CTNF 18/952,210 CTNF 79247 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Application Claims 1-20 have been examined in this application. This communication is the first action on the merits. Information Disclosure Statement The information disclosure statement (IDS) submitted on 11/19/2024 is being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In determining whether a claim falls within an excluded category, the Examiner is guided by the Court’s two-part framework, described in Mayo and Alice . Id. at 217-18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc ., 566 U.S. 66, 75-77 (2012)); Bilski v. Kappos , 561 U.S. 593, 611 (2010); 2019 Revised Patent Subject Matter Eligibility Guidance , 84 Fed. Reg. 50 (Jan. 7, 2019); the October 2019 Update of the 2019 Revised Guidance (Oct. 17, 2019); 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (July 17, 2024), and the USPTO’s Paten Subject Matter Eligibility Memorandums of August 4, 2025 and December 5, 2025. Step 1 Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l , 573 U. S. ____ (2014). The broadest reasonable interpretation of claim 1 encompasses a computer system ( e.g. , hardware such as a processor and memory) that implements the recited functions. If assuming that the system comprises a device or set of devices, then the system is directed to a machine, which is a statutory category of invention. Claim 13 is directed to a statutory category, because a series of recited steps satisfies the requirements of a process (a series of acts). ( Step 1: Yes ). Next, the claim is analyzed to determine whether it is directed to a judicial exception. Step 2A – Prong 1 Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more of generating a recommendation for the ambulatory medical device. The claim recites: 13. A method comprising: receiving over a network physiological signal data obtained by an ambulatory medical device; processing the received physiological signal data by inputting the physiological signal data into one or more pre-trained machine learning models, each of the one or more pre-trained machine learning models trained to generate an output indicating whether the physiological signal data represents an arrhythmia episode of a particular type; upon obtaining an output from the one or more pre-trained machine learning models indicating a detected arrythmia episode of a first type, determining that an on-device arrythmia detection algorithm of the ambulatory medical device did not detect a corresponding arrythmia episode of the first type; and as a result of determining that the on-device arrythmia detection algorithm of the ambulatory medical device did not detect a corresponding arrythmia episode of the first type, generating a reprogramming recommendation for the ambulatory medical device. The limitations of receiving data; processing the data; training ML model; determining a detection result, and generating a recommendation , as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III), and/or certain methods of organizing human activity, such as following rules or instructions, but for the recitation of generic computer components. (Note: Examiner’s language ( e.g . “ receiving data”; “processing the data”; etc. ) is an abbreviated reference to the detailed claim steps and is not an oversimplification of the claim language; the Examiner employing such shortcuts (that refer to more specific steps) when attempting to explain the rejection). That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind, and/or performed as organized human activity. Aside from the general technological environment (addressed below), it covers purely mental concepts and/or certain methods of organizing human activity processes, and the mere nominal recitation of a generic network appliance ( e.g . an interface for inputting or outputting data, or generic network-based storage devices and displays) does not take the claim limitation out of the mental processes and/or certain methods of organizing human activity grouping. Specifically, the utilizing statistical tools to process data and to output the estimated values - said functions could be performed by a human using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas ( e.g ., mental comparison regarding a sample or test subject to a control or target data in Ambry , Myriad CAFC , or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in In re Grams , 888 F.2d 835 (Fed. Cir. 1989) ( Grams )). In Grams , the recited functions require obtaining data or patient information (from sensors), and analyze that data to ascertain the existence and identity of an abnormality or estimated responses, and possible causes thereof. While said functions are performed by a computer, they are in essence a mathematical algorithm, in that they represent "[a] procedure for solving a given type of mathematical problem." Gottschalk v. Benson, 409 U.S. 63, 65, 93 S.Ct. 253, 254, 34 L.Ed.2d 273 (1972). Moreover, the Federal Circuit has held, “without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). Here, the claimed subject matter is directed to the abstract idea of manipulating existing information ( e.g ., “ received data ”) to generate additional information ( e.g ., “ a reprogramming recommendation ”). See id. Further, “analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, [are] essentially mental processes within the abstract-idea category.” Elec. Power, 830 F.3d at 1354; see also Synopsys, Inc. v. Mentor Graphics Corp ., 839 F.3d 1138, 1146 (Fed. Cir. 2016). “[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.” Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can . (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012). As per the use of artificial intelligence and/or machine learning techniques (AI/ML), said recitation does not make the claim patent eligible, because said tools are utilized merely for data gathering and comparing, and are not utilized in express manipulation and control of functional aspects and/or hardware components/equipment of real-world processes and systems using output of AI models ( e.g ., manufacturing processes and equipment, medical treatments, communications processes and systems, logistics systems and hardware, interactive smart phone apps, etc. ). It is similar to other abstract ideas held to be non-statutory by the courts. See , also, Recentive Analytics, Inc. v. Fox Corp . (Fed. Cir. 2025), wherein the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Additionally, applying machine learning to determining of accuracy of a measurement, an activity predating computers, does not transform the abstract idea into a patent-eligible invention. As per receiving, storing and outputting data limitations, it has been held that “As many cases make clear, even if a process of collecting and analyzing information is ‘limited to particular content’ or a particular ‘source,’ that limitation does not make the collection and analysis other than abstract.” SAP Am., Inc. v. InvestPic, LLC , 898 F.3d 1161, 1168 (Fed. Cir. 2018) (citation omitted); see also In re Jobin, 811 F. App’x 633, 637 (Fed. Cir. 2020) (claims to collecting, organizing, grouping, and storing data using techniques such as conducting a survey or crowdsourcing recited a method of organizing human activity, which is a hallmark of abstract ideas). All these cases describe the significant aspects of the claimed invention, albeit at another level of abstraction. See Apple, Inc. v. Ameranth, Inc. , 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) ("An abstract idea can generally be described at different levels of abstraction. As the Board has done, the claimed abstract idea could be described as generating menus on a computer, or generating a second menu from a first menu and sending the second menu to another location. It could be described in other ways, including, as indicated in the specification, taking orders from restaurant customers on a computer."). Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and/or “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea . ( Step 2A – Prong 1: Yes ). Step 2A – Prong 2 In Prong Two, the Examiner determines whether claim 13, as a whole, recites additional elements that integrate the judicial exception into a practical application of the exception, i.e ., whether the additional elements apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the judicial exception. See Guidance , 84 Fed. Reg. at 54-55. If the additional elements do not integrate the judicial exception into a practical application, then the claim is directed to the judicial exception. See id., 84 Fed. Reg. at 54. “An additional element [that] reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” is indicative of integrating a judicial exception into a practical application. See Guidance , 84 Fed. Reg. at 55. The Examiner determined that this judicial exception is not integrated into a practical application , because there are no meaningful limitations that transform the exception into a patent eligible application. In particular, the claim recites additional elements – using a processor to perform the steps of receiving data; processing the data; training ML model; determining a detection result, and generating a recommendation . However, the processor in each step is recited (or implied) at a high level of generality, i.e., as a generic processor performing a generic computer functions of processing data, including receiving, storing, comparing, and outputting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The processor that performs the recited steps merely automates these steps which can be done mentally or manually. Thus, while the additional elements have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." The claim only manipulates abstract data elements into another form, and does not set forth improvements to another technological field or the functioning of the computer itself and, instead, uses computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, - their collective functions merely provide conventional computer implementation. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp ., slip op. at 16 (citing Bilski v. Kappos , 561 U.S. 610, 611 (U.S. 2010)). The recited steps do not control or improve operation of a machine (MPEP 2106.05(a)), do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and do not apply the judicial exception with, or by use a particular machine (MPEP 2106.05(b)), but, instead, require receiving, storing, comparing and outputting data. Regarding the use of AI/ML techniques, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a particular computing application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Thus, the use of a trained machine learning model does not integrate the abstract idea of limitation into a practical application, because, under its broadest reasonable interpretation when read in light of the specification, the determining of accuracy of a measurement by comparing to a certain value or a threshld encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Similar to Recentive Analytics, claim 13 recites conventional machine learning models without specific improvements to the technology itself. The court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Claim 13 does not articulate "how" a technological improvement is achieved. As per receiving, storing and/or outputting data limitations, these recitations amount to mere data gathering and/or outputting, is insignificant post-solution or extra-solution component and represents nominal recitation of technology. Insignificant " post-solution” or “ extra-solution " activity means activity that is not central to the purpose of the method invented by the applicant. However, “(c) Whether its involvement is extra-solution activity or a field-of-use, i.e ., the extent to which (or how) the machine or apparatus imposes meaningful limits on the execution of the claimed method steps. Use of a machine or apparatus that contributes only nominally or insignificantly to the execution of the claimed method ( e.g ., in a data gathering step or in a field-of-use limitation) would weigh against eligibility”. See Bilski , 138 S. Ct. at 3230 (citing Parker v. Flook , 437 U.S. 584, 590, 198 USPQ 193, ___ (1978)). Thus, claim drafting strategies that attempt to circumvent the basic exceptions to § 101 using, for example, highly stylized language, hollow field-of-use limitations, or the recitation of token post-solution activity should not be credited. See Bilski , 130 S. Ct. at 3230. Therefore, claim 13 as a whole, outputs only data structure, - everything remains in the form of a code stored in the computer memory. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea. ( Step 2A – Prong 2: No ). Step 2B If a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether the provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The Examiner determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application , the additional element of using a processor to perform the steps of receiving data; processing the data; training ML model; determining a detection result, and generating recommendation amount to no more than mere instructions to apply the exception using a generic computer component. The claim is now re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The method would require a processor and memory in order to perform basic computer functions of receiving information, storing the information in a database, retrieving information from the database, comparing data, and outputting said information. These components are not explicitly recited and therefore must be construed at the highest level of generality. Based on the Specification , the invention utilizes conventional sensors, communication networks and generic processors, which can be found in mobile devices or desktop computers, conventional memory and display devices, and the functions performed by said generic computer elements are basic functions of a computer - performing a mathematical operation, receiving, storing, comparing and outputting data - have recognized by the courts as routine and conventional activity. Specifically, regarding the recited functions, MPEP 2106.05(d)(II) defines said functions as routine and conventional, or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result ‐‐ a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)); ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp ’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”); collecting and comparing known information in Classen 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011) iii. Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and vi. A web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). Regarding pre - training a neural model , and obtaining data via repetitive operation of said model, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a patient monitoring application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Claim 13 neither specifies a specific technical purpose for which the method is used, nor the claim defines a specific technical implementation of the method, nor the claimed method is particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer. Said AI/ML algorithms and computations are done inside of a computer, and do not have a real-world impact and are not tied to the functionality of the computer. Further, there is no evidence that the invention lies in the training phase or execution phase or both; said AI/ML recitation represents merely conventionally applying an existing model to measurement data, with the result being not technological, but purely entrepreneurial. Similar to Recentive Analytics, Inc. v. Fox Corp . (Fed. Cir. 2025), the machine learning technology as recited in claim 13 and described in the Specification is conventional, and the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. Thus, the background of the current application does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs . court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well ‐ understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Also, the claim does not involve a non-conventional and non-generic arrangement of known, conventional pieces, as asserted, by receiving information from an external source of data. The receiving of data from an external source over a network, such as via the Internet, can fairly be characterized as insignificant extra-solution activity that does not receive patentable weight. See Bilski , 545 F.3d 943, 963 (Fed. Cir. 2008) ( en banc ), aff’d sub nom Bilski v. Kappos , 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity). Similar to Content Extraction , 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC , 772 F.3d 709, 715 (Fed. Cir. 2014): “ And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis .” Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular asserted inventive technology for performing those functions. They are therefore directed to an abstract idea. As such, the additional elements, considered individually and in combination with the other claim elements, do not make the claim as a whole significantly more than the abstract idea itself. Accordingly, a conclusion that the recited steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, similar to Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) (Power Group), claim’ invocation of computers, networks, and displays does not transform the claimed subject matter into patent-eligible applications. Claim 13 does not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices. Nothing in the claim, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. Analogous to Power Group, claim 13 does not even require a new source or type of information, or new techniques for analyzing it. As a result, the claim does not require an arguably inventive set of components or methods, such as measurement devices or techniques that would generate new data. The claim does not invoke any assertedly inventive programming . Merely requiring the selection and manipulation of information - to provide a “humanly comprehensible” amount of information useful for users - by itself does not transform the otherwise-abstract processes of information collection and analysis into patent eligible subject matter. Merely obtaining and selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas. Therefore, the recited steps represent implementing the abstract idea on a generic computer, or “reciting a commonplace business method aimed at processing business information despite being applied on a general purpose computer” Versata, p. 53; Ultramerical, pp. 11-12. Furthermore, the recited functions do not improve the functioning of computers itself, including of the processor(s) or the network elements. There are no physical improvements in the claim, like a faster processor or more efficient memory, and there is no operational improvement, like mathematical computation that improve the functioning of the computer. Applicant did not invent a new type of computer; Applicant like everyone else programs their computer to perform functions. The Supreme Court in Alice indicated that an abstract claim might be statutory if it improved another technology or the computer processing itself. Using a (programmed) computer to implement a common business practice does neither. The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC , 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions ( e.g., receiving, storing, comparing and transmitting data—see the Specification as well as Alice Corp. ; Intellectual Ventures I LLC v. Symantec Corp. , 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. “ However, it is not apparent how appellant’s programmed digital computer can produce any synergistic result. Instead, the computer will simply do the job it is instructed to do. Where is there any surprising or unexpected result? The unlikelihood of any such result is merely one more reason why patents should not be granted in situations where the only novelty is in the programming of general purpose digital computers ”. See Sakraida v. Ag. Pro, Inc., 425 U.S. 273 [ 96 S.Ct. 1532, 47 L.Ed.2d 784], 189 USPQ 449 (1976) and A P Tea Co. V. Supermarket Corp ., 340 U.S. 147 [ 71 S.Ct. 127, 95 L.Ed. 162], 87 USPQ 303 (1950). Further, there are no improvements in said machine learning techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process or equipment using the outcome of said techniques. Said machine learning algorithms and computations are done inside of a computer, and do not have a real-world impact and are not tied to the functionality of the computer. Further, there is no evidence that the invention lies in the training phase or execution phase or both. However, machine learning subject matter becomes patent-eligible only when it achieves a technical purpose and, at minimum, offers a technical effect that does more than performing the solution more quickly or efficiently. The general application of machine learning techniques to solve a problem predictably is not eligible for patentability. Furthermore, there is no transformation recited in the claim as understood in view of 35 USC 101. The recited steps merely represent abstract ideas which cannot meet the transformation test because they are not physical objects or substances. Bilski , 545 F.3d at 963. Said steps are nothing more than mere manipulation or reorganization of data, which does not satisfy the transformation prong . It is further noted that the underlying idea of the recited steps could be performed via pen and paper or in a person's mind. Moreover, “ We agree with the district court that the claimed process manipulates data to organize it in a logical way such that additional fraud tests may be performed. The mere manipulation or reorganization of data, however, does not satisfy the transformation prong .” and “ Abele made clear that the basic character of a process claim drawn to an abstract idea is not changed by claiming only its performance by computers, or by claiming the process embodied in program instructions on a computer readable medium. Thus, merely claiming a software implementation of a purely mental process that could otherwise be performed without the use of a computer does not satisfy the machine prong of the machine-or-transformation test ”. CyberSource, 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011) Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because, when considered separately and in combination, the claim elements do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself; do not apply the judicial exception by use of a particular machine; do not effect a transformation or reduce a particular article to a different state or thing; and do not add a specific limitation other than what is well-understood, routine and conventional in the operation of a generic computer. None of the hardware recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Id ., slip op. at 16 (citing Bilski v. Kappos , 561 U.S. 610, 611 (U.S. 2010)). As per “ receiving over a network … data ”, and “ processing the… data by … pre-trained machine learning models ” recitations, these limitations do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment, that is, implementation via computers." Id ., slip op. at 16 (citing Bilski v. Kappos , 561 U.S. 610, 611 (U.S. 2010)). Limiting the claims to the particular technological environment is, without more, insufficient to transform the claim into patent-eligible applications of the abstract idea at their core. Accordingly, claim 13 is not directed to significantly more than the exception itself, and is not eligible subject matter under § 101. ( Step 2B: No ). Further, although the Examiner takes the steps recited in the independent claim as exemplary, the Examiner points out that limitations recited in dependent claims 14-20 further narrow the abstract idea but do not make the claims any less abstract. Dependent claims 14-20 each merely add further details of the abstract steps recited in claim 13 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. These claims "add nothing of practical significance to the underlying idea," and thus do not transform the claimed abstract idea into patentable subject matter. Ultramercial, 772 F.3d at 716. Therefore, dependent claims 14-20 are also directed to non-statutory subject matter. Because Applicant’s apparatus claims 1-12 add nothing of substance to the underlying abstract idea, they too are patent ineligi-ble under §101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy et al. (US 2021/0338134 A1) in view of Perschbacher et al. (US 2019/0231207 A1) . Claims 1 and 13. Chakravarthy et al. (Chakravarthy) discloses a computing device for generating a reprogramming recommendation for an ambulatory medical device, the computing device comprising: one or more processors; and a memory device storing instructions, which when executed by the processor, cause the computing device to perform operations comprising: receiving over a network physiological signal data obtained by the ambulatory medical device; Figs. 1-4; [0036]-[0040] (receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device) processing the received physiological signal data by inputting the physiological signal data into one or more pre-trained machine learning models, each of the one or more pre-trained machine learning models trained to generate an output indicating whether the physiological signal data represents an arrhythmia episode of a particular type; [0041] (applying, by the computing device, a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient) While Chakravarthy does not explicitly teach: upon obtaining an output from the one or more pre-trained machine learning models indicating a detected arrythmia episode of a first type, determining that an on-device arrythmia detection algorithm of the ambulatory medical device did not detect a corresponding arrythmia episode of the first type; and as a result of determining that the on-device arrythmia detection algorithm of the ambulatory medical device did not detect a corresponding arrythmia episode of the first type, generating the reprogramming recommendation for the ambulatory medical device, Chakravarthy discloses: In response to determining that an episode of arrhythmia has occurred in patient 4 , computing system 24 outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia. Computing system 24 may receive, in response to the report, one or more adjustments to one or more parameters used by implantable medical device 10 to sense the cardiac electrogram data of patient 4 and perform such adjustments to implantable medical device 10 for subsequent sensing [0007]; [0042], thereby at least suggesting the recited limitations. Chakravarthy also teaches: medical device system 2 as described herein may allow implantable medical device 10 to act as a low-granularity filter for detecting arrhythmia in patient 4 while offloading power-intensive and computationally-complex validation of arrhythmia detection to an external device, such as external device 12 or computing system 24 . [0044] Further, Perschbacher (Perschbacher) discloses processing the received from an ambulatory medical device a physiological signal data at the external computing device by inputting the physiological signal data into one or more pre-trained machine learning models, each of the one or more pre-trained machine learning models trained to generate an output indicating whether the physiological signal data represents an arrhythmia episode of a particular type; and as a result of determining that the on-device arrythmia detection algorithm of the ambulatory medical device did not detect (missed) a corresponding arrythmia episode of the first type, generating the reprogramming recommendation for the ambulatory medical device. [0007]; [0022]; [0031]; [0053]; [0062]; [0065]; [0068]; [0079]; [0081] Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Chakravarthy to include the recited limitations, as suggested in Chakravarthy and disclosed in Perschbacher, because it would advantageously provide heightened accuracy in the detection and classification of arrhythmia in patient 4 , while reducing power usage and improving battery lifetime of IMD 10 , and to ensure that new or changing arrhythmias are not missed, as specifically stated in Chakravarthy. [0044]; [0109] Claims 2 and 14. The computing device of claim 1, wherein the ambulatory medical device is programmed to operate in a first sensitivity mode of a plurality of sensitivity modes with each sensitivity mode corresponding with a first set of predefined threshold values for use by an on-device arrythmia detection algorithm in detecting arrythmia episodes, and the reprogramming recommendation is a recommendation to program the ambulatory medical device to operate using a second sensitivity mode, the second sensitivity mode having at least one predefined threshold value for use by the on-device arrythmia detection algorithm that is lower than a corresponding predefined threshold value for the first sensitivity mode, thereby providing increased sensitivity for detecting arrhythmia episodes compared to the first sensitivity mode. Chakravarthy, [0032]; [0033]; [0038]; [0051]; [0072]; [0138]; Perschbacher, [0007]-[0009]; [0011]; [0024]; [0026]; [0030]-[0031]; [0046]; [0062]; [0065]; [0068]; [0079]; [0082]. Same rationale as applied to claims 1 and 13. Claims 3 and 15. The computing device of claim 2, wherein the on-device arrythmia detection algorithm of the ambulatory medical device operates in two stages comprising a detection stage and a confirmation stage; wherein i) during the detection stage, the on-device arrythmia detection algorithm analyzes incoming physiological signal data and identifies candidate arrythmia events based on the first set of predefined threshold values associated with the first sensitivity mode, ii) upon identifying a candidate arrythmia event during the detection stage, the ambulatory medical device begins recording and storing a segment of the physiological signal data corresponding to the time of the initial candidate arrythmia event detection, iii) during the confirmation stage, the on-device arrythmia detection algorithm analyzes the stored segment of physiological signal data corresponding to the candidate arrythmia event to confirm whether the event meets criteria for a confirmed arrythmia episode based on the first set of predefined detection algorithm threshold values associated with the first sensitivity mode, and iv) changing the programming of the ambulatory medical device from the first sensitivity mode to the second sensitivity mode results in changing to a second set of predefined detection algorithm threshold values used by one or both of the detection stage and confirmation stage. Chakravarthy, [0053]-[0055]; [0059]; Perschbacher, [0007]-[0009]; [0011]; [0024]; [0026]; [0030]-[0031]; [0046]; [0062]; [0065]; [0068]; [0079]; [0082]. Same rationale as applied to claims 1 and 13. Claims 4 and 16. The computing device of claim 1, wherein the physiological signal data obtained by the ambulatory medical device is stored locally on the ambulatory medical device until a wireless connection is established between the ambulatory medical device and an intermediary device, and upon establishing the wireless connection with the intermediary device, the physiological signal data is transmitted from the ambulatory medical device to the intermediary device and then transmitted from the intermediary device to the computing device over a network. Chakravarthy, Figs. 1-2 Claims 5 and 17. The computing device of claim 1, wherein processing the received physiological signal data comprises: inputting the physiological signal data into a trained machine learning model to generate by the trained machine learning model a plurality of confidence scores, each confidence score indicating a likelihood that the physiological signal data represents an arrhythmia episode of a particular type of arrythmia; and for a first type of arrythmia, comparing the confidence score for the first type of arrhythmia to a confidence threshold to determine whether an episode of the first arrhythmia type is detected in the physiological signal data. Chakravarthy; [0072]; [0089]; [0090]; [0107]-[0111]; [0138]; Perschbacher, [0058]; [0062]; [0065]; [0079]; [0085]. Same rationale as applied to claims 1 and 13. Claims 6 and 18. The computing device of claim 1, wherein processing the received physiological signal data comprises: inputting the physiological signal data into each of a plurality of trained machine learning models, each trained machine learning model trained to detect a different arrhythmia type; obtaining an output from each of the plurality of trained machine learning models indicating whether the physiological signal data represents an episode of the arrhythmia type the model is trained to detect; and determining that the physiological signal data represents an episode of the first arrhythmia type based on the output of the one of the plurality of trained machine learning models trained to detect the first arrhythmia type. Chakravarthy, [0007]; [0042]; [0053]-[0055]; [0059]; Perschbacher, [0007]-[0009]; [0011]; [0024]; [0026]; [0030]-[0031]; [0046]; [0062]; [0065]; [0068]; [0079]; [0082]. Same rationale as applied to claims 1 and 13. Claims 7 and 19. The computing device of claim 1, wherein the first type of arrhythmia episode is an atrial fibrillation episode and the reprogramming recommendation is a recommendation to reprogram the ambulatory medical device to use a sensitivity setting having predefined threshold values for an on-device arrythmia detection algorithm that are more sensitive for detecting atrial fibrillation episodes. Chakravarthy, [0028]; [0036]; [0055]; [0071]; Perschbacher, [0011]; [0024]; [0046]; [0062]; [0065]; [0068]; [0079]; [0080]-[0082]. Same rationale as applied to claims 1 and 13. Claims 8 and 20. The computing device of claim 7, wherein increased sensitivity for detecting atrial fibrillation episodes by the on-device arrythmia detection algorithm is achieved by modifying one or more of: decreasing an R-R interval irregularity threshold used by the ambulatory medical device to declare an atrial fibrillation episode; decreasing a density index threshold calculated from R-R intervals that must be satisfied to declare an atrial fibrillation episode; decreasing a minimum atrial fibrillation episode duration threshold. Chakravarthy, [0026]; [0053]; [0073]; Perschbacher, [0007]; [0011]; [0026]; [0062]-[0065]. Same rationale as applied to claims 1, 7, 13 and 20. Claim 9. The computing device of claim 1, wherein the output from the one or more machine learning models indicates that the physiological signal data contains an arrythmia episode comprising one of premature ventricular contractions (PVCs) or premature atrial contractions (PACs); and based on the indication of PVCs/PACs in the physiological signal data, the reprogramming recommendation comprises modifying one or more predefined threshold values used by an on-device arrythmia detection algorithm of the ambulatory medical device to detect an episode of PVC/PAC. Chakravarthy, [0007]; [0042]; [0077]; [0107]; Perschbacher, [0007]; [0022]; [0031]; [0053]; [0062]; [0065]; [0068]; [0079]; [0081]. Same rationale as applied to claim 1. Claim 10. The computing device of claim 1, wherein the output from the one or more machine learning models indicates that the physiological signal data contains T-wave oversensing (TWOS); and based on the indication of TWOS in the physiological signal data, the reprogramming recommendation comprises a recommendation to program the ambulatory medical device to operate with a sensitivity mode having a different refractory period to avoid oversensing of T-waves. Chakravarthy, [0026]; [0054]. Claim 11. The computing device of claim 1, wherein generating the reprogramming recommendation comprises: determining that the output from the one or more machine learning models indicates detection of the arrhythmia episode of the first type a predetermined plurality of times; wherein the reprogramming recommendation is generated only after the predetermined plurality of times that the arrhythmia episode of the first type is detected by the one or more machine learning models. Chakravarthy, [0007]; [0042]; Perschbacher, [0007]; [0022]; [0031]; [0053]; [0062]; [0065]; [0068]; [0079]; [0081]. Same rationale as applied to claim 1. Distinguishable Subject Matter Claim 12 remains distinguishable from the prior art of record. The closest prior art of record, Chakravarthy and Perschbacher, fails to disclose the following limitations recited in claim 12: “generating the reprogramming recommendation comprises: incrementing a counter each time the output from the one or more machine learning models indicates detection of the arrhythmia episode of the first type; and determining that the counter exceeds a threshold number of days, wherein the counter indicates detection of the arrhythmia episode of the first type on consecutive days; wherein the reprogramming recommendation is generated only after the counter indicates that the arrhythmia episode of the first type is detected on the consecutive days.” Citations of pertinent art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bryce Alexander , MD; Adrian Baranchuk, MD “Remote Device Reprogramming”, Circ Arrhythm Electrophysiol. 2020;13:e008949. DOI: 10.1161/CIRCEP.120.008949 pp. 1233-1235, discloses various aspects of remote medical devices reprogramming based on specific parameters . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Igor Borissov whose telephone number is 571-272-6801. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor Kambiz Abdi can be reached on 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Center system. For more information about the Patent Center, https://patentcenter.uspto.gov. Should you have questions on access to the Patent Center system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /IGOR N BORISSOV/Primary Examiner, Art Unit 3685 5/23/2026 Application/Control Number: 18/952,210 Page 2 Art Unit: 3685 Application/Control Number: 18/952,210 Page 3 Art Unit: 3685 Application/Control Number: 18/952,210 Page 4 Art Unit: 3685 Application/Control Number: 18/952,210 Page 5 Art Unit: 3685 Application/Control Number: 18/952,210 Page 6 Art Unit: 3685 Application/Control Number: 18/952,210 Page 7 Art Unit: 3685 Application/Control Number: 18/952,210 Page 8 Art Unit: 3685 Application/Control Number: 18/952,210 Page 9 Art Unit: 3685 Application/Control Number: 18/952,210 Page 10 Art Unit: 3685 Application/Control Number: 18/952,210 Page 11 Art Unit: 3685 Application/Control Number: 18/952,210 Page 12 Art Unit: 3685 Application/Control Number: 18/952,210 Page 13 Art Unit: 3685 Application/Control Number: 18/952,210 Page 14 Art Unit: 3685 Application/Control Number: 18/952,210 Page 15 Art Unit: 3685 Application/Control Number: 18/952,210 Page 16 Art Unit: 3685 Application/Control Number: 18/952,210 Page 17 Art Unit: 3685 Application/Control Number: 18/952,210 Page 18 Art Unit: 3685 Application/Control Number: 18/952,210 Page 19 Art Unit: 3685 Application/Control Number: 18/952,210 Page 20 Art Unit: 3685 Application/Control Number: 18/952,210 Page 21 Art Unit: 3685 Application/Control Number: 18/952,210 Page 22 Art Unit: 3685 Application/Control Number: 18/952,210 Page 23 Art Unit: 3685 Application/Control Number: 18/952,210 Page 24 Art Unit: 3685 Application/Control Number: 18/952,210 Page 25 Art Unit: 3685 Application/Control Number: 18/952,210 Page 26 Art Unit: 3685 Application/Control Number: 18/952,210 Page 27 Art Unit: 3685 Application/Control Number: 18/952,210 Page 28 Art Unit: 3685 Application/Control Number: 18/952,210 Page 29 Art Unit: 3685
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