults the database associated with that customer/patient and determines the number of pills prescribed and deducts that amount from the selected supply container and adds them to the filled individual prescription order); and indicating at least one failure prediction in the pharmacy order associated with the at least one pharmacy order package (see at least paragraph [0064] to Louie et al wherein the system can be used to ensure the proper dispensing of medications, minimize required storage space, quickly identify status changed medications within the system, and identify fraudulent, excessive or otherwise improper dispensing of medications), further comprising a pharmacy fulfilment device associated with pharmacy order fulfilment of a high- volume pharmacy, wherein the pharmacy fulfilment device: receives, from an order processing device, instructions to fulfill a pharmacy order (see at least paragraph [0029] to Louie et al, wherein an automated filling machine or system 30 is provided and used to fill the prescription orders 14. The automated filling system 30 is located at the central fill facility 12 in the embodiment of FIG. 1, and at the local pharmacy 10 or healthcare facility in the embodiment in FIG. 2); identifies a movable pallet corresponding to a prescription contained of the pharmacy order (see at least paragraph [0030] to Louie et al, wherein the supply bins 32 are sealed and tagged at the remote location 34, preferably with an electronic tag 50 such as a barcode, RF tag, RFID tag, GPS tag, or the like, that travels with each supply bin 32. The tag 50 includes identifying information about the medication contained within the supply bin 32 to which it is attached); controls a loading device to load the prescription container into the pallet (see at least paragraph [0032] to Louie et al, wherein the supply bins 32 are sealed and tagged at the remote location 34, preferably with an electronic tag 50 such as a barcode, RF tag, RFID tag, GPS tag, or the like, that travels with each supply bin 32. The tag 50 includes identifying information about the medication contained within the supply bin 32 to which it is attached); and controls at least one dispensing device to fulfill the pharmacy order into the prescription container on the pallet (see at least paragraph [0035] to Louie et al, wherein the automated filling machine 30 may include standard automated filling verification systems 60 such as weight verification, label verification, pill count verification, video comparison of the pills to an image of the pill in a standard catalog of pills, and the like. These verification systems 60 verify that the automated filling machine 30 properly placed the correct medication and the correct amount of that medication into a container that has been properly labeled for a particular customer or patient; one or more sensors associated with pharmacy order fulfilment of the high-volume pharmacy that senses data associated with the pharmacy order (see at least paragraph [0035] to Louie et al, wherein the automated filling machine 30 may include standard automated filling verification systems 60 such as weight verification, label verification, pill count verification, video comparison of the pills to an image of the pill in a standard catalog of pills, and the like).
Shannon et al and Louis et al does not appear to explicitly disclose provid[ing], to a predictive model, the updated data, including at least some of the sensor data, associated with the at least one pharmacy order package, and wherein the at least one of the one or more sensors sensing operation of the at least one automated dispensing device.
However, Jacobs et al discloses an inventory assurance system and method, further comprising provid[ing], to a predictive model, the updated data, including at least some of the sensor data, associated with the at least one pharmacy order package, and wherein the at least one of the one or more sensors sensing operation of the at least one automated dispensing device (see at least paragraph [0029] to Jacobs et al, wherein a prescription may be tracked as the prescription is being filled and stored in a container 120. For example, a user may place a tracking device 130 onto a container 120 and transmit a prescription identifier to the tracking device 130 such that the tracking device 130 and container 120 are associated with a specific prescription… Once the prescription is verified, the user may place the container 120 with the filled prescription in a filled prescription holding area. In some embodiments, the prescription management system 100 may record that the filled prescription has been verified. In some embodiments, the container 120 and/or the tracking device 130 may include a sensor for detecting a correct formulation or preparation of the medication. In these embodiments, the container 120 and/or the tracking device 130 may be configured to transmit an alert to the prescription management system 110 to alert a user if a formulation and/or preparation of a medication is incorrect and [0038] to Jacobs et al, wherein , a remedial action may need to occur, which is discussed in greater detail with regards to FIG. 9. In some embodiments, if the prescription management system 110 detects an event associated with a container 120).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the system and method for predictive data analytics, as disclosed by Shannon et al and the pharmacy security system and method as taught by Louie et al, further in view of the inventory assurance system and method, as disclosed by Jacobs et al, in order to enable the system to automatically determine supply issues and shortages and implement actions to remediate said potential issues, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the system of claim 1, wherein the one or more sensors includes at least a weight sensor (see at least paragraph [0035]to Louie et al, wherein the automated filling machine 30 may include standard automated filling verification systems 60 such as weight verification) The examiner submits that the disclosure of the aforementioned references does not disclose anything that would preclude the usage of a sensor to collect weight data. As such, the combination of the applied prior art and the inclusion of a sensor to determine the weight of an object, is determined to be obvious to try.
Regarding claim 3, the prior art discloses the system of claim 2, wherein the expected value corresponds to an expected weight of at least one component of the at least one pharmacy order package and the predicted actual value corresponds to a predicted actual weight generated by the predictive model based on data associated with one or more other pharmacy order packages (see at least paragraph [0035]to Louie et al, wherein the automated filling machine 30 may include standard automated filling verification systems 60 such as weight verification) The examiner submits that the disclosure of the aforementioned references does not disclose anything that would preclude the usage of a sensor to collect weight data. As such, the combination of the applied prior art and the inclusion of a sensor to determine the weight of an object, is determined to be obvious to try.
Regarding claim 4, the prior art discloses the system of claim 1, wherein the instructions further causing the processor to perform the act least one corrective action by: determining whether the predicted actual value indicates a change in the expected value; and in response to a determination that the predicted actual value indicates a change in the expected value, updating, based on the predicted actual value, the expected value associated with the at least one pharmacy order package and one or more other pharmacy order packages having an expected value corresponding to the expected value of the at least one pharmacy order package (see at least paragraph [0139] to Shannon et al “Scenarios that may be simulated include changes to a formulary, changes to which prescription or generic drugs are offered, changes to the purchasing channels of certain prescription drugs, and any other conditions that can affect the drug spend of a prescription drug plan”).
Regarding claim 5, the prior art discloses the system of claim 4, wherein the instructions further causing the processor to perform the act least one corrective action further by: in response to a determination that the predicted actual value does not indicate a change in the expected value, generating, for display, an output indicating the at least one failure prediction; and providing, at a display, the output indicating the at least one failure prediction (see at least paragraph [0139] to Shannon et al).
Regarding claim 6, the prior art discloses the system of claim 5, wherein the instructions further causing the processor to perform the act least one corrective action further by, in response to the determination that the predicted actual value does not indicate a change in the expected value, preemptively changing one or more aspects of the at least one pharmacy order package responsive to the predicted actual value (see at least paragraphs [0138] and [0139] to Shannon et al).
Regarding claim 7, the prior art discloses the system of claim 1, wherein the predictive model includes at least one machine learning model executed by an artificial intelligence engine that includes nodes to process the sensor data(see at least paragraph [0146] to Shannon et al “computer system 1100 within which a set of instructions may be executed causing the machine to perform any one or more than one methods, processes, operations, or methodologies discussed herein. For example, the system 1100 may develop the model and run iterations of the model to determine effects of enrollment, de-enrollment and drug usage probabilities in the drug plan for a time period”).
Regarding claim 8, the prior art discloses the system of claim 7, wherein the at least one machine learning model is initially trained using at least the historical pharmacy order package data (see at least paragraph [0062] to Shannon et al “Additionally, the imposed correlation 217 determines how likely a second and/or additional medications are to be prescribed to a member having designated demographic data responsive to that same member being prescribed the first medication. This likelihood can be empirically determined from the historical data 201”) wherein the historical pharmacy order package data includes at least pharmacological pill image data (see at least paragraph [0035] to Louie et al, wherein the automated filling machine 30 may include standard automated filling verification systems 60 such as weight verification, label verification, pill count verification, video comparison of the pills to an image of the pill in a standard catalog of pills, and the like).
Regarding claim 9, the prior art discloses the system of claim 8, wherein the at least one machine learning model is subsequently trained using at least output generated by the at least one machine learning model (see at least paragraph [0052] to Shannon et al “The logistic regression models represent a machine learning technique that is executed by the machine to compute the model. Other machine learning can be used in place of logistic regression, e.g., neural networks, decision trees, support vector boosts, gradient boosts, or the like”).
Claims 10-20 each contain recitations substantially similar to those addressed above and, therefore, are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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/TALIA F CRAWLEY/ Primary Examiner, Art Unit 3627