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 non-Final Office Action.
In response to Examiner's communication of 10/27/2025, Applicant responded on 1/27/2026. Amended claim 1-3, and 5-10.
IDS filed on 1/27/2026 is acknowledged and considered by the Examiner.
Claims 1-15 are pending in this application and claim 1-10 have been examined, claim 11-15 were withdrawn from consideration with respect to Applicant’s election from Examiner’s restriction requirement.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/27/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1-3, and 5-10 are not sufficient to overcome the 35 USC 101 set forth in the previous action.
Applicant's amendments to claims 1-3, and 5-10 are sufficient to overcome the prior art 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 a specific, structured computing system including one or more processors and one or more storage devices comprising processor-executable instructions that configure the system to perform concrete, technological operations involving distributed data integration, neural-network computation, and dynamic interface rendering. The claim therefore falls squarely within the statutory category of a "machine" under 35 U.S.C. § 101. Accordingly, claim 1 as amended herein is directed to a particular machine or system configured to carry out advanced scheduling, prediction, and interface-generation operations, not to an abstract idea...The system of claim 1 is expressly configured to curate distributed scheduling and capacity data from multiple supply chain systems; apply those datasets as structured input to a trained machine learning model; generate probabilistic machine learning predictions relating to rescheduling events; and output, via a display module, an interactive scheduling interface that renders scheduling content and actuating controls enabling augmentation of the scheduling operations. A human mind cannot perform these operations. Humans cannot integrate distributed datasets in real-time from multiple asynchronous supply chain systems; execute a trained machine learning model to produce probabilistic outputs; or dynamically generate an interactive, computer-rendered interface that allows actuator-based adjustment of scheduling operations…Claim 1 as amended herein clearly fall within that category. The human mind is not equipped to perform distributed data curation, machine learning prediction, real-time scheduling augmentation, or interface rendering as recited. Accordingly, the claimed system is not directed to a mental process or abstract idea, but rather to a concrete machine or system that performs specific technical functions using specialized computing components. Claim 1 is therefore not directed to a mental process, and is not abstract under Step 2A Prong 1…claim 1 recite specific and structurally defined machine/system comprising processors, storage devices containing processor executable instructions, a trained machine learning model, and a display module configured to output an interactive scheduling interface that includes actuating controls for augmenting scheduling operations. These are not filed-of-use limitations or generic computing elements. The claimed system takes data from multiple supply chain systems, computes rescheduling probabilities employing a trained machine learning model, and renders an interactive interface with actuator controls tied to the outputs. These operations meaningfully limit the claim to a specific technical environment and cannot be reduced to mental process or genetic automation….claim 1 as amended herein applies machine learning predictions to control real scheduling system. The claim now recite outputting an interactive scheduling interface that operationalizes the prediction, enables augmentations to scheduling operations, and embeds control logic directly into a specialized computing environment. The interactive scheduling interface is not extra-solution activity nor is it passive reporting. It is technical mechanism through which the machine learned output is transformed into actionable scheduling modifications, integrating the output within a physical process that changes the state of a tangible system. Accordingly, claim 1 as amended herein satisfies Step 2A Prong 2...Claim 1 as amended herein produces a technical improvement in the operation of individualized medicine scheduling. The system uses a trained machine learning to predict rescheduling events across the supply chain systems, transforms those predictions into an interactive interface with actuating controls, 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; and (4) generating an interactive scheduling interface to transform the machine learned predictions into real-time actionable scheduling modifications. This ordered combination is not conventional, routine, and generic for several reasons. First, conventional computer systems do not aggregate data from multiple heterogeneous, asynchronous supply chain systems, each having distinct schemas and parameters. The claimed system performs specific structuring and transforming these disparate data into a unified machine learning input. This type of multi-source normalization is not a routine computer function and is not performed by generic data collection systems. Second, generating an interactive scheduling interface with actuator-based controls is a non-generic implementation of a graphical user interface. This interface is not a passive display of information but a specialized tool that converts machine learned outputs into real-time executable scheduling modifications within the claimed system. This type of structured, interactive interface is not a generic display function found on conventional computers. Lastly, when considered together, the system's architecture accomplishes a task that would be impossible for generic computers operating in isolation. 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.
Unlike the Aug 2025 Memo, by Applicant’s own admission, the claims and the argued elements, are directed to, …scheduling…to facilitate a stakeholder activity in connection with the personalized medicine supply chain....(e.g., automated courier scheduling, raw material delivery, kit tracking capabilities, raw and final product preparation, streamlining ordering and scheduling, capacity and resource management, and several other activities)…to facilitate stakeholder activity (e.g., rescheduling)…, 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) and a mental process (i.e. human predicting manufacturing schedules and human patient schedules to deliver medication), 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. applying trained neural network with generic computer. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, 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 or amount to significantly more under Step 2B.
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.
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 2106.04(a)(2).
[T]he 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;
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)
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);
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).
[T]he 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;
[T]he 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);
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);
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);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are JP Patent Publication to JP2003196404A to Honchi et al., (hereinafter referred to as “Honchi”) in view of US Patent Publication to US20090112618A1 to Johnson et al., (hereinafter referred to as “Johnson”) in view of US Patent Publication to US20140052761A1 to Teitelbaum, (hereinafter referred to as “Teitelbaum”).
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1,
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 the set of sites;
applying the scheduling data, the scheduling availability, and the capacity as inputs to a trained machine learning model;
predicting, using the trained machine learning model, a probability of a rescheduling event occurring based on events corresponding to the scheduling availability and the capacity for executing the scheduled tasks; and
outputting, via a display module, an interactive scheduling interface effective to render content associated with the scheduling operations and actuating controls associated with augmentation of the scheduling operations;
wherein the trained machine learning model is a neural network 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.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1.
The prior art rejection is hereby withdrawn.
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 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 scheduling operations comprising:
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 the set of sites;
applying the scheduling data, the scheduling availability, and the capacity as inputs to a trained … model;
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; and
outputting, via … effective to render content associated with the scheduling operations and actuating controls associated with augmentation of the scheduling 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, …perform scheduling operations comprising…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 the set of sites; applying the scheduling data, the scheduling availability, and the capacity as inputs to a trained … model; 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; and outputting, via … effective to render content associated with the scheduling operations and actuating controls associated with augmentation of the scheduling 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, …perform scheduling operations comprising…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 the set of sites; applying the scheduling data, the scheduling availability, and the capacity as inputs to a trained … model; 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; and outputting, via … effective to render content associated with the scheduling operations and actuating controls associated with augmentation of the scheduling 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: one or more processors; and one or more storage devices comprising processor executable instructions that, responsive to execution by the one or more processors, cause the system to, machine learning, neural network, a display module, an interactive scheduling interface
Claim 3, 8: cache
Claim 4, 5: collection site system, an infusion site system, a manufacturing site system, or a courier system
, 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…” , 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…”, data output – “outputting…render…”
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.
[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.
[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 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.
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/PO HAN LEE/Primary Examiner, Art Unit 3623