DETAILED ACTION
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 .
Status of the Claims
The office action is in response to the claims filed on March 26, 2026, for the application filed on December 14, 2023, which claims priority to Provisional Application Nos. 63/436,178 filed December 30, 2022, 63/436,192 filed December 30, 2022, 63/436,173 filed December 30, 2022, and 63/436,185 filed December 30, 2022. Claims 1 – 30 are currently pending and have been examined as discussed below.
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 – 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the flowchart in MPEP 2016(III).
Eligibility Step 1:
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within one of the statutory categories of invention (i.e., a process, machine, manufacture, or composition of matter). See MPEP 2106.03. In the instant application, claims 1 – 13 are directed to a method (i.e., a process); claims 14 – 24 are directed to a method (i.e., a process); and claims 25 – 30 are directed to a method (i.e., a process).
While each one of claims 1 – 30 appears to fall within one or more statutory categories of invention, the Office has determined that the full eligibility analysis is required because there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself. The eligibility of each claim is not self-evident at least because each claim as a whole did not appear to clearly improve a technology or computer functionality. To the contrary, each claim as a whole appeared to merely apply one or more judicial exceptions on a computer.
Accordingly, it has been determined that each one of claims 1 – 30 as a whole falls within one or more statutory categories under Step 1, and the Office proceeds with the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) as discussed below.
Eligibility Step 2A, Prong One:
Under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to one or more of the judicial exceptions (i.e., an abstract idea, law of nature, or natural phenomenon). See MPEP 2106.04(II)(A)(1). After evaluation, it has been determined that claims 1 – 30 are directed to judicial exceptions because claims 1 – 30 recite abstract ideas. (The Office will not determine that a claim is not directed to a judicial exception under Step 2A, Prong One for the mere reason that claim further recites one or more additional elements beyond the judicial exception.)
Independent claims 1, 14, and 25 are determined to be directed to a judicial exception including abstract ideas (i.e., mental process).
Regarding claim 1, the abstract idea (i.e., a mental process) is identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient, the method comprising:
storing, by one or more processors of the IMD, firmware for execution by the one or more processors at a memory, wherein the firmware includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple code paths;
a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths; and
a third firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a third code path of the multiple code paths;
receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths;
storing, by the one or more processors, the code path information in the memory;
quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem, the device power management hardware subsystem, or the stimulation therapy pulse generator subsystem.
Regarding claim 14, the abstract idea (i.e., a mental process) is identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient, the method comprising:
training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include:
a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths; and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple code paths;
receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths of the multiple code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof;
updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model;
transmitting, by the one or more processors, the updated trained AI model to the at least one IMD; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metrics is the device power management hardware subsystem or the stimulation therapy pulse generator hardware subsystem.
Regarding claim 25, the abstract idea (i.e., a mental process) is identified in bold as:
A method of controlling operations of implantable medical devices (IMDs) using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs, comprising:
receiving, by one or more servers for IMD management, code path data from a plurality of IMDs after implantation in respective patients, the code path data representing firmware code instructions executed by one or more firmware modules of a plurality of firmware modules corresponding to firmware executable by one or more processors of each of the plurality of IMDs to control corresponding hardware components of the IMDs for respective IMD functions, and wherein multiple identified code paths traversing respective firmware code instructions are identified for each of the plurality of firmware modules, and the plurality of firmware modules of at least one IMD of the plurality of IMDs include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple identified code paths; and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple identified code paths;
training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD;
distributing the trained AI model from one or more servers for IMD management to a set of IMDs;
operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations;
detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD; and
performing, by the one or more processors of the at least one IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem.
Claim 1 recites the combination of limitations identified in bold as “a method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient,” “generating … code path information associated with one or more code paths of the multiple code paths,” and “quantifying … performance of the IMD based on the code path information.” This combination represents the abstract idea of determining performance of an IMD and thus may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Claim 14 recites the combination of limitations identified in bold as “a method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient” and “quantify performance of an IMD.” This combination represents the abstract idea of determining performance of an IMD and thus may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Claim 25 recites the combination of limitations identified in bold as “a method of controlling operations of implantable medical devices (IMDs),” “wherein multiple identified code paths traversing respective firmware code instructions are identified for each of the plurality of firmware modules,” “detecting … a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD,” and “the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem.” This combination represents the abstract idea of determining a response action for controlling an operation of the IMD (i.e., with the response action being based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem) and thus may be practically performed in the human mind using observation, evaluation, judgment, and opinion. With the exception of generic computer-implemented steps, there is nothing in each of claims 1, 14, and 25 themselves that forecloses them from being performed by a human, mentally or with tools such as pen and paper. Thus, the abstract idea in each of claims 1, 14, and 25 falls in the "mental process" grouping.
Accordingly, claims 1, 14, and 25 are directed to judicial exceptions under Step 2A, Prong One.
Dependent claims 2 – 13, 15 – 24, and 26 – 30 are directed to one or more judicial exceptions (i.e., abstract idea exceptions) under Step 2A, Prong One of the full eligibility analysis as follows:
Regarding claims 2 – 13, each combination of limitations identified in bold as “the code paths, the code path metrics, or both included in the training dataset include sets of code paths, sets of code path metrics, or both corresponding to different therapy configurations for the population of patients” in claim 2 , “the training of the AI model is configured to identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules” in claim 3, “the training of the AI model is configured to identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules” in claim 4, “one or more hyperparameters of the AI model are modified during at least one iteration of the training of the AI model” in claim 5, “transmitting, via the wireless communication circuitry, feedback data to an analytics platform, wherein the training dataset is updated based on the feedback data” in claim 6, “the feedback data comprises information associated with deviations from one or more baseline code paths, information indicating one or more observed code paths that were misclassified by the AI model, differences between expected or baseline code paths and observed code paths, information identifying one or more stimulation programs of the IMD, information identifying one or more stimulation parameters, information identifying one or more IMD component states, or a combination thereof, and wherein the training dataset is updated based on the feedback data” in claim 7, “the wireless communication circuitry is configured to support Bluetooth communication” in claim 8, “wherein performance of the IMD is quantified as one or more code metrics generated by the AI model, the method comprising: determining a graduated response based on the one or more code metrics quantifying the performance of the IMD” in claim 9, “the graduated response comprises adjustments to one or more therapy modes of the IMD, adjustments to one or more stimulation settings of a therapy program configured for the IMD, rebooting a particular firmware module of the IMD, updating firmware of the IMD, or a combination thereof” in claim 10, “the graduated response is determined based on a sequence of responses stored in memory” in claim 11, “the firmware code instructions comprise one or more identifiers or markers corresponding to execution of particular firmware code instructions, and wherein the code path information comprises at least one of the one or more identifiers or markers corresponding to the executed one or more code paths of the IMD” in claim 12, and “quantifying the performance of the IMD comprises determining whether the code path information indicates normal operations of the IMD or abnormal operations of the IMD” in claim 13 represents a mental process of determining performance of an IMD including the steps of generating (i.e., determining) code path information associated with one or more code paths of the multiple code paths and quantifying performance of a device based on the code path information. This mental process may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions. See MPEP 2106.04(a)(2)(III). Regarding claims 15 – 24, each combination of limitations identified in bold as “aggregating feedback received from a plurality of IMDs to produce an updated training dataset” in claim 15, “the aggregating comprises determining one or more average values associated with the feedback, one or more maximum values associated with the feedback, one or more minimum values associated with the feedback, one or more mean values associated with the feedback, or a combination thereof” in claim 16, “the AI model is trained over a plurality of iterations until a stop criterion is satisfied, and wherein hyperparameters of the AI model are tuned after at least one iteration of the plurality of iterations” in claim 17, “the stop criterion comprises a threshold performance level of the AI model” in claim 18, “the threshold performance level corresponds to an accuracy of the AI model with respect to quantifying the performance of an IMD” in claim 19, “the IMDs corresponding to the population of patients are configured to provide different therapies to respective patients of the population of patients” in claim 20, “establishing one or more baseline or expected code paths during the training of the AI model” in claim 21, “determining one or more graduated responses for different outputs of the AI model, wherein the one or more graduated responses are updated periodically based on additional training of the AI model” in claim 22, and “the one or more graduated responses comprise adjustments to one or more therapy modes of a particular IMD, adjustments to one or more stimulation settings of a therapy program configured for the particular IMD, rebooting a particular firmware module of the particular IMD, updating firmware of the particular IMD, or a combination thereof” in claim 23 represents a mental process of determining performance of an IMD. This mental process may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions. See MPEP 2106.04(a)(2)(III). Regarding claims 26 – 30, each combination of limitations identified in bold as “training the AI model using the received code path data to generate the model of IMD operations representing proper operation of an IMD comprises preprocessing the code path data prior to training the AI model” in claim 26, “the preprocessing comprises generating code path metrics based on the code path data, wherein the code path metrics are utilized to train the AI model” in claim 27, “preprocessing additional code path data prior to training the AI model” in claim 28, “the set of IMDs is: different from the plurality of IMDs; the same as the plurality of IMDs; or includes at least one IMD included in the plurality of IMDs and at least one IMD not included in the plurality of IMDs” in claim 29, “one or more baseline or expected code paths are learned by the AI model during the training, the one or more baseline or expected code paths corresponding to proper operation of the IMD” in claim 30 represents a mental process of determining a response action for controlling an operation of the IMD (i.e., with the response action being based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem). This mental process may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions. See MPEP 2106.04(a)(2)(III). Thus, claims 2 – 13, 15 – 24, and 26 – 30 recite an abstract idea in the "mental process" grouping, and thus recite a judicial exception under Step 2A, Prong One.
Eligibility Step 2A, Prong Two:
Claims 1, 14, and 25 recite additional limitations beyond the judicial exceptions.
Claim 1 recites the additional limitations identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient, the method comprising:
storing, by one or more processors of the IMD, firmware for execution by the one or more processors at a memory, wherein the firmware includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple code paths;
a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths; and
a third firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a third code path of the multiple code paths;
receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths;
storing, by the one or more processors, the code path information in the memory;
quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem, the device power management hardware subsystem, or the stimulation therapy pulse generator subsystem.
Claim 14 recites the additional limitations identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient, the method comprising:
training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include:
a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths; and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple code paths;
receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths of the multiple code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof;
updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model;
transmitting, by the one or more processors, the updated trained AI model to the at least one IMD; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metrics is the device power management hardware subsystem or the stimulation therapy pulse generator hardware subsystem.
Claim 25 recites the additional limitations identified in bold as:
A method of controlling operations of implantable medical devices (IMDs) using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs, comprising:
receiving, by one or more servers for IMD management, code path data from a plurality of IMDs after implantation in respective patients, the code path data representing firmware code instructions executed by one or more firmware modules of a plurality of firmware modules corresponding to firmware executable by one or more processors of each of the plurality of IMDs to control corresponding hardware components of the IMDs for respective IMD functions, and wherein multiple identified code paths traversing respective firmware code instructions are identified for each of the plurality of firmware modules, and the plurality of firmware modules of at least one IMD of the plurality of IMDs include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple identified code paths; and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple identified code paths;
training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD;
distributing the trained AI model from one or more servers for IMD management to a set of IMDs;
operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations;
detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD; and
performing, by the one or more processors of the at least one IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem.
Claim 1 recites the additional limitations identified in bold as “an implantable medical device (IMD),” “storing, by one or more processors of the IMD, firmware for execution by the one or more processors at a memory, wherein the firmware includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include: a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple code paths; a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths; and a third firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a third code path of the multiple code paths,” “receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients,” “generating, by the one or more processors, code path information associated with one or more code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to one or more code paths of the plurality of code paths,” “storing, by the one or more processors, the code path information in the memory,” “quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model,” and “performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem, the device power management hardware subsystem, or the stimulation therapy pulse generator subsystem.” Claim 14 recites the additional limitations identified in bold as “training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients,” “transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions,” “receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof,” “updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model,” and “transmitting, by the one or more processors, the updated trained AI model to the at least one IMD.” Claim 25 recites the additional limitations identified in bold as “implantable medical devices (IMDs),” “using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs,” “receiving, by one or more servers for IMD management, code path data from a plurality of IMDs after implantation in respective patients, the code path data representing firmware code instructions executed by one or more firmware modules of a plurality of firmware modules corresponding to firmware executable by one or more processors of each of the plurality of IMDs to control corresponding hardware components of the IMDs for respective IMD functions,” “the plurality of firmware modules of at least one IMD of the plurality of IMDs include: a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple identified code paths; and a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple identified code paths,” “training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD,” “distributing the trained AI model from one or more servers for IMD management to a set of IMDs,” “operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations,” “detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD,” and “performing, by the one or more processors of the at least one IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem.”
Regarding the consideration under MPEP 2106.04(d)(2), each one of claims 1, 14, and 25 as a whole does not amount to a particular treatment or prophylaxis. At best, the claims recite the limitation of performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem (claims 1 and 25), the device power management hardware subsystem (claims 1 and 14), or the stimulation therapy pulse generator subsystem (claims 1, 14, and 25). However, each claim as a whole does not positively recite the particular operation of the IMD at any level of detail, and thus not limited to any particular manner or type of treatment under MPEP 2106.04(d)(2).
Regarding the consideration under MPEP 2106.05(a), each claim as a whole does not “purport to improve the functioning of the computer itself" or "any other technology or technical field,” but rather merely provides an improvement in the abstract idea itself (i.e., determining a response action for controlling an operation of the IMD). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. When determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. See MPEP 2106.05(f). Here, there are no details about a particular first firmware module, a particular device communication hardware subsystem, or a particular first code path, or how the first firmware module operates to control the device communication hardware subsystem other than that it is being used to control the device communication hardware subsystem. There are no details about a particular second firmware module, a particular device power management hardware subsystem, or a particular second code path, or how the second firmware module operates to control the device power management hardware subsystem other than that it is being used to control the device power management hardware subsystem. There are no details about a particular third firmware module, a particular stimulation therapy pulse generator hardware subsystem, or a particular third code path, or how the third firmware module operates to control the stimulation therapy pulse generator hardware subsystem other than that it is being used to control the stimulation therapy pulse generator hardware subsystem. There are no details about how the one or more processors generate code path information (i.e., associated with one or more code paths of the multiple code paths executed during operation of the IMD) based on the firmware code instructions and indicating execution of code corresponding to the one or more code paths other than that the one or more processors are being used to generate code path information based on the firmware code instructions and indicating execution of code corresponding to the one or more code paths. There are no details about a particular AI model or how the one or more processors uses the AI model to quantify performance of the IMD based on the code path information other than that the processors are using the AI model to quantify performance of the IMD based on the code path information. There are no details about a particular response action or a particular implantable medical device (IMD), how the processors are being used to perform the response action, or how the response action controls operation of the IMD. This combination of elements (i.e., the first firmware module, the device communication hardware subsystem, the first code path, the second firmware module, the device power management hardware subsystem, the second code path, the third firmware module, the stimulation therapy pulse generator hardware subsystem, the third code path, the AI model, the response action, and the IMD) is being used to generally apply the abstract idea (i.e., determining a response action for controlling an operation of the IMD) without placing any limitation on how the combination of elements operates to determine the response action for controlling operation of the IMD. In addition, the combination of elements recites only the idea of controlling operation of the IMD without details on how this is actually accomplished. Each claim as a whole omits any details as to how the combination of elements solves a technical problem, and instead recites only the idea of a solution or outcome (i.e., determining a response action for controlling an operation of the IMD). Also, the claim invokes generic computer components (e.g., the first firmware module, the device communication hardware subsystem, the second firmware module, the device power management hardware subsystem, the third firmware module, the stimulation therapy pulse generator hardware subsystem, the AI model, the response action, and the IMD) merely as tools for performing the recited function of controlling operation of the IMD rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, each claim as a whole represents no more than mere instructions to apply the judicial exception on a computer. Each claim as a whole can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers.
Regarding the consideration under MPEP 2106.05(g), the steps of storing (i.e., by one or more processors of the IMD) firmware controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, receiving (i.e., by the processors) an artificial intelligence (AI) model from a remote device via wireless communication circuitry, training the AI model based on a training dataset (code paths, code path metrics, or both) associated with IMDs corresponding to a population of patients, and storing (by the processors) the code path information in the memory are determined to not add more than insignificant extra-solution activities to the judicial exception. These extra activities represent the well-known pre-solution activities of necessary input data gathering because they are incidental to the primary process of performing IMD-diagnostic operations and thus are merely nominal or tangential additions to the associated claims.
Accordingly, in view of these considerations, the Office has determined that claims 1, 14, and 25 do not have one or more additional limitations, individually or in combination, that integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Dependent claims 2 – 13, 15 – 24, and 26 – 30 present additional information in tandem with further details regarding elements and the abstract idea from an associated one of independent claims 1, 14, and 25 and are therefore directed to an abstract idea for similar reasons as given Under Step 2A, Prong One above. None of claims 2 – 13, 15 – 24, and 26 – 30 recite additional limitations beyond the abstract idea of performing diagnostic operations.
Accordingly, in view of these considerations, the Office has determined that claims 2 – 13, 15 – 24, and 26 – 30 do not have one or more additional limitations, individually or in combination, that integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Eligibility Step 2B:
Regarding independent claims 1, 14, and 25, the Office carries over its identification of the additional elements (and combinations thereof) from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B.
Under Step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims recite additional elements or a combination of elements such that the claims amount to significantly more than the judicial exception. After evaluation, the Office has determined that none of the claims recite any additional elements or any combination of elements such that the claims would amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, each claim as a whole does not cover a particular solution to a problem or a particular way to achieve a desired outcome and thus does not provide an improvement to technology or technical field under MPEP 2106.05(a). Each claim as a whole merely recites the idea of a solution or outcome. Each claim as a whole represents no more than mere instructions to apply the judicial exception on a computer under MPEP 2106.05(f) and/or recites limitations representing necessary data gathering and/or data outputting under MPEP 2106.05(g). The courts have recognized that computer functions may be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality as discussed under Step 2A Prong 2 above). See MPEP 2106.05(d), subsection II. Evidence that conducting diagnostic analysis of firmware code of IMDs is well-understood, routine, and conventional is provided by NPL Zhu.
Furthermore, looking at the limitations individually or as any ordered combination adds nothing that is not already present when looking at the combinations of elements. The Office has determined that each claim as a whole does not amount to an inventive concept.
Therefore, whether taken individually or as an ordered combination, claims 1, 14, and 25 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Regarding Dependent claims 2 – 13, 15 – 24, and 26 – 30, the Office carries over its determination that none of claims 2 – 13, 15 – 24, and 26 – 30 recites additional elements from Step 2A, Prong Two so as to apply the same determination in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B. The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. Claims 2 – 13, 15 – 24, and 26 – 30 are all encompassed by the abstract idea grouping of mental processes.
Therefore, whether taken individually or as an ordered combination, claims 1 – 30 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 3, 6, and 8 – 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles (U.S. Pub. No. 2022/0203107 A1) in view of Haller (U.S. Pub. No. 2002/0013613 A1), Katra (U.S. Pub. No. 2021/0344880 A1), NPL Zhu, and Rade (U.S. Pub. No. 2023/0066914 A1).
Regarding independent claim 1, Nobles teaches the limitations identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient, (Paragraphs [0031] – [0032] and [0085] of Nobles. In the instant application, the broadest interpretation of “method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient” reads on therapy system in Nobles (Paragraphs [0031] – [0032]) wherein an implantable medical device (IMD) may be configured to provide stimulation therapy to a patient using waveform generation, and the active program module in Nobles (Paragraph [0085]) may be configured with code portions (i.e., pieces of software) that may be pre-loaded at initialization by the program manager module, and the program can be overwritten by the program manager module.), the method comprising:
storing, by one or more processors of the IMD, firmware for execution by the one or more processors at a memory, wherein the firmware includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, (Paragraphs [0031], [0034], [0036], [0072], and [0085] of Nobles. In the instant application, the broadest interpretation of “storing, by one or more processors of the IMD, firmware for execution by the one or more processors at a memory, wherein the firmware includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and the plurality of firmware modules include:” reads on the implantable medical device (IMD) in Nobles (Paragraphs [0031], [0036], [0072], and [0085]) having a controller/processing and embedded memory modules, pulse driving circuitry with therapy application module, a far-field and/or near field communication module etc. The IMD's software/firmware code (e.g., RTOS) may be stored in the memory of the IMD.) and the plurality of firmware modules include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple code paths (Paragraphs [0034], [0036], and [0072] of Nobles. In the instant application, the broadest interpretation of “the plurality of firmware modules include: a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple code paths” reads on the far-field and/or near field communication module in Nobles (Paragraphs [0034], [0036], and [0072]) operative with applicable communication protocol stacks, battery charging circuitry, switching circuitry, sensing circuitry, and the like to effectuate the overall control and management of waveform-based stimulation.);
a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths; and
a third firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a third code path of the multiple code paths (Paragraphs [0034], [0036], and [0072] of Nobles. In the instant application, the broadest interpretation of “the plurality of firmware modules include: … a third firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a third code path of the multiple code paths” reads on the IMD’s therapy application module in Nobles (Paragraphs [0034], [0036], and [0072]) including or operative with a waveform player and/or the control module in Nobles (Paragraphs [0034], [0036], and [0072]) configured to support a plurality of functional/structural modules operative to effectuate the overall control and management of waveform-based stimulation in conjunction with output driver circuitry block.);
receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths;
storing, by the one or more processors, the code path information in the memory;
quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem, the device power management hardware subsystem, or the stimulation therapy pulse generator subsystem (Paragraph [0084] of Nobles. In the instant application, the broadest interpretation of “performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path information is the device communication hardware subsystem, the device power management hardware subsystem, or the stimulation therapy pulse generator subsystem” reads on the activity in Nobles (Paragraph [0084]) of coordinating, by the active program module in the IMD’s control module, stimulation changes with waveform player to provide seamless changes to pulse characteristics through an active program buffer.).
Nobles does not appear to explicitly disclose, but Haller teaches the limitation identified in bold as “the plurality of firmware modules include: … a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths” (Paragraphs [0091] – [0092] and [0152] – [0156] of Haller. In the instant application, the broadest interpretation of “the plurality of firmware modules include: … a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths” reads on the power management module in Haller (Paragraphs [0091] – [0092] and [0152] – [0156]) configured to switch to the remaining battery as an electrical power source for communication module and/or mobile telephone.).
Nobles does not appear to explicitly disclose, but Katra teaches the limitation identified in bold as “receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry” reads on the activity in Katra (Paragraph [0064]) of using a cloud-deployed API to access an ML model and to perform additional work within the monitoring system, such that various ML models or AI engines may be deployed as so-called light versions that are configured to operate efficiently on devices with significantly limited resources (e.g., mobile devices, tablets, etc.). The broadest interpretation of “the AI model is trained based on a training dataset comprising code paths, code path metrics, or both” reads on the activity in Katra (Paragraphs [0015], [0056], and [0346]) of training the ML model on various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.).
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients” (Third Paragraph and Fourth Paragraph in Second Column on Page 878; and Second Paragraph in First Column on Page 891 and Last Paragraph in Second Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “associated with IMDs corresponding to a population of patients” reads on the iEEG recordings in NPL Zhu (Last Paragraph in Second Column on Page 889) corresponding to 11 patients having 106 annotated seizures over 1255 hours), to train the model.).
Nobles does not appear to explicitly disclose, but Rade teaches the limitation identified in bold as “generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths” (Paragraph [0034] of Rade. In the instant application, the broadest interpretation of “generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths” reads on the activity in Rade (Paragraph [0034]) of generating a sequence or set of ALL instructions for execution by the SCU operating as the secondary processor in order to control and configure one or more SEs for generating one or more pulse waveforms pursuant to the therapy program selection.).
Nobles does not appear to explicitly disclose, but Katra teaches the limitation identified in bold as “storing, by the one or more processors, the code path information in the memory” (Paragraphs [0056], [0125], [0132], and [0346] of Katra. In the instant application, the broadest interpretation of “storing, by the one or more processors, the code path information in the memory” reads on the processing circuitry in Katra (Paragraph [0132]) including fixed function circuitry embodied as firmware. The Office has determined that a person of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing would understand that fixed function circuitry can include memory, and while fixed-function (or hardwired) logic performs a specific, unalterable operation, that operation often requires physical storage.).
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model” (Last Paragraph in Second Column on Page 880 to Second Paragraph in First Column on Page 881 of NPL Zhu. In the instant application, the broadest interpretation of “quantifying, by the one or more processors, performance of the IMD based on the code path information using the AI model” reads on the activity in NPL Zhu (Second Paragraph in First Column on Page 881) evaluating, by using the SoC, the classifier’s performance based on sensitivity (i.e., true positive rate), specificity (i.e., selectivity or True Negative rate), accuracy, F1 score, the area under the ROC curve (AUC), and the false alarm rate (FAR) in ML studies on neutral datasets.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles to include the plurality of firmware modules including a second firmware module configured to control a device power management hardware subsystem associated with a second code path of the multiple code paths, as taught by Haller (Paragraphs [0091] – [0092] and [0152] – [0156]), in order to more easily, quickly and cost-effectively monitor and control the performance of an IMD in a patient on a regular or continuous basis, where the patient is not required to visit a health care facility or a health care provider in person when the monitoring is undertaken (Paragraph [0017] of Haller); include the activity of receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients, and storing, by the one or more processors, the code path information in the memory, as taught by Katra (Paragraphs [0015], [0056], [0064], [0125], [0132], and [0346]), in order to provide remote post-IMD monitoring for implantation site infections (Paragraph [0048] of Katra); include the activity of receiving, by the one or more processors, an artificial intelligence (AI) model from a remote device via wireless communication circuitry, wherein the AI model is trained based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients, as taught by NPL Zhu (Third Paragraph and Fourth Paragraph in Second Column on Page 878; and Second Paragraph in First Column on Page 891 and Last Paragraph in Second Column on Page 889), in order to implement a machine learning (ML) algorithm directly on the implant to predict the onset or severity of neurological symptoms (Second Paragraph in First Column on Page 878 of NPL Zhu); and include the activity of generating, by the one or more processors, code path information associated with one or more code paths of the multiple code paths executed during operation of the IMD, wherein the code path information is generated based on the firmware code instructions and indicates execution of code corresponding to the one or more code paths, as taught by Rade (Paragraph [0034]), in order to improve compatibility and ease of programming/control of therapy delivery in the context of emerging complex stimulation programs where it would otherwise become increasingly difficult to predict and to avoid therapy collisions, which typically occur in multi-frequency, multi-lead applications such as, e.g., dual brain hemisphere DBS therapies (Paragraph [0097] of Rade).
Regarding claim 2, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 teaches the limitation identified in bold as “the code paths, the code path metrics, or both included in the training dataset include sets of code paths, sets of code path metrics, or both corresponding to different therapy configurations for the population of patients” (Second Paragraph in First Column on Page 886, and Last Paragraph in Second Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “the code paths, the code path metrics, or both included in the training dataset include sets of code paths, sets of code path metrics, or both corresponding to different therapy configurations for the population of patients” reads on the features in NPL Zhu (Second Paragraph in First Column on Page 886) (i.e., bandpower in multiple frequency bands, the ratio of high-frequency oscillations, phase-amplitude coupling, and tremor power) corresponding to the detected onset of rest-state tremor episodes in PD and the iEEG recordings in NPL Zhu (Last Paragraph in Second Column on Page 889) corresponding to 11 patients having 106 annotated seizures over 1255 hours, using 47 to 128 channels) and easily combined with invasive neuromodulation techniques for improved symptom control.).
Regarding claim 3, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 teaches the limitation identified in bold as “the training of the AI model is performed iteratively until a stop criterion is satisfied” (Third Paragraph in First Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “the training of the AI model is performed iteratively until a stop criterion is satisfied” reads on the activity in NPL Zhu (Third Paragraph in First Column on Page 889) of training the proposed DVTE model using gradient boosting framework over multiple rounds, with deeper trees being gradually added to DVTE in later boosting rounds to better fit on training data (i.e., until bias has been reduced).).
Regarding claim 6, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 teaches the limitation identified in bold as “transmitting, via the wireless communication circuitry, feedback data to an analytics platform, wherein the training dataset is updated based on the feedback data” (Paragraph [0032] of Nobles. In the instant application, the broadest interpretation of “transmitting, via the wireless communication circuitry, feedback data to an analytics platform, wherein the training dataset is updated based on the feedback data” reads on the activity in Nobles (Paragraph [0032]) of establishing the bi-directional communication link between the IMD and the external device for facilitating a therapy application executing on external device 108 to, inter alia, receive various pieces of information, e.g., therapy measurements, sensory data, personal data, logging data, etc., from IMD 104, and to program or send instructions to IMD 104, using a standard or proprietary communication protocol stack on the external device that may also be commonly accessible to one or more other applications or software programs hosted by the external device.).
Regarding claim 8, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 6 teaches the limitation identified in bold as “the wireless circuitry is configured to support Bluetooth communication” (Paragraph [0032] of Nobles. In the instant application, the broadest interpretation of “the wireless communication circuitry is configured to support Bluetooth communication” reads on the bi-directional communication link in Nobles (Paragraph [0032]) effectuated via a wireless personal area network (WPAN) using a standard wireless protocol such as Bluetooth Low Energy (BLE), Bluetooth, Wireless USB, Zigbee, Near-Field Communications (NFC), WiFi, Infrared Wireless, and the like.).
Regarding claim 9, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 teaches the limitation identified in bold as “performance of the IMD is quantified as one or more code metrics generated by the AI model, the method comprising determining a graduated response based on the one or more code metrics quantifying the performance of the IMD” (Second Paragraph in Second Column on Page 887; Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “performance of the IMD is quantified as one or more code metrics generated by the AI model” reads on the performance in NPL Zhu (Second Paragraph in Second Column on Page 887) of the algorithm/hardware (of the neural prostheses) quantified as accuracy, F1 score, sensitivity (i.e., true positive rate), power vs. energy efficiency, and detection versus system latency, as well as specificity (i.e., selectivity or True Negative rate), the area under the ROC curve (AUC), and the false alarm rate (FAR) in ML studies on neutral datasets. The broadest reasonable interpretation of “the method comprising determining a graduated response based on the one or more code metrics quantifying the performance of the IMD” reads on the activities in NPL Zhu (Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889) of incrementally learning to account for previously unseen changes in neurological patterns and updating model parameters (e.g., gradually adding deeper trees to DVTE) with the sequential arrival of data (and associated performance metrics), thus dynamically adapting to new signal patterns.).
Regarding claim 10, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 9 teaches the limitation identified in bold as “the graduated response comprises adjustments to one or more therapy modes of the IMD, adjustments to one or more stimulation settings of a therapy program configured for the IMD, rebooting a particular firmware module of the IMD, updating firmware of the IMD, or a combination thereof” (Paragraph [0079] of Nobles. In the instant application, the broadest interpretation of “the graduated response comprises adjustments to one or more therapy modes of the IMD, adjustments to one or more stimulation settings of a therapy program configured for the IMD, rebooting a particular firmware module of the IMD, updating firmware of the IMD, or a combination thereof” reads on the activity in Nobles (Paragraph [0079]) of overwriting, using the programs manager module, default programming for certain types of modes, e.g., MRI mode, surgery mode and system impedance.).
Regarding claim 11, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 9 teaches the limitation identified in bold as “the graduated response is determined based on a sequence of responses stored in memory” (Paragraph [0066] of Nobles. In the instant application, the broadest interpretation of “the graduated response is determined based on a sequence of responses stored in memory” reads on the different patterns of pulse definitions and timing interval definitions in Nobles (Paragraph [0066]) generated using and/or supplemented with signal sampling and processing techniques to inject various sequences of irregularity in a program record in order to avoid and/or mitigate the effect of tissue habituation.).
Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 3, and further in view of Verbeek (U.S. Pub. No. 2004/0215252 A1).
Regarding claim 4, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 3 teaches the limitation identified in bold as “the training of the AI model is configured to identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “the training of the AI model” reads on the activity in Katra (Paragraphs [0015], [0056], [0064], and [0346]) of training the ML model on, for example, various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.).
Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 3 does not appear to explicitly disclose, but Verbeek teaches the limitation identified in bold as “the training of the AI model is configured to identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules” (Paragraph [0026] of Verbeek. In the instant application, the broadest interpretation of “identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules” reads on the activity in Verbeek (Paragraph [0026]) of using a second sequence of instructions as a means for determining baseline inter-ventricular asynchrony without pacing.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Rade to implement the training of the AI model being configured to identify one or more baseline or expected code paths, code path metrics, or a combination thereof for each of the plurality of firmware modules, as taught by Verbeek (Paragraph [0026]), in order to resynchronize ventricular contraction thereby reducing mitral regurgitation and optimizing left ventricular filling thereby improving cardiac function (Paragraph [0005] of Verbeek).
Claim 5 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 3, and further in view of Shoaran (U.S. Pub. No. 2020/0388397 A1).
Regarding claim 5, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 3 does not appear to explicitly disclose, but Shoaran teaches the limitation identified in bold as “one or more hyperparameters of the AI model are modified during at least one iteration of the training of the AI model” (Paragraph [0146] of Shoaran. In the instant application, the broadest interpretation of “one or more hyperparameters of the AI model are modified during at least one iteration of the training of the AI model” reads on the hyperparameters in Shoaran (Paragraph [0146]) of the machine learning model being tuned during multiple iterations of training the model until a stable cross-validation is obtained.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Rade to implement the one or more hyperparameters of the AI model being modified during at least one iteration of the training of the AI model, as taught by Shoaran (Paragraph [0146]), in order to provide electrical stimulation from the electrodes 16 to alleviate a patient's symptoms, such as chronic back pain (Paragraph [0005] of Shoaran).
Claim 7 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 6 in view of Zhang (U.S. Pub. No. 2022/0266027 A1).
Regarding claim 7, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 6 does not appear to explicitly disclose, but Zhang teaches the limitation identified in bold as “the feedback data comprises information associated with deviations from one or more baseline code paths, information indicating one or more observed code paths that were misclassified by the AI model, differences between expected or baseline code paths and observed code paths, information identifying one or more stimulation programs of the IMD, information identifying one or more stimulation parameters, information identifying one or more IMD component states, or a combination thereof, and wherein the training dataset is updated based on the feedback data” (Paragraph [0106] of Zhang. In the instant application, the broadest interpretation of “the feedback data comprises information associated with deviations from one or more baseline code paths, information indicating one or more observed code paths that were misclassified by the AI model, differences between expected or baseline code paths and observed code paths, information identifying one or more stimulation programs of the IMD, information identifying one or more stimulation parameters, information identifying one or more IMD component states, or a combination thereof, and wherein the training dataset is updated based on the feedback data” reads on the closed-loop feedback activities in Zhang (Paragraph [0106]) comprising identifying the best set of stimulation parameters and recording the sensed neural response corresponding to that set of stimulation parameters.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Rade to implement the feedback data comprising information associated with deviations from one or more baseline code paths, information indicating one or more observed code paths that were misclassified by the AI model, differences between expected or baseline code paths and observed code paths, information identifying one or more stimulation programs of the IMD, information identifying one or more stimulation parameters, information identifying one or more IMD component states, or a combination thereof, and wherein the training dataset is updated based on the feedback data, as taught by Zhang (Paragraph [0106]), in order to determine effective therapy for the patient (Paragraph [0106] of Zhang).
Claim 12 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1, and further in view of Chakravarthy (U.S. Pub. No. 2020/0352522 A1).
Regarding claim 12, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 does not appear to explicitly disclose, but Chakravarthy teaches the limitation identified in bold as “the firmware code instructions comprise one or more identifiers or markers corresponding to execution of particular firmware code instructions, and wherein the code path information comprises at least one of the one or more identifiers or markers corresponding to the executed one or more code paths of the IMD” (Paragraph [0118] of Chakravarthy. In the instant application, the broadest interpretation of “the firmware code instructions comprise one or more identifiers or markers corresponding to execution of particular firmware code instructions, and wherein the code path information comprises at least one of the one or more identifiers or markers corresponding to the executed one or more code paths of the IMD” reads on the diagnosis and procedure codes in Chakravarthy (Paragraph [0118]), such as codes defined in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and Current Procedural Technology (CPT) codes, such that the cohort-based Cardiac Rhythm Classifier (CRC) may be trained based on segments of cardiac electrogram (EGM) strips and, in some examples, other patient data of a select group of patients, all of which have a shared ICD-10 and/or CPT code history in their medical records).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Rade to implement the firmware code instructions comprising one or more identifiers or markers corresponding to execution of particular firmware code instructions, with the code path information comprising at least one of the one or more identifiers or markers corresponding to the executed one or more code paths of the IMD, as taught by Chakravarthy (Paragraph [0118]), in order to personalize cardiac rhythm classification models, train cardiac rhythm classification models for smaller cohorts of patients, train cardiac rhythm classification models for individual patients, enable a user to analyze the cardiac EGM records of patient in the context of a more representative population or relative to the previous history of patient, and enable the user to make decisions more applicable to the individual patient in question thereby reducing power consumption and/or bandwidth utilization of the medical device (Paragraph [0043] of Chakravarthy).
Claim 13 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1, and further in view of Yoder (U.S. Pub. No. 2023/0046704 A1).
Regarding claim 13, Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 teaches the limitation identified in bold as “quantifying the performance of the IMD comprises determining whether the code path information indicates normal operations of the IMD or abnormal operations of the IMD” (Last Paragraph in Second Column on Page 880 to Second Paragraph in First Column on Page 881 of NPL Zhu. In the instant application, the broadest interpretation of “quantifying the performance of the IMD” reads on the activity in NPL Zhu (Second Paragraph in First Column on Page 881) of evaluating, by using the SoC, the classifier’s performance based on sensitivity (i.e., true positive rate), specificity (i.e., selectivity or True Negative rate), accuracy, F1 score, the area under the ROC curve (AUC), and the false alarm rate (FAR) in ML studies on neutral datasets.).
Nobles as modified by Haller, Katra, NPL Zhu, and Rade and applied to claim 1 does not appear to explicitly disclose, but Yoder teaches the limitation identified in bold as “quantifying the performance of the IMD comprises determining whether the code path information indicates normal operations of the IMD or abnormal operations of the IMD” (Paragraph [0081] of Yoder. In the instant application, the broadest interpretation of “determining whether the code path information indicates normal operations of the IMD or abnormal operations of the IMD” reads on the activity in Yoder (Paragraph [0081]) of implementing a model to differentiate normal cardiac activity from cardiac episodes; the personalized values calibrate that logic and its model to the specific cardiac activity of the patient such that the model is now configured to differentiate the patient’s normal or healthy cardiac activity from any abnormal and/or dangerous cardiac activity.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Rade to include the activity of determining whether the code path information indicates normal operations of the IMD or abnormal operations of the IMD, as taught by Yoder (Paragraph [0081]), in order to improve its patient monitoring and/or therapy functionality. (Paragraph [0003] of Yoder).
Claims 14 and 23 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, NPL Zhu, Haller, and Zhang.
Regarding independent claim 14, Nobles teaches the limitations identified in bold as:
A method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient (Paragraphs [0031] – [0032] and [0085] of Nobles. In the instant application, the broadest interpretation of “method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient” reads on therapy system in Nobles (Paragraphs [0031] – [0032]) wherein an implantable medical device (IMD) may be configured to provide stimulation therapy to a patient using waveform generation, and the active program module in Nobles (Paragraph [0085]) may be configured with code portions (i.e., pieces of software) that may be pre-loaded at initialization by the program manager module, and the program can be overwritten by the program manager module.), the method comprising:
training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients;
transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions (Paragraphs [0031], [0034], [0036], [0072], and [0085] of Nobles. In the instant application, the broadest interpretation of “the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions” reads on the implantable medical device (IMD) in Nobles (Paragraphs [0031], [0036], [0072], and [0085]) having a controller/processing and embedded memory modules, pulse driving circuitry with therapy application module, a far-field and/or near field communication module etc. The IMD's software/firmware code (e.g., RTOS) may be stored in the memory of the IMD.), and the plurality of firmware modules include:
a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths; and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple code paths (Paragraphs [0034], [0036], and [0072] of Nobles. In the instant application, the broadest interpretation of “the plurality of firmware modules include: … a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple code paths” reads on the IMD’s therapy application module in Nobles (Paragraphs [0034], [0036], and [0072]) including or operative with a waveform player and/or the control module in Nobles (Paragraphs [0034], [0036], and [0072]) configured to support a plurality of functional/structural modules operative to effectuate the overall control and management of waveform-based stimulation in conjunction with output driver circuitry block.);
receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof;
updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model; and
transmitting, by the one or more processors, the updated trained AI model to the at least one IMD; and
performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metrics is the device power management hardware subsystem or the stimulation therapy pulse generator hardware subsystem (Paragraph [0084] of Nobles. In the instant application, the broadest interpretation of “performing, by the one or more processors of the IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metrics is the device power management hardware subsystem or the stimulation therapy pulse generator hardware subsystem” reads on the activity in Nobles (Paragraph [0084]) of coordinating, by the active program module in the IMD’s control module, stimulation changes with waveform player to provide seamless changes to pulse characteristics through an active program buffer.).
Nobles does not appear to explicitly disclose, but Katra teaches the limitation identified in bold as “training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “training, by one or more processors, an AI model … to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients” reads on the activity in Katra (Paragraphs [0015], [0056], and [0346]) of training the ML model on, for example, various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.)
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients” (Second Paragraph in First Column on Page 881 of NPL Zhu. In the instant application, the broadest interpretation of “quantify performance of an IMD using an artificial intelligence (AI) model” reads on the activity in NPL Zhu (Second Paragraph in First Column on Page 881) of evaluating, by using the SoC, the classifier’s performance based on sensitivity (i.e., true positive rate), specificity (i.e., selectivity or True Negative rate), accuracy, F1 score, the area under the ROC curve (AUC), and the false alarm rate (FAR) in ML studies on neutral datasets.)
Nobles does not appear to explicitly disclose, but Katra teaches the limitation identified in bold as “transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “transmitting, by the one or more processors, the trained AI model to at least one IMD” reads on the activity in Katra (Paragraph [0064]) of using a cloud-deployed API to access an ML model and to perform additional work within the monitoring system, such that various ML models or AI engines may be deployed as so-called light versions that are configured to operate efficiently on devices with significantly limited resources (e.g., mobile devices, tablets, etc.). The broadest interpretation of “the trained AI model” reads on the ML model in Katra (Paragraphs [0015], [0056], and [0346]) trained on various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.).
Nobles does not appear to explicitly disclose, but Haller teaches the limitation identified in bold as “the plurality of firmware modules include: a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths” (Paragraphs [0091] – [0092] and [0152] – [0156] of Haller. In the instant application, the broadest interpretation of “the plurality of firmware modules include: a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths” reads on the power management module in Haller (Paragraphs [0091] – [0092] and [0152] – [0156]) configured to switch to the remaining battery as an electrical power source for communication module and/or mobile telephone.).
Nobles does not appear to explicitly disclose, but Zhang teaches the limitation identified in bold as “receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof” (Paragraph [0106] of Zhang. In the instant application, the broadest interpretation of “receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof” reads on the closed-loop feedback activities in Zhang (Paragraph [0106]) comprising identifying the best set of stimulation parameters and recording the sensed neural response corresponding to that set of stimulation parameters.).
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model” (Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model” reads on the activities in NPL Zhu (Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889) of incrementally learning to account for previously unseen changes in neurological patterns and updating model parameters (e.g., gradually adding deeper trees to DVTE) with the sequential arrival of data (and associated performance metrics), thus dynamically adapting to new signal patterns.).
Nobles does not appear to explicitly disclose, but Goetz teaches the limitation identified in bold as “transmitting, by the one or more processors, the updated trained AI model to the at least one IMD” (Paragraphs [0015], [0056], [0064], and [0346] of Katra In the instant application, the broadest interpretation of “transmitting, by the one or more processors, the updated trained AI model to the at least one IMD” reads on the activity in Katra (Paragraph [0064]) of using a cloud-deployed API to access an ML model and to perform additional work within the monitoring system, such that various ML models or AI engines may be deployed as so-called light versions that are configured to operate efficiently on devices with significantly limited resources (e.g., mobile devices, tablets, etc.). The broadest interpretation of “the updated AI model” reads on the ML model in Katra (Paragraphs [0015], [0056], and [0346]) trained on various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles to include the activity of training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients, include the activity of transmitting, by the one or more processors, the trained AI model to at least one IMD, wherein the at least one IMD comprises firmware for execution by one or more processors of the IMD, wherein the firmware of the IMD includes a plurality of firmware modules controlling one or more corresponding hardware components of the IMD configured to support respective IMD functions, and wherein each of the plurality of firmware modules is associated with multiple code paths traversing respective firmware code instructions, and include the activity of transmitting, by the one or more processors, the updated trained AI model to the at least one IMD, as taught by Katra (Paragraphs [0015], [0056], [0064], and [0346]), in order to provide remote post-IMD monitoring for implantation site infections (Paragraph [0048] of Katra); include the activity of training, by one or more processors, an AI model to quantify performance of an IMD using an artificial intelligence (AI) model to produce a trained AI model, wherein the training of the AI model is based on a training dataset comprising code paths, code path metrics, or both associated with IMDs corresponding to a population of patients, and include the activity of updating, by the one or more processors, the trained AI model based on the feedback to produce an updated trained AI model, as taught by NPL Zhu (Second Paragraph in First Column on Page 881), in order to implement a machine learning (ML) algorithm directly on the implant to predict the onset or severity of neurological symptoms (Second Paragraph in First Column on Page 878 of NPL Zhu); include the plurality of firmware modules include: a first firmware module configured to control a device power management hardware subsystem associated with a first code path of the multiple code paths, as taught by Haller (Paragraphs [0091] – [0092] and [0152] – [0156]), in order to more easily, quickly and cost-effectively monitor and control the performance of an IMD in a patient on a regular or continuous basis, where the patient is not required to visit a health care facility or a health care provider in person when the monitoring is undertaken (Paragraph [0017] of Haller); and the activity of receiving, by the one or more processors, feedback from the at least one IMD, wherein the feedback comprises code path metrics generated based on execution of one or more code paths traversed during operations of the at least on IMD, information identifying one or more stimulation programs of the at least one IMD, information identifying one or more stimulation parameters of the one or more stimulation programs, information identifying one or more component states for the at least one IMD, or a combination thereof, as taught by Zhang (Paragraph [0106]), in order to determine effective therapy for the patient (Paragraph [0106] of Zhang).
Regarding claim 23, Nobles as modified by Haller, Katra, NPL Zhu, and Zhang and applied to claim 22 teaches the limitation identified in bold as “the one or more graduated responses comprise adjustments to one or more therapy modes of a particular IMD, adjustments to one or more stimulation settings of a therapy program configured for the particular IMD, rebooting a particular firmware module of the particular IMD, updating firmware of the particular IMD, or a combination thereof” (Paragraph [0079] of Nobles. In the instant application, the broadest interpretation of “the one or more graduated responses comprise adjustments to one or more therapy modes of a particular IMD, adjustments to one or more stimulation settings of a therapy program configured for the particular IMD, rebooting a particular firmware module of the particular IMD, updating firmware of the particular IMD, or a combination thereof” reads on the activity in Nobles (Paragraph [0079]) of overwriting, using the programs manager module, default programming for certain types of modes, e.g., MRI mode, surgery mode and system impedance.).
Claim 15 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14, and further in view of Thomas (U.S. Pub. No. 2022/0336088 A1).
Regarding claim 15, Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 teaches the limitation identified in bold as “aggregating feedback received from a plurality of IMDs to produce an updated training dataset” (Last Paragraph in First Column to First Paragraph in Second Column on Page 879; Third Paragraph in Second Column on Page 882; and Last Paragraph in Second Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “feedback received from a plurality of IMDs” reads on the electrophysiological activity of the brain in NPL Zhu (Last Paragraph in First Column to First Paragraph in Second Column on Page 879; Third Paragraph in Second Column on Page 882; and Last Paragraph in Second Column on Page 889) recorded through various noninvasive, minimally-invasive, or invasive electrode such as scalp EEG, subscalp EEG, electrocorticography (ECoG), also known as intracranial EEG (iEEG), stereo-EEG (sEEG) and deep-brain leads.).
Nobles as modified by Haller, Katra, NPL Zhu, and Zhang and applied to claim 14 does not appear to explicitly disclose, but Thomas teaches the limitation identified in bold as “aggregating feedback received from a plurality of IMDs to produce an updated training dataset” (Paragraphs [0174], [0175], and [0180] of Thomas. In the instant application, the broadest interpretation of “aggregating … to produce an updated training dataset” reads on the activity in Thomas (Paragraphs [0174], [0175], and [0180]) of generating a training dataset comprising input data corresponding to a stream of patient data received from a pool of similar patients and aggregated over a course of operation of an inpatient medical facility over different time periods over the course of operation of the inpatient medical facility.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Haller, Katra, NPL Zhu, and Zhang to include the activity of aggregating feedback received from a plurality of IMDs to produce an updated training dataset, as taught by Thomas (Paragraphs [0174], [0175], and [0180]), in order to help care providers identify complex patients, learn more about their prospective outcomes, and plan their care both before and after their discharge (Paragraph [0003] of Thomas).
Claims 20 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14, and further in view of Boveja (U.S. Pub. No. 2003/0212440 A1).
Regarding claim 20, Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 does not appear to explicitly disclose, but Boveja teaches the limitation identified in bold as “the IMDs corresponding to the population of patients are configured to provide different therapies to respective patients of the population of patients” (Paragraph [0132] of Boveja. In the instant application, the broadest interpretation of “the IMDs corresponding to the population of patients are configured to provide different therapies to respective patients of the population of patients” reads on the stimulator devices in Boveja (Paragraph [0132]) corresponding to the physician’s patient population and configured to provide customized therapies for each respective individual patient in the population of patients).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Katra, NPL Zhu, Haller, and Zhang to implement the IMDs corresponding to the population of patients being configured to provide different therapies to respective patients of the population of patients, as taught by Boveja (Paragraph [0132]), in order to achieve true modulation of the vagus nerve by using a multilevel digital type of baseband signal, which is varied appropriately for the application and is software controlled (Paragraph [0044] of Boveja).
Claim 21 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 in view of Verbeek.
Regarding claim 21, Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 does not appear to explicitly disclose, but Verbeek teaches the limitation identified in bold as “establishing one or more baseline or expected code paths during the training of the AI model” (Paragraph [0026] of Verbeek. In the instant application, the broadest interpretation of “establishing one or more baseline or expected code paths during the training of the AI model” reads on the activity in Verbeek (Paragraph [0026]) of using a second sequence of instructions as a means for determining baseline inter-ventricular asynchrony without pacing.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Katra, NPL Zhu, Haller, and Zhang to include the activity of establishing one or more baseline or expected code paths during the training of the AI model, as taught by Verbeek (Paragraph [0026]), in order to resynchronize ventricular contraction thereby reducing mitral regurgitation and optimizing left ventricular filling thereby improving cardiac function (Paragraph [0005] of Verbeek).
Claim 24 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 in view of Yoder.
Regarding claim 24, Nobles as modified by Katra, NPL Zhu, Haller, and Zhang and applied to claim 14 teaches the limitation identified in bold as “the trained AI model comprises a classifier configured to classify one or more code paths observed at a particular IMD as normal or abnormal” (Paragraph [0081] of Yoder. In the instant application, the broadest interpretation of “the trained AI model comprises a classifier configured to classify one or more code paths observed at a particular IMD as normal or abnormal” reads on the model in Yoder (Paragraph [0081]) being implemented to differentiate normal cardiac activity from cardiac episodes; the personalized values calibrate that logic and its model to the specific cardiac activity of the patient such that the model is now configured to differentiate the patient’s normal or healthy cardiac activity from any abnormal and/or dangerous cardiac activity.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Katra, NPL Zhu, Haller, and Zhang to implement the trained AI model comprises a classifier configured to classify one or more code paths observed at a particular IMD as normal or abnormal, as taught by Yoder (Paragraph [0081]), in order to improve its patient monitoring and/or therapy functionality. (Paragraph [0003] of Yoder).
Claims 25 and 29 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, Goetz (U.S. Pub. No. 2005/0061336 A1), and Zhang.
Regarding independent claim 25, Nobles teaches the limitations identified in bold as:
A method of controlling operations of implantable medical devices (IMDs) using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs (Paragraphs [0031] – [0032] and [0085] of Nobles. In the instant application, the broadest interpretation of “method for conducting diagnostic analysis of firmware code of an implantable medical device (IMD) of a patient” reads on therapy system in Nobles (Paragraphs [0031] – [0032]) wherein an implantable medical device (IMD) may be configured to provide stimulation therapy to a patient using waveform generation, and the active program module in Nobles (Paragraph [0085]) may be configured with code portions (i.e., pieces of software) that may be pre-loaded at initialization by the program manager module, and the program can be overwritten by the program manager module.), comprising:
receiving, by one or more servers for IMD management, code path data from a plurality of IMDs after implantation in respective patients, the code path data representing firmware code instructions executed by one or more firmware modules of a plurality of firmware modules corresponding to firmware executable by one or more processors of each of the plurality of IMDs to control corresponding hardware components of the IMDs for respective IMD functions, and wherein multiple identified code paths traversing respective firmware code instructions are identified for each of the plurality of firmware modules (Paragraphs [0031], [0034], [0036], [0072], and [0085] of Nobles. In the instant application, the broadest interpretation of “receiving, by one or more servers for IMD management, code path data from a plurality of IMDs after implantation in respective patients, the code path data representing firmware code instructions executed by one or more firmware modules of a plurality of firmware modules corresponding to firmware executable by one or more processors of each of the plurality of IMDs to control corresponding hardware components of the IMDs for respective IMD functions, and wherein multiple identified code paths traversing respective firmware code instructions are identified for each of the plurality of firmware modules” reads on the implantable medical device (IMD) in Nobles (Paragraphs [0031], [0036], [0072], and [0085]) having a controller/processing and embedded memory modules, pulse driving circuitry with therapy application module, a far-field and/or near field communication module etc. The IMD's software/firmware code (e.g., RTOS) may be stored in the memory of the IMD.), and the plurality of firmware modules of at least one IMD of the plurality of IMDs include:
a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple identified code paths (Paragraphs [0034], [0036], and [0072] of Nobles. In the instant application, the broadest interpretation of “the plurality of firmware modules of at least one IMD of the plurality of IMDs include: a first firmware module configured to control a device communication hardware subsystem associated with a first code path of the multiple identified code paths” reads on the far-field and/or near field communication module in Nobles (Paragraphs [0034], [0036], and [0072]) operative with applicable communication protocol stacks, battery charging circuitry, switching circuitry, sensing circuitry, and the like to effectuate the overall control and management of waveform-based stimulation.); and
a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple identified code paths (Paragraphs [0034], [0036], and [0072] of Nobles. In the instant application, the broadest interpretation of “the plurality of firmware modules include: … a second firmware module configured to control a stimulation therapy pulse generator hardware subsystem associated with a second code path of the multiple identified code paths” reads on the IMD’s therapy application module in Nobles (Paragraphs [0034], [0036], and [0072]) including or operative with a waveform player and/or the control module in Nobles (Paragraphs [0034], [0036], and [0072]) configured to support a plurality of functional/structural modules operative to effectuate the overall control and management of waveform-based stimulation in conjunction with output driver circuitry block.);
training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD;
distributing the trained AI model from one or more servers for IMD management to a set of IMDs;
operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations;
detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD; and
performing, by the one or more processors of the at least one IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem (Paragraph [0084] of Nobles. In the instant application, the broadest interpretation of “performing, by the one or more processors of the at least one IMD, a response action controlling an operation of the IMD, the response action is based on whether a type of hardware subsystem associated with the code path metric is the device communication hardware subsystem or the stimulation therapy pulse generator hardware subsystem” reads on the activity in Nobles (Paragraph [0084]) of coordinating, by the active program module in the IMD’s control module, stimulation changes with waveform player to provide seamless changes to pulse characteristics through an active program buffer.).
Nobles does not appear to explicitly disclose, but Goetz teaches the limitation identified in bold as “a method of controlling operations of implantable medical devices (IMDs) using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “using an artificial intelligence (AI) model” reads on the activity in Katra (Paragraph [0064]) of using a cloud-deployed API to access an ML model and to perform additional work within the monitoring system (i.e., the IMD), such that various ML models or AI engines may be deployed as so-called light versions that are configured to operate efficiently on devices with significantly limited resources (e.g., mobile devices, tablets, etc.).).
Nobles does not appear to explicitly disclose, but Katra teaches the limitation identified in bold as “training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD” (Paragraphs [0015], [0056], [0064], and [0346] of Katra. In the instant application, the broadest interpretation of “training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD” reads on the activity in Katra (Paragraphs [0015], [0056], and [0346]) of training the ML model on various abnormality libraries (e.g., training sets), with the training sets being code retrieved from any available data storage media accessed by one or more computers or one or more processors.).
Nobles does not appear to explicitly disclose, but Goetz teaches the limitation identified in bold as “distributing the trained AI model from one or more servers for IMD management to a set of IMDs” (Paragraphs [0045] and [0046] of Goetz. In the instant application, the broadest interpretation of “distributing the trained AI model from one or more servers for IMD management to a set of IMDs” reads on the activity in Goetz (Paragraphs [0045] and [0046]) of transferring the updated IMD program from the patient computing device 512 to the IMD 503 via third party server.).
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations” (Paragraph [0045] of Goetz. In the instant application, the broadest interpretation of “operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations” reads on the activity in Goetz (Paragraph [0045]) of performing the actual programming of IMD dynamically or after the updated program has been completely transferred to telemetry interface module.).
Nobles does not appear to explicitly disclose, but NPL Zhu teaches the limitation identified in bold as “detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD” (Paragraph [0106] of Zhang. In the instant application, the broadest interpretation of “detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD” reads on the closed-loop feedback activities in Zhang (Paragraph [0106]) comprising identifying the best set of stimulation parameters and recording the sensed neural response corresponding to that set of stimulation parameters.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles to include the method of controlling operations of implantable medical devices (IMDs) using an artificial intelligence (AI) model of IMD operation based on firmware code path execution in respective IMDs, and include the activity of training, using one or more servers, the AI model using the received code path data to generate a model of IMD operations representing proper operation of an IMD, as taught by Katra (Paragraphs [0015], [0056], [0064], and [0346]), in order to provide remote post-IMD monitoring for implantation site infections (Paragraph [0048] of Katra); include the activity of distributing the trained AI model from one or more servers for IMD management to a set of IMDs, and include the activity of operating IMDs of the set of IMDs to perform IMD-diagnostic operations based on the distributed, trained AI model to control IMD operations, as taught by Goetz (Paragraphs [0045] and [0046]), in order to facilitate IMD telemetry while leveraging existing computing devices and platforms (Paragraph [0009] of Goetz); and include the activity of detecting, by one or more processors of the at least one IMD and based on the IMD-diagnostic operations, a deviation of a code path metric from the model of IMD operations representing the proper operation of the IMD, as taught by Zhang (Paragraph [0106]), in order to determine effective therapy for the patient (Paragraph [0106] of Zhang).
Regarding claim 29, Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25 teaches the limitation identified in bold as “the set of IMDs is: different from the plurality of IMDs; the same as the plurality of IMDs; or includes at least one IMD included in the plurality of IMDs and at least one IMD not included in the plurality of IMDs” (Paragraph [0038] of Goetz. In the instant application, the broadest interpretation of “the set of IMDs is: different from the plurality of IMDs; the same as the plurality of IMDs; or includes at least one IMD included in the plurality of IMDs and at least one IMD not included in the plurality of IMDs” reads on the IMD and legacy IMDs in Goetz (Paragraph [0038]) already implanted in patients.).
Claims 26 – 27 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25, and further in view of Aubin (U.S. Pub. No. 2023/0022710 A1).
Regarding claim 26, Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25 teaches the limitation identified in bold as “training the AI model using the received code path data to generate the model of IMD operations representing proper operation of an IMD comprises preprocessing the code path data prior to training the AI model” (Paragraph [0326] of Aubin. In the instant application, the broadest interpretation of “training the AI model using the received code path data to generate the model of IMD operations representing proper operation of an IMD comprises preprocessing the code path data prior to training the AI model” reads on the activity in Aubin (Paragraph [0326]) of training the machine learning model processing a potentially large collection of patient datasets across a patient population to classify subsequent instances of sensor data (“kinematic data “) as one of a particular type of movement, with data preprocessing measures being taken to ensure quality and consistency of the kinematic data across the patient population that is used to train the machine-learning model.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Katra, Goetz, and Zhang such that training the AI model using the received code path data to generate the model of IMD operations representing proper operation of an IMD comprises preprocessing the code path data prior to training the AI model, as taught by Aubin (Paragraph [0326]), in order to evaluate kinematic data obtained from a single device per patient, such as a single intelligent implant or externally worn device (e.g., on or adjacent to one body part associated with a joint, such as a tibia) (Paragraph [0091] of Aubin).
Regarding claim 27, Nobles as modified by Katra, Goetz, Zhang, and Aubin and applied to claim 26 teaches the limitation identified in bold as “the preprocessing comprises generating code path metrics based on the code path data, wherein the code path metrics are utilized to train the AI model” (Second Paragraph in Second Column on Page 887; Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “the preprocessing comprises generating code path metrics based on the code path data, wherein the code path metrics are utilized to train the AI model” reads on the activity in NPL Zhu (Second Paragraph in Second Column on Page 887) of determining the performance of the algorithm/hardware (of the neural prostheses) quantified as accuracy, F1 score, sensitivity, power vs. energy efficiency, and detection versus system latency.).
Claim 28 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25, and further in view of NPL Zhu.
Regarding claim 28, Nobles as modified by Haller, Katra, and NPL Zhu and applied to claim 25 teaches the limitation identified in bold as “preprocessing additional code path data prior to training the AI model” (Second Paragraph in First Column on Page 881; Last Paragraph in First Paragraph to First Paragraph in Second Column on Page 888; and Second Paragraph in First Column on Page 889 of NPL Zhu. In the instant application, the broadest interpretation of “preprocessing additional code path data prior to training the AI model” reads on the activity in NPL Zhu (Second Paragraph in First Column on Page 881) of evaluating the classifier’s performance including sensitivity (i.e., true positive rate), specificity (i.e., selectivity or True Negative rate), the area under the ROC curve (AUC), and the false alarm rate (FAR) in ML studies on neutral datasets.).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles to include the activity of preprocessing additional code path data prior to training the AI model, as taught by NPL Zhu (Third Paragraph and Fourth Paragraph in Second Column on Page 878; and Second Paragraph in First Column on Page 891 and Last Paragraph in Second Column on Page 889), in order to implement a machine learning (ML) algorithm directly on the implant to predict the onset or severity of neurological symptoms (Second Paragraph in First Column on Page 878 of NPL Zhu).
Claim 30 is rejected under 35 U.S.C. 103(a) as being unpatentable over Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25, and further in view of Haubrich (U.S. Pub. No. 2008/0046037 A1).
Regarding claim 30, Nobles as modified by Katra, Goetz, and Zhang and applied to claim 25 teaches the limitation identified in bold as “one or more baseline or expected code paths are learned by the AI model during the training, the one or more baseline or expected code paths corresponding to proper operation of the IMD” (Paragraph [0028] of Haubrich. In the instant application, the broadest interpretation of “one or more baseline or expected code paths are learned by the AI model during the training, the one or more baseline or expected code paths corresponding to proper operation of the IMD” reads on the training sequence in Haubrich (Paragraph [0028]) being a pseudo-random noise code or other sequence developed to provide a range of data frequencies, amplitudes and data rates that are expected to be encountered during communication network transmissions).
Therefore, it would have been obvious to one of ordinary skill in the art of edge artificial intelligence for implantable pulse generators (IPGs) at the time of filing to modify the method of Nobles as modified by Katra, Goetz, and Zhang to implement one or more baseline or expected code paths being learned by the AI model during the training, the one or more baseline or expected code paths corresponding to proper operation of the IMD, as taught by Haubrich (Paragraph [0028]), in order to provide efficient communication between implanted medical devices distributed through a patient's body or regions of a patient's body, as well as with devices located external to a patient's body (Paragraph [0005] of Haubrich).
Allowable Subject Matter
Claims 16 – 19 and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: it appears none of the cited art discloses the limitation of “the aggregating comprises determining one or more average values associated with the feedback, one or more maximum values associated with the feedback, one or more minimum values associated with the feedback, one or more mean values associated with the feedback, or a combination thereof” in claim 16, the limitation of “the AI model is trained over a plurality of iterations until a stop criterion is satisfied, and wherein hyperparameters of the AI model are tuned after at least one iteration of the plurality of iterations” in claim 17, the limitation of “the stop criterion comprises a threshold performance level of the AI model” in claim 18, the limitation of “the threshold performance level corresponds to an accuracy of the AI model with respect to quantifying the performance of an IMD” in claim 19, and the limitation of “determining one or more graduated responses for different outputs of the AI model, wherein the one or more graduated responses are updated periodically based on additional training of the AI model” in claim 22.
Response to Arguments
Applicant's arguments (Second Paragraph on Page 13 to Second Paragraph on Page 23 of the Amendment filed March 26, 2026) regarding the rejection of claims 1 – 30 under 35 U.S.C. § 101 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
In the Amendment (Last Paragraph on Page 17 to Second Paragraph on Page 19), Applicant argued that claim 1 integrates the purported abstract idea into a practical application by improving implantable medical device technology. The Office respectfully disagrees. As described in the body of the present Final Action, the Office respectfully disagrees because the claim covers does not cover a particular solution to a problem or a particular way to achieve a desired outcome, but rather merely covers the idea of a solution or outcome.
Applicant's arguments (Third Paragraph on Page 23 to Second Paragraph on Page 28 of the Amendment filed March 26, 2026) regarding the rejections of claims 1 – 30 under 35 U.S.C. § 103 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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