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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 4/1/2026, Applicant responded on 6/24/2026. Amended claim 1-3, 5-9. Added claims 16-19.
Claims 1-19 are pending in this application and claim 1-10, 16-19 have been examined, claim 11-15 were withdrawn from consideration with respect to Applicant’s election from Examiner’s restriction requirement.
Response to Amendment
Applicant's amendments to claims 1-3, 5-9 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Claim 1 as amended herein recites curating scheduling data from a distributed set of heterogeneous data stores; applying that curated data, together with scheduling availability and capacity determinations, as structured inputs to a trained machine learning model; predicting, using the trained machine learning model, probabilities of rescheduling events; and outputting, via a display module, an interactive scheduling interface that renders scheduling content and actuating controls for augmenting scheduling operations. No human mind can perform these operations. A human cannot curate and normalize scheduling data in real time from multiple asynchronous, distributed data stores with distinct schemas; execute a trained machine learning model to generate probabilistic outputs across a personalized medicine supply chain; or dynamically render an interactive interface with actuating controls tied to those outputs. Examiner's mental process analysis strips the claim of its concrete technical limitations and applies an impermissibly high level of abstraction. Accordingly, claim 1 is not directed to a judicial exception.…claim 1 as amended herein are not directed to a mental process. Even assuming, arguendo, that portions of the claim are characterized as reciting an abstract idea, claim 1 as amended herein integrates any such concept into a practical application that improves the functioning of a computer-based scheduling platform and the operation of individualized medicine supply chain networks. As stated in paragraph [0009] of the as-filed specification, existing scheduling technologies in the personalized medicine field are inflexible, relying on "pre-configured, hard- coded, and inaccurate assumptions" and "manual processes that result in faulty schedules, ... scheduling conflicts, errors, inefficient scheduling, forgone manufacturing capacity, inefficient labor and resource allocation, and delay in and inability to respond to sudden schedule changes..." Claims as amended herein provide a concrete technical solution to this documented problem. Furthermore, the machine learning limitations of claim 1 as amended herein are not generic. The machine learning model is trained on historical values of input feature comprising patient-specific features, manufacturing features, courier and logistics features, scheduling and capacity features, and/or sensor features. The trained machine learning model is configured to predict probabilities of enumerated rescheduling events, including patient rescheduling, addition of patients to a waitlist, time durations for material collection and transport, time to transport a therapeutic product to an infusion site, and batching of multiple orders for pickup. The "outputting" limitation is likewise not generic. The interactive scheduling interface does not merely display results. It renders actuating controls that enable real-time augmentation of scheduling operations. This is an action-enabling interface that transforms machine learned predictions into executable scheduling modifications, not passive data output. Taken together, the elements of claim 1 integrate any alleged exception into a practical application by specifically improving the functioning of distributed scheduling technology in the personalized medicine supply chain. Accordingly, claim 1 as amended herein satisfies Step 2A Prong 2…Claim 1 as amended herein produces a significant improvement in the operation of individualized medicine scheduling. The system uses a trained machine learning to predict rescheduling events across the heterogeneous supply chain systems, transforms those predictions into an interactive interface with actuating controls, records scheduling/pending reservations and capacity registry data as immutable data records via a blockchain network, and enables real-time modification of scheduling operations. This is not a mere abstract result. This is a significant technical improvement that enables personalized medicine supply chains to operate more reliably, efficiently, and safely. This technical improvement makes the claimed elements sufficient to amount to significantly more than the alleged judicial exception. Moreover, the claimed system comprises multi-layered architecture that is not conventional, nor is it routine and generic. The ordered combination of elements in the claimed system yields capabilities that conventional computers cannot achieve. The ordered combination of elements in the claimed system comprises (1) collecting capacity datasets from multiple supply chain system, each having distinct schemas and parameters; (2) structuring and transforming the data into input suitable for a trained machine learning model; (3) computing probabilities of rescheduling events occurring across a personalized medicine supply chain; (4) recording scheduling/pending reservations on a blockchain network as immutable cryptographically linked records; and (5) outputting an interactive scheduling interface with actuating controls. This ordered combination is not conventional, routine, and generic. No prior art has ever combined the elements of claim 1 as amended herein. If no prior art teaches this ordered combination, Examiner cannot simultaneously characterize it as well-understood, routine, and conventional under Step 2B. Furthermore, recording, via a blockchain network, scheduling reservations and capacity registry data as immutable records on cryptographically linked blocks is categorically not conventional electronic record-keeping or generic data storage. A conventional database record can be modified, deleted, or corrupted. But, an immutable record on a cryptographically linked blockchain block cannot be modified, deleted, or corrupted. This is a technically specific, non- conventional mechanism for ensuring the integrity and provenance of scheduling reservation data across a distributed supply chain network. No human administrator with pen and paper, and no generic database performing routine record-keeping functions, can replicate this capability. Thus, the claimed system as a whole is a non-conventional and non-generic ordered combination of elements that achieves a significant technical solution that conventional computers cannot perform. This is significantly more than the alleged judicial exception as required under Step 2B….” The Examiner respectfully disagrees.
Applicant admits, “…scheduling technologies in the personalized medicine field are inflexible, relying on "pre-configured, hard- coded, and inaccurate assumptions" and "manual processes that result in faulty schedules, ... scheduling conflicts, errors, inefficient scheduling, forgone manufacturing capacity, inefficient labor and resource allocation, and delay in and inability to respond to sudden schedule changes…”, that scheduling…in the personalized medicine field…, is indeed a manual process. Thus, Applicant admits the claims recite and direct to a mental process, and certain methods of organizing human activities.
By Applicant’s own admission, the claims and the argued elements, are directed to, …scheduling… curate and normalize scheduling data…to facilitate a stakeholder activity (i.e. human activity) in connection with the personalized medicine supply chain....(e.g., automated courier scheduling (i.e. human activity), raw material delivery (i.e. human activity), kit tracking capabilities (i.e. human activity), raw and final product preparation (i.e. human activity), streamlining ordering and scheduling (i.e. human activity), capacity and resource management (i.e. human activity), and several other activities)…to facilitate stakeholder activity (e.g., rescheduling) (i.e. human activity)…predict probabilities (i.e. mathematical mental process) of enumerated rescheduling events, including patient rescheduling (i.e. human activity), addition of patients to a waitlist (i.e. human activity), time durations for material collection and transport (i.e. human activity), time to transport a therapeutic product to an infusion site (i.e. human activity), and batching of multiple orders for pickup (i.e. human activity)…enables personalized medicine supply chains to operate more reliably, efficiently, and safely (i.e. human activity)…computing probabilities (i.e. mathematical mental process) of rescheduling events (i.e. human activity) occurring across a personalized medicine supply chain…recording scheduling/pending reservations (i.e. human activity)…, which is a problem directed to organizing human activity (i.e. commercial interaction and organizing human behaviors of scheduling humans to manufacturing and deliver medication for human patients based on human manufacturing capacity and human patient scheduling, are managing relationship between human courier, human stakeholder, human patient, human stakeholder to enables personalized medicine supply chains to operate more reliably, efficiently, and safely) and a mental process (i.e. human predicting manufacturing schedules and human patient schedules to deliver medication based on mathematical probabilities to enables personalized medicine supply chains to operate more reliably, efficiently, and safely), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. The alleged solutions are solutions directed to solving abstract ideas, which are still abstract ideas. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer, machine learning, neural network, blockchain, scheduling interface. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, machine learning, neural network, generic blockchain, generic scheduling interface, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and generally link) or amount to significantly more under Step 2B (apply it and wurc).
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process and organizing human activities, applied with generic computing components, i.e. computer, machine learning, neural network, blockchain, scheduling interface, recited at a high level of generality and generally linked to a technical environment performing extra solution activities. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Further, “the transformation is extra-solution activity or a field-of-use (i.e., the extent to which (or how) the transformation imposes meaningful limits on the execution of the claimed method steps). A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application).” MPEP 2106.05(c). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and certain methods of organizing human activities implemented using or applying generic computer components.
Additionally, [u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). MPEP 2106.05(f).
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
vii. Selecting one type of content (e.g., FM radio content) from within a range of existing broadcast content types, or selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of well-known, routine, conventional functions performed by the hardware, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016). See 2106.05(a).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
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 1-10, 16-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 recites, “…to perform operations for optimizing delivery of therapeutics to a subject, the operations comprising:
(a) determining a scheduling availability and a capacity for executing scheduled tasks among a set of sites comprising at least one of a patient database, a collection site, a courier data store, or an infusion site, wherein the scheduling availability and the capacity are based on scheduling data curated from a distributed set of data stores corresponding to heterogeneous supply chain …,
wherein curating the scheduling data comprises normalizing data from the heterogeneous supply chain … into a unified structured input for a trained … model,
wherein the trained … model is trained on historical values of input features, and
wherein the input features comprise patient-specific features, manufacturing features, courier and logistics features, scheduling and capacity features, and/or and sensor features;
(b) applying the scheduling data, the scheduling availability, and the capacity as inputs to the trained … model;
(c) predicting, using the trained … model, a probability of a rescheduling event occurring based on events corresponding to the scheduling availability and the capacity for executing the scheduled tasks;
(d) recording, via a …, scheduling reservations, pending reservations, and/or capacity registry data corresponding to the distributed set of data stores as immutable data records stored on …; and
(e) outputting, via a …, an … scheduling … to render content associated with the operations …;
wherein the trained … model is a … trained to predict probabilities of rescheduling events comprising at least one of a patient rescheduling, an addition of one or more patients to a scheduling waitlist, a first time duration for collection of a material, a second time duration to transport collected material to a manufacturing site, a third time duration to transport a therapeutic product to an infusion site, or a batching of multiple orders for pickup.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …to perform operations for optimizing delivery of therapeutics to a subject, the operations comprising: (a) determining a scheduling availability and a capacity for executing scheduled tasks among a set of sites comprising at least one of a patient database, a collection site, a courier data store, or an infusion site, wherein the scheduling availability and the capacity are based on scheduling data curated from a distributed set of data stores corresponding to heterogeneous supply chain …, wherein curating the scheduling data comprises normalizing data from the heterogeneous supply chain … into a unified structured input for a trained … model, wherein the trained … model is trained on historical values of input features, and wherein the input features comprise patient-specific features, manufacturing features, courier and logistics features, scheduling and capacity features, and/or and sensor features; (b) applying the scheduling data, the scheduling availability, and the capacity as inputs to the trained … model; (c) predicting, using the trained … model, a probability of a rescheduling event occurring based on events corresponding to the scheduling availability and the capacity for executing the scheduled tasks; (d) recording, via a …, scheduling reservations, pending reservations, and/or capacity registry data corresponding to the distributed set of data stores as immutable data records stored on …; and (e) outputting, via a …, an … scheduling … to render content associated with the operations …; wherein the trained … model is a … trained to predict probabilities of rescheduling events comprising at least one of a patient rescheduling, an addition of one or more patients to a scheduling waitlist, a first time duration for collection of a material, a second time duration to transport collected material to a manufacturing site, a third time duration to transport a therapeutic product to an infusion site, or a batching of multiple orders for pickup..…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations above. Therefore, the claims are directed to a mental process.
Further, …to perform operations for optimizing delivery of therapeutics to a subject, the operations comprising: (a) determining a scheduling availability and a capacity for executing scheduled tasks among a set of sites comprising at least one of a patient database, a collection site, a courier data store, or an infusion site, wherein the scheduling availability and the capacity are based on scheduling data curated from a distributed set of data stores corresponding to heterogeneous supply chain …, wherein curating the scheduling data comprises normalizing data from the heterogeneous supply chain … into a unified structured input for a trained … model, wherein the trained … model is trained on historical values of input features, and wherein the input features comprise patient-specific features, manufacturing features, courier and logistics features, scheduling and capacity features, and/or and sensor features; (b) applying the scheduling data, the scheduling availability, and the capacity as inputs to the trained … model; (c) predicting, using the trained … model, a probability of a rescheduling event occurring based on events corresponding to the scheduling availability and the capacity for executing the scheduled tasks; (d) recording, via a …, scheduling reservations, pending reservations, and/or capacity registry data corresponding to the distributed set of data stores as immutable data records stored on …; and (e) outputting, via a …, an … scheduling … to render content associated with the operations …; wherein the trained … model is a … trained to predict probabilities of rescheduling events comprising at least one of a patient rescheduling, an addition of one or more patients to a scheduling waitlist, a first time duration for collection of a material, a second time duration to transport collected material to a manufacturing site, a third time duration to transport a therapeutic product to an infusion site, or a batching of multiple orders for pickup…, under the broadest reasonable interpretation, are scheduling humans to manufacturing and delivering medication for human patients based on human manufacturing capacity and human patient scheduling, therefore it is, commercial interactions and managing interactions between people. Thus, the claims are directed to certain methods of organizing human activity.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1: A system comprising: (A) one or more processors; and (B) one or more storage devices comprising processor executable instructions that, responsive to execution by the one or more processors, cause the system, systems, machine learning, blockchain network, cryptographically linked blocks, display module, interactive scheduling interface, interactive scheduling interface effective to render content associated with the operations and actuating controls associated with augmentation of the operations, neural network
Claim 3, 8: cache
Claim 4, 5: collection site system, an infusion site system, a manufacturing site system, or a courier system
Claim 16: abstraction layer
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “determining…”, ”…inputs…” “outputting…render…”, “…aggregate…”, “…reconciles…”, “…consumed…”, “…generating…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – determining…”, ”…inputs…”, “…aggregate…”, “…reconciles…”, “…consumed…”, data output – “outputting…render…”, “…generating…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0017] FIG. 3C illustrates an example, non-limiting diagram of an interactive scheduling interface 300C rendered by distributed scheduling system at a client device in association with a personalized medicine supply chain in accordance with one or more implementations described herein.
[0068] In an aspect, the term module is used to denote any combination of software, hardware and/or firmware that can be configured to provide the corresponding functionality such that individualized medicine platform module 106 and client individualized medicine module 180 can be implemented using any of these combinations. In various implementations, individualized medicine platform module 106 can correspond to a client application that renders a user interface (e.g., using display module 190) on a corresponding display device of computing device 104, and communicates over a network to a server application, such as individualized medicine platform module 106. Alternatively, or additionally, client individualized medicine module 180 can represent a stand-alone application that includes the functionality of individualized medicine platform module 106 onto a same device. In one or more implementations, server(s) device 102 can represent one or more server that distribute various aspects of the individualized medicine platform module 106 across the multiple devices and/or provide cloud-based services to multiple user devices.
[0072] In a non-limiting embodiment, individualized medicine platform module 106 can comprise supply chain optimization module 110-2, commercial scale module 120-2, custody & identification module 130-2, system integrations module 140-2, and analytics module 150-2 to work in concert to provide medicine supply chain monitoring, tracking, scheduling, ordering, analytics, querying, and other interactive features. Some combinations of these modules communicate with one another to exchange information, such as by defining data structures according to a set of rules to provide a mechanism for cross-entity data sharing, as well as predictable and repeatable processing by different entities to achieve expected results. For example, the set of rules can outline the type of information the supply chain data included in the data structure describes, an amount of supply chain data stored within the data structure, a format in which the supply chain data is stored within the data structure, and other such information types.
[0073] By following these rules, a first entity can create and store a data structure such that a second entity can successfully access and interpret the data included in the data structure. A data structure can include any suitable type of structure in which data can be stored, defined, and/or retrieved, such as an object, a vector, a table, a tree, a graph, a queue, a linked-list, a record, a union, a set, a string, a list, an array, a container, a matrix, a function parameter, a heap, and other such structures. In an aspect, server device 102 can include an ordering module 110-2 that acquires, processes and curate's data associated with ordering activities in a personalized medicine supply chain. For example, the supply chain can include the acquisition and generation of order data associated with ordering a courier to pick up or drop off a collection sample. In another aspect, server device 102 can include an ordering module 110-2 that acquires, process and curate's data associated with ordering activities in a personalized medicine supply chain. For example, the supply chain can include the acquisition and generation of order data associated with ordering a courier to pick up or drop off a collection sample.
[0074] In an aspect, a patient's blood sample with cells may be collected at a center of excellence (e.g., hospital) and transported to a manufacturing facility to manufacture the personalized therapeutic medicine and the personalized therapeutic medicine must again be transported back to a COE for infusion into a patient. In an aspect, supply chain optimization module 110-2 can acquire order data representing a wide range of ordering information such as shipment destination, shipment origin, storage conditions during transport, required temperature conditions during transport, handling instructions, instructions to place an order with a particular carrier, type of transportation mode (e.g., ground transport, air transport, etc.), sample information (e.g., quantity, type of sample, etc.).
[0075] Furthermore, in an aspect, custody & identification module 130-2 can acquire, generate, curate, store, and query chain of identity information (e.g., patient identification information corresponding to a sample, patient identification information corresponding to a personalized medicine, medicine specifications, courier identification information, manufacturer identification information, storage facility identification information, supplier identification information of raw material, etc.), chain of custody information (e.g., temperature data, tilt data of package, tracking a sample or medicine custodian at a given point in supply chain, etc.), and other such ordering information. In an aspect, this non-limiting example of an ordering feature of individualized medicine platform module 106 illustrates how any suitable type of order data can be acquired, generated and/or stored by ordering module 110-2 and how modules can perform operations in combination with other modules. In an aspect, server(s) device 102 can include database device(s) 160 to represent any suitable source of data and/or information. Alternatively, or additionally, database device(s) 160 can represent storage for data generated by the medicine supply chain module 106.
[0089] For example, distributed scheduling engine module 110-1 can execute a set of rules that determine which entity can generate and store a data structure such that other entities can successfully access and interpret the data included in such data structure. Furthermore, distributed scheduling engine module 110-1 can provide for data structures that limit the amount of data stored within such structure and the format in which such data must take. In an aspect, a data structure can include any suitable type of structure in which data can be stored (e.g., array, string, file, bitmap, object, etc.). Accordingly, distributed scheduling engine module 110-1 can be configured as an abstraction layer that aggregates, curates, and analyzes capacity data and scheduling data sourced from a distributed array of databases and/or data stores. For instance, distributed scheduling engine module 110-1 can access and source scheduling and capacity data from one or more patient databases, collection site and/or infusion site databases 122, courier databases 124, and manufacturing site databases 126.
[0098] In another implementation, distributed scheduling system 103 can employ prediction engine module 150-1 to execute one or more prediction model to estimate a likelihood of rescheduling events to occur in relation to patient activities (e.g., blood draws, etc.), time duration for various activities (e.g., material collection, material transportation, other transportation lags, etc.), manufacturing activities (e.g., manufacturing system integration, final product assembly, etc.), location prediction of items (e.g., location of materials, samples, products, location within manufacturing process, etc.), prediction of time duration to batch multiple orders, and other such predictions. Furthermore, prediction engine module 150-1 can predict delays across the individualized medicine therapeutic supply chain to optimize capacity (e.g., using capacity engine module 130-1) and scheduling operations (e.g., using distributed scheduling engine module 140-1).
[0099] Turning now to FIG. 1B, illustrated is an example environment 100B in accordance with one or more implementations. In various implementations, the example described with respect to FIG. 1B can be considered a continuation of the example described in FIG. lA. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. FIG. 1B illustrates an example, non-limiting block diagram of a representative environment 100B in which a distributed scheduling system 103 associated with an individualized medicine platform module 106 and a blockchain system 132 can be utilized in accordance with one or more implementations described herein.
[00100] In an aspect, environment 100B can include or otherwise be associated with one or more server device(s) 102 that can execute individualized medicine platform module 106, smart label module 108, distributed scheduling system 103 and associated modules such as supply chain optimization module 110-2, commercial scale module 120-2, custody & identification module 130-2, system integrations module 140-2, analytics module 150-2, distributed scheduling engine module 110-1, rescheduling engine module 120-1, capacity engine module 130-1, load balancing engine module 140-1, prediction engine module 150-1 and first communication module 170. Furthermore, server device(s) 102 can comprise database(s) 163. Environment 100B can also include computing device(s) 104 that employs client individualized medicine module, client smart label module 194, client distributed scheduling module 195, input module 192, display module 190, and second communication module 196. Environment 100B can also include network component 114, smart label device(s) 198, collection site and/or infusion site databases 122, courier databases 124, and manufacturing site databases 126. Furthermore, in an aspect, Environment 100B can include blockchain 132 and blockchain node(s) 134.
[00101] In an aspect, blockchain 132 can be generated in accordance with one or more implementation disclosed herein. Furthermore, blockchain 132 can comprise blockchain node(s) 134.In a non-limiting embodiment, environment 100B can be configured to communicate and/or integrate with a distributed blockchain system comprising multiple computing nodes, such that each computing node is configured to store a copy or a portion of a blockchain. In some instances, a blockchain 132 may be used to store data associated with distributed scheduling system 103, collection site and/or infusion site databases 122, courier databases 124, manufacturing site databases 126, smart label device 198, individualized medicine platform module 106, smart label module 108 and other such devices in environment 100B. Furthermore, in an aspect, blockchain 132 can be a distributed database that maintains a continuously updated list of data records (e.g., data of manufacturing site/infusion site/collection site/center of excellence site capacities and schedules, scheduling reservations, pending reservations, predictive data, time estimates, list of smart label device locations, smart label device sensor data, smart label device data user authentication data, geo-fencing location data, specimen condition data, cryogenic storage temperature data, courier data, etc.) where each the updated list of data records can be stored on blocks linked together as a chain. In an aspect, the blocks can be secure storage vehicles (e.g., encryption based on public-key and private-key pairings) and are immutable (cannot be changed).
[00102] In another aspect, the blockchain 132 can record a transaction (e.g., a transfer of information onto, from or with individualized medicine platform module 106, distributed scheduling system 108, smart label module 108, smart label device(s) 198) within a ledger database of the blockchain 132 that is shared by devices participating in a distributed network of computers. In yet another aspect, the distributed ledger can present a consensual (by all computers within the distributed network) record corresponding to a cryptographic audit trail that is validated and maintained by independent computers. In yet another aspect, blockchain 132 can employ a network of blockchain nodes 134 that can support the blockchain, which may include a set of blocks that store data corresponding to individualized medicine platform module 106 and distributed scheduling system 103.
[00103] In an aspect, data stored on nodes of blockchain 132 can include transactional data (e.g., scheduling data, capacity data, time duration data, scheduling data, predictive data, event data, identifier data, smart label device(s) 198 data, location data, custody data, triggering event data, etc.) acquired, generated, curated, transformed, and/or received by individualized medicine platform module 106. In a non-limiting aspect, the data can be transmitted as duplicate data to a blockchain system employing blockchain 132. In another non-limiting aspect, the data can be transmitted to the blockchain system as original data (non-duplicate data), such that the blockchain system serves as a primary data store for one or more sets of data. In an embodiment, only the additional data in a transaction (not including the previously transmitted duplicate data) can be transmitted for incorporation onto the blockchain.
[00104] As such, the blockchain can be configured to store data corresponding to the data of individualized medicine platform module 106, distributed scheduling system 103, smart label module 108, and/or smart label device(s) 198. In an aspect, blockchain 132 is a data structure that stores a list of transactions. In an aspect, blockchain 132 can be a distributed electronic ledger that records transactions between various stakeholders (e.g., providers, patients, centers of excellence, couriers or third-party logistics providers, manufacturers, suppliers, cryogenic storage facilities, administrative managers, and other such stakeholders) of the individualized medicine platform module 106. In one or more non- limiting embodiments, blockchain 132 can be a decentralized public (or private) transaction ledger that can be deployed over one or more node (e.g., server) and configured to perform transaction-based state transitions and smart contract functionality.
[00105] In a non-limiting implementation, computing device(s) 104 can interact with a server device(s) 102 configured to control one or more node (e.g., blockchain node 134 and other such nodes) of blockchain 132. In an aspect, server device(s) 102 can be configured to facilitate access to one or more node of blockchain 132. For instance, server device(s) 102 can control access to blockchain node 134 that represents an Ethereum blockchain or other distributed ledger node featuring transaction-based state transitions and/or smart contract functionality. In another aspect, a smart contract may be stored as a block with blockchain 132 and data included as components of the smart contract can be stored within separate blocks within the blockchain. Furthermore, in a non-limiting embodiment, each node of a blockchain 320 can store a copy of a smart contract program structure. In an aspect, environment 100B allows for a communication between any individual device(s) in environment 100C and blockchain 132. In other non-limiting embodiments, blockchain communication routes can be limited between select devices.
[00108] In an aspect, processor 230 can comprise one or more processor configured to perform one or more operations (of at least one module of client individualized medicine module 180) using hardware. As such, processor 230 can include hardware elements 240 that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. In an aspect, the hardware elements 240 are not limited by the materials from which they are formed, or the processing mechanisms employed by such materials. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits). In such a context, processor- executable instructions may be electronically executable instructions.
[00109] The computer-readable media 240 is illustrated as including memory storage 250. The memory storage 250 represents memory storage capacity associated with one or more computer- readable media. The memory storage 250 may include volatile media (such as random-access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory storage 250 may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media 240 may be configured in a variety of other ways as further described below. In an aspect, client individualized medicine module 180 of Figure 1 is illustrated as residing within memory storage 250, client smart label module 194 and client distributed scheduling module 195, but alternate or additional implementations can implement client individualized medicine module 180 using combinations of firmware, hardware, and/or software without departing from the scope of the claimed subject matter, such as hardware elements 240.
[00110] Example environment 200 can enable multiple devices to be interconnected through server device(s) 102, where server device(s) 102 can be local to the multiple devices, remote from the multiple devices, or any combination thereof. In one or more implementations, server device(s) 102 can be configured as a cloud of one or more server computers that are connected to the multiple devices through a network (e.g., using network component 114), the Internet, or other data communication link capable of enabling functionality to be delivered across multiple devices (e.g., several smartphone devices, desktops, tablets, etc.) to provide a common and seamless experience to a user of the multiple devices. Each of the multiple devices may have different physical requirements and capabilities, and the central computing device uses a platform to enable the delivery of an experience to the device that is both tailored to the device and yet common to all devices. In a non-limiting embodiment, a class of target devices having unique physical features, types of usage or other such characteristics can be deployed, and tailored user experiences can be implemented on such class of generic class of devices.
[00111] In an aspect, cloud computing network or network component 114 can include or represent an abstraction platform 210 for resources 220. In another aspect, the abstraction platform 212 abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud 210. In an aspect, resources 220 may include applications and/or data that can be utilized while computer processing is executed on servers (e.g., server device(s) 102) that are remote from the computing device(s) 104. For example, resources 220 can include individualized medicine platform module 106, smart label module 108, and distributed scheduling system 103 at FIG. lA. In another aspect, the abstraction platform 210 may abstract resources and functions to connect computing device 104 with other computing devices and may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources 220 that are implemented via the abstraction platform 210. Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system. For example, the functionality may be implemented in part on the computing device 104 as well as via the abstraction platform 210 that abstracts the functionality of the network component 114.
[00112] In another aspect, abstraction platform 212 can allow for external system integrators to keep data private on individualized medicine platform module 106, distributed scheduling system 103, smart label module 108 while abstraction platform 210 can extract learnings from such data to educate a range of machine learning algorithms. For instance, hyper-parameters of machine learning models applied to a first external device data can be extracted and the hyper-parameters can be applied to data of a second external device. As such, learnings from analyzing and curating a first client data can be applied to the analysis and curation of a second client data without ever exposing the private data of each user.
[00116] In another aspect, scheduling engine module 110-1 may source scheduling and capacity data from various sources at different speeds due to a range of factors related to the source systems. In some implementations, for those sources that are slow or in other implementations for all sources of scheduling data and capacity data, capacity module 32A can source data a capacity registry such that caching service time can be reduced. As an example, capacity module 320A can cache capacity data based on the type of storage media used for the cache, cache location within a storage media, data storage density, as well as read and write speed that can be realized in transferring data to and from the cache. As such, scheduling engine module 110-1 can reduce data access times to perform scheduling operations by accessing some or all capacity data from the cache registry and other data directly from the data source (e.g., database, data store, third party system caches, etc.). Given the volume of data analyzed and accessed by scheduling engine module 110-1, the cache registry can be used to read and write large amounts of data at faster speeds.
[00152] In an aspect, the determination model can employ a neural network model to propagate layers of determinations to select optimal sites and provide estimated transit times. In an aspect, a transit time may not merely be proportional to a distance between sites but rather may need to consider other factors specific to the mode of transportation (e.g., air, car, rail, etc.), traffic conditions, weather conditions, requirements of the therapeutic or specimen, and other such considerations. With respect to parameters that predict the fastest manufacturing and transportation timings, such predictions can be based on prediction models applied to historical data to estimate future predictions. In another aspect, other parameters can take into account variance across manufacturing sites such as type of machines, age of equipment, quality control processes, standard operating procedures, and other such variables. Due to the non-standard nature of such variables, such parameters can be incorporated into determination models via tagging operations, rule-based implementations, and other such mechanisms.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-10, 16-19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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