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 .
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to patent eligible subject matter. Based upon consideration of all of the relevant factors with respect to the claim as a whole, the claims are determined to be directed to a judicial exception, specifically an abstract idea, without significantly more.
Step 1
The claimed inventions in claims 1-23 are directed to statutory subject matter as the claim(s) recite(s) a method and a system of detecting pause episodes of device classified arrhythmias.
Step 2A, Prong One
Claims 1 and 18 recite the following steps or instructions for “obtaining device classified arrhythmia data sets”, “applying a convolutional neural network model …to the DCA data sets…”, “presenting information concerning…the DCA data sets”, which is grouped as a mental process in MPEP 2106.04(a)(2)(III) and mathematical concept in MPEP 2106.04(a)(2)(I).
For example, the limitations concern data set obtaining, applying a model to those data sets, and presenting information concerning the subset of the data sets, directed to mental processes of performing concepts in a human mind or by a human using a pen and paper and mathematical concepts. These limitations are nothing more than a medical professional obtaining a previously recorded or test parameters of data sets, applying a model trained to detect pause episodes – where the examiner notes that the claim does not provide any details on the model other than a name “convolutional neural network” which encompasses a simple model such as comparing the data sets to a threshold, followed by the medical professional presenting the results verbally or manually by hand. The examiner notes the step of “applying a convolutional neural network model …to the DCA data sets…” is directed to mathematical concept of applying a model analysis to the data sets.
Accordingly, each of the above-identified claims recites an abstract idea as in MPEP 2106.04(a).
In addition, Claims 1 and 18 recite additional elements of a processor, and claim 1 alone recites additional elements of a memory and display.
Step 2A, Prong Two
The above-identified abstract idea in each of independent Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) is not integrated into a practical application under MPEP 2106.04(d) because the additional elements (identified above in independent Claims 1 and 18), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use according to MPEP 2106.05(h) and appear to be extra solution activity where data to be analyzed by the abstract idea is acquired or obtained.
More specifically, the additional elements of: a processor, memory, and display as noted above, are generically recited computer elements in independent Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) which do not improve the functioning of a computer, or any other technology or technical field according to MPEP 2106.04(d)(1) and 2106.05(a). Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine according to MPEP 2106.05(b), effect a transformation according to MPEP 2106.05(c), provide a particular treatment or prophylaxis according to MPEP 2106.04(d)(2) or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception according to MPEP 2106.04(d)(2) and 2106.05(e). Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer in accordance with MPEP 2106.05(f). For at least these reasons, the abstract idea identified above in independent Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) is not integrated into a practical application in accordance with MPEP 2106.04(d).
Moreover, the above-identified abstract idea is not integrated into a practical application in accordance with MPEP 2106.04(d) because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and using mathematical concepts) using rules (e.g., computer instructions) executed by a computer (e.g. processor and memory as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer according to MPEP 2106.05(f). Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims according to MPEP 2106.05(a). That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) is not integrated into a practical application under MPEP 2106.04(d)(I).
Accordingly, independent Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) are each directed to an abstract idea according to MPEP 2106.04(d).
Step 2B
Claims 1 and 18 do not include additional elements that are sufficient to amount to significantly more than the abstract idea in accordance with MPEP 2106.05 for at least the following reasons:
These claims require the additional elements of: a processor, memory, and display.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, MPEP 2106.05(d)(II) along with Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification: a processor, memory, and display are described in the disclosure as a component that is generic and conventionally used and known in the art – such as a workstation or cell phone, etc. [¶¶ 252 – published app]. Accordingly, in light of Applicant’s specification, a processor, memory, and display and their function are considered well-understood routine and conventional in the art.
Additionally, the claimed terms processor, memory, and display are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process. See MPEP 2106.05(f).
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the processor, memory, display, etc.. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see MPEP 2106.05(d)(I)(2) and 2106.07(a)(III)). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications along with MPEP 2106.05(d)(I)).
The recitation of the above-identified additional limitations in Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See MPEP 2106.05(f) along with Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. See MPEP 2106.05(a) along with McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, per MPEP 2106.05(a), the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the system and method of Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself or providing a technical solution to a problem in a technical field according to MPEP 2106.05(a), or (ii) providing meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e).
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in Claims 1 and 18 (and respective dependent claims 2-17 and 19-21) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment according to MPEP 2106.05(h). When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment according to MPEP 2106.05(h). When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself according to MPEP 2106.04(d)(2) and 2106.05(e). Moreover, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity according to MPEP 2106.05(g). As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application as required by MPEP 2106.05.
Regarding dependent claims 2-17 and 19-21, the limitations of these claims further define limitations directed to the abstract idea.
As such, claims 1-23 when analyzed as a whole, do not appear to be patent eligible for the reasons set forth above.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cheng et al. (WO-2022251145-A1; hereinafter “Cheng”).
Regarding claim 1, Cheng discloses a system for declaring pause in cardiac activity, comprising: memory to store specific executable instructions and a convolutional neural network (CNN) model trained to detect pause episodes, the CNN model comprising a global average pooling (GAP) layer (e.g. ¶¶ 102); one or more processors configured to execute the specific executable instructions to: obtain device classified arrhythmia (DCA) data sets generated by an implantable medical device (IMD) for corresponding candidate pause episodes declared by the IMD, the DCA data sets including cardiac activity (CA) signals for one or more beats sensed by the IMD (e.g. ¶¶ 76-86); and apply the CNN model to the DCA data sets to identify a valid subset of the DCA data sets that correctly characterizes the corresponding CA signals (e.g. ¶¶ 112-119); and a display configured to present information concerning the valid subset of the DCA data sets (e.g. ¶¶ 74, etc.).
Regarding claim 18, Cheng discloses a computer implemented method to confirm device documented (DD) pause episodes, comprising: under control of one or more processors configured with specific executable instructions, obtaining device classified arrhythmia (DCA) data sets generated by an implantable medical device (IMD) for corresponding candidate pause episodes declared by the IMD, the DCA data sets including cardiac activity (CA) signals for one or more beats sensed by the IMD (e.g. ¶¶ 76-86); applying a convolutional neural network (CNN) model trained to detect pause episodes to the DCA data sets to identify a valid subset of the DCA data sets that correctly characterizes the corresponding CA signals (e.g. ¶¶ 112-119); and presenting information concerning the valid subset of the DCA data sets (e.g. ¶¶ 74, etc.).
Regarding claims 2 and 19, Cheng discloses the one or more processors are further configured to apply the CNN model to the DCA data sets to identify an invalid subset of the DCA data sets that incorrectly characterizes the corresponding CA signals, wherein the valid subset of the DCA data sets indicates a portion of the DCA data sets that indicates true pause, and the invalid subset of the DCA data sets indicates a second portion of the DCA data sets that indicates false pause (e.g. ¶¶ 108-114).
Regarding claims 3 and 20, Cheng discloses the information is indicative of a recommendation for i) a change in a treatment for a patient associated with the IMD that will increase the DCA data sets identified as the valid subset of the DCA data sets, or ii) a change in a treatment associated with the IMD that will increase the DCA data sets identified as the valid subset of the DCA data sets (e.g. ¶¶ 130).
Regarding claims 4 and 21, Cheng discloses the one or more processors are further configured to apply the CNN model to the DCA data sets to identify an invalid subset of the DCA data sets that incorrectly characterizes the corresponding CA signals, wherein in response to the change in the treatment for the patient or the IMD, the one or more processors are further configured to: obtain second DCA data sets generated by the IMD; apply the CNN model to the second DCA data sets to identify a second invalid subset of the DCA data sets that incorrectly characterizes the corresponding CA signals; and confirm that the second invalid subset of the DCA data sets has fewer invalid candidate pause episodes compared to the invalid subset of the DCA data sets (e.g. ¶¶ 83, 108-114, etc.).
Regarding claim 5, Cheng discloses the CNN model comprises at least two 1-dimensional (1D) convolutional layers, the GAP layer configured to receive outputs from each of the at least two 1D convolutional layers, wherein the one or more processors are further configured to process an output of a first 1D convolutional layer using i) a rectified linear unit activation function, ii) a batch normalization function, or iii) a dropout function (e.g. ¶¶ 50-53).
Regarding claim 6, Cheng discloses the CNN model comprises at least first and second 1D convolutional layers, wherein the one or more processors are further configured to: normalize an output of the first 1D convolutional layer; and send the normalized output of the first 1D convolutional layer to the second 1D convolutional layer (e.g. ¶¶ 24-27).
Regarding claim 7, Cheng discloses the CNN model further comprises a fully connected layer configured to receive input from the GAP layer (e.g. ¶¶ 40-43).
Regarding claim 8, Cheng discloses the CNN model outputs, in connection with each of a plurality of the DCA data sets, a confidence indicator indicative of a degree of confidence that the corresponding DCA data set represents a true positive or false positive designation of pause (e.g. ¶¶ 50-53).
Regarding claim 9, Cheng discloses the one or more processors are further configured to compare the confidence indicator to a detection threshold, wherein the information further comprises information concerning the valid subset of the DCA data sets that exceed the detection threshold (e.g. ¶¶ 83).
Regarding claim 10, Cheng discloses in response to an adjustment to the detection threshold, the one or more processors are further configured to present the information concerning the valid subset of the DCA data sets that exceed the adjusted detection threshold (e.g. ¶¶ 61).
Regarding claim 11, Cheng discloses the CNN model comprises five 1-dimensional (1D) convolutional layers, the GAP layer configured to receive outputs from each of the at least two 1D convolutional layers (e.g. ¶¶ 44).
Regarding claim 12, Cheng discloses the CNN model represents a model that is trained utilizing an augmented collection of DCA data sets, wherein the augmented collection of the DCA data sets includes reference DCA data sets from patients and synthetic DCA data sets that are generated based on the reference DCA data sets (e.g. ¶¶ 50-53).
Regarding claim 13, Cheng discloses the synthetic DCA data sets are generated using i) noise addition, ii) signal inversion, iii) magnification, iv) inserting one or more p waves during a pause interval at a previous R-R interval, v) stretching or shrinking a duration of a pause interval, or vi) two or more of noise addition, signal inversion, magnification, inserting one or more p waves during a pause interval at a previous R-R interval, or stretching or shrinking a duration of a pause interval (e.g. ¶¶ 53).
Regarding claim 14, Cheng discloses the information is indicative of a recommendation for adjustment to a sensing parameter or a therapy parameter associated with the IMD (e.g. ¶¶ 44, 61, 68, etc.).
Regarding claim 15, Cheng discloses the IMD comprising: a combination of subcutaneous electrodes configured to collect the CA signals; IMD memory configured to store program instructions; and one or more IMD processors configured to execute the program instructions to: analyze the CA signals and based on the analysis declare candidate pause episodes; and generate the DCA data sets including the corresponding CA signals; and a transceiver configured to wirelessly transmit the DCA data sets to an external device (e.g. ¶¶ 43, 59, etc.).
Regarding claim 16, Cheng discloses an external device that includes the memory and the one or more processors and a transceiver, the transceiver configured to wirelessly receive the DCA data sets from the IMD (e.g. ¶¶ 43).
Regarding claim 17, Cheng discloses a server that includes the memory and the one or more processors, the memory configured to store a collection of the DCA data sets, the one or more processors configured to apply the CNN model to the collection of the DCA data sets (e.g. ¶¶ 50-53).
Regarding claim 22, Cheng discloses tuning the CNN model by inputting, into a layer of the CNN model, at least one of i) age-related feature, ii) gender, iii) body mass index, iv) ethnicity, v) medical history, vi) medication usage, vii) lifestyle factor, or viii) family history (e.g. ¶¶ 25, 45, etc.).
Regarding claim 23, Cheng discloses the CNN model comprises at least two layers, the method further comprising: freezing one of the at least two layers; and updating, using additional DCA data sets, at least one layer of the CNN model that is not frozen (e.g. ¶¶ 50-53).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael D’Abreu whose telephone number is (571) 270-3816. The examiner can normally be reached on 7AM-4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J D'ABREU/Primary Examiner, Art Unit 3796
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