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 .
Formal Matters
Applicant's response, filed 27 August 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-3, 6-9, 11-16, and 20 are currently pending and have been examined.
Claims 1, 7-8, 11-12, 16, and 20 have been amended.
Claims 4-5, 10, and 17-19 have been canceled.
Claims 1-3, 6-9, 11-16, and 20 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 10 January 2023 claiming benefit to Provisional Application 63/479,339.
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 1-3, 6-9, 11-16, and 20 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.
Step 1 – Statutory Categories of Invention:
Claims 1-3, 6-9, 11-16, and 20 are drawn to a system or method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for estimating a recall probability for a medical device in part performing the steps of generate a network of medical devices having a relationship to a focal medical device using the predicate device database; using the generated network to form features; and applying the features to a plurality of Graph Convolution Networks to determine the recall probability wherein at least one of the plurality of Graph Convolution Networks is constructed for a 1-hop network and one of the Graph Convolution Networks is constructed for a 2-hop network.
Independent claim 8 recites a method for estimating a recall probability for a focal medical device in part performing the steps of (a) generating a predicate device network for a focal medical device; (b) using the predicate device network to generate features for the focal medical device; (c) applying the features to a predictive model, wherein the predictive model has been trained on training data to estimate a medical device recall probability from features associated with a predicate device network, wherein the predictive model comprises a plurality of branches, each branch being associated with a different number of hops in the predicate device network; and (c) outputting a probability that the focal medical device will be recalled within a time window generated by the predictive model.
Independent claim 16 recites a method in part performing the steps of applying features to a Graph Convolution Network having an adjacency matrix that is determined from a predicate device network for a focal medical device wherein applying the features to a Graph Convolution Network comprises applying the features to a first Graph Convolution Network and a second Graph Convolution Network, wherein an adjacency matrix for the first Graph Convolution Network is determined from the predicate device network by forming a 1-hop network consisting of only medical devices that are one hop away from the focal medical device in the predicate device network and wherein an adjacency matrix of the second Graph Convolution Network is determined from the predicate device network by forming a 2-hop network consisting of only medical devices that are two hops away from the focal medical device in the predicate device network; and using the output of the Graph Convolution Network to determine a probability of the focal medical device being recalled.
These steps of collecting and mathematically modeling device recall data to predict a recall probability/event amount to a mathematical concept which includes mathematical relationships, mathematical formulas or equations, and mathematical calculations. The mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships).
Dependent claim 2 recites, in part, wherein each relationship between two medical devices in the predicate device database is a predicate relationship wherein one of the two medical devices has been listed as a predicate of another of the two medical devices.
Dependent claim 3 recites, in part, wherein using the generated network to form features further comprises retrieving and using recall data for medical devices in the generated network to form the features.
Dependent claim 6 recites, in part, herein a first Graph Convolution Network is for a 1-hop network at a first time point and a second Graph Convolution Network is for the 1-hop network and a second time point.
Dependent claim 7 recites, in part, wherein the first Graph Convolution Network and the Second Convolution Network provide respective outputs to a Gated Recurrent Unit.
Dependent claim 9 recites, in part, applying the features to a second predictive model associated with a second time window and outputting a probability that the focal medical device will be recalled within the second time window generated by the second predictive model.
Dependent claim 11 recites, in part, wherein each branch of the predictive model comprises a Graph Convolution Network trained for the number of hops associated with the branch.
Dependent claim 12 recites, in part, wherein the plurality of branches receive temporal features and a second plurality of branches receive static features.
Dependent claim 13 recites, in part, wherein each branch of the first plurality of branches comprises a plurality of Graph Convolution Networks, wherein each Graph Convolution Network along a branch is associated with a separate time point.
Dependent claim 14 recites, in part, wherein the predictive model further comprises a sequence processing model that receives the outputs of the Graph Convolution Networks at the separate time points.
Dependent claim 15 recites, in part, herein using the predicate device network to generate features for the focal medical device further comprises using recall data for medical devices in the predicate device network to generate the features.
Dependent claim 20 recites, in part, applying features to a third Graph Convolution Network having an adjacency matrix that is determined from the predicate device network by forming a 1-hop network consisting of only medical devices that are one hop away from the focal medical device in the predicate device network, wherein the third Graph Convolution Network is associated with a different time point than the first Graph Convolution Network; and using the output of the third Graph Convolution Network to determine the probability of the focal medical device being recalled.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1, 8, and 16 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 8 recites a computer system. Claim 1 recites a processor and predicate database in communication. The specification defines the computer and corresponding hardware as any device suitable for the intended purpose (see the instant specification in ¶ 0081-85). The use of a computer system, processor, and predicate database serve as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 8 recites a computer system. Claim 1 recites a processor and predicate database in communication.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “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)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-3, 6-9, 11-16, and 20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 27 August 2025 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that Graph Convolution Network is not a mathematical concept because the claim does not directly recite an equation or formula. Examiner is not persuaded. Under MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships may be expressed in words or using mathematical symbols. One of ordinary skill in the art would recognize that a graph convolutional network is a mathematical representation of data relationships defined by known formulas (see Thomas Kipf, Graph Convolutional Networks, GitHub (Sept. 30, 2016). It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas).
Next, Applicant asserts that the claims are directed towards an improvement to computer or technology via an improvement to the smoothing operations of the graph convolutional network by reducing the one-hop adjacency matrix calculations for each layer of the GCN. Without conceding the unconventionality of the GCN structure solution, Examiner disagrees that this amounts to an improvement to technology/computers. An improvement to the abstract idea of graph convolution network structure does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to reducing over smoothing of a mathematical representation of a data set. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
The same legal analysis applies to Applicants arguments regarding independent claim 8 and 16 wherein Applicant has attempted to include different elements/embodiments of the graph convolution network. As stated above, Examiner is not persuaded that a graph convolution network is not a mathematical relationship under Step 2A Prong 1 wherein layers are propagated by a widely recognized math equation disclosed in Applicant’s own specification in ¶ 0055-56. Furthermore, the design structure of the GCN would amount to an improvement to the abstract idea and therefore not a practical application via an improvement to computers/technology under Step 2A Prong 2.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN JACKSON whose telephone number is (571) 272-5389 and fax number is (571) 273-1626. The examiner can normally be reached on Monday – Thursday, 6:30 AM - 4:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long, can be reached on (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 3682