DETAILED ACTION
This Non-Final Office action is in response to Applicant’s Response on 09/25/2026. Claims 1-15 are pending; claims 9-13 are withdrawn; and claims 1-8 and 14-15 are examined below. The effective filing date of the claimed invention is 12/30/2021.
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 1-8, 14-15 are rejected under 35 U.S.C. 101 because the claims are found to be directed to abstract idea.
Step 1 – Claims 1-8 relate to process claims; and claims 14-15 are system/machine claims. Step 1 is satisfied.
Step 2A Prong 1 – Exemplary claim 1 recites the abstract idea of:
a prediction model computerized module implements (MPEP 2106.04(a)(2)(I))
the at least one first prediction model to obtain a prediction output according to the first prediction model (MPEP 2106.04(a)(2)(I)(A)), and
at least one other prediction model to obtain a prediction output according to this other prediction model (MPEP 2106.04(a)(2)(I)(A)),
a similarity computerized module implements a similarity scoring method to determine a similarity score between (MPEP 2106.04(a)(2)(I)):
the prediction output of the at least one first prediction model (MPEP 2106.04(a)(2)(I)(A))and
the prediction output of the at least one other prediction model (MPEP 2106.04(a)(2)(I)(A)),
a reliability computerized module implement an reliability model which determines the reliability of the prediction output of the at least one first prediction model based on the determined similarity score (MPEP 2106.04(a)(2)(I)(A), (C)).
When viewed alone and in ordered combination, these limitations are found to recite abstract idea.
Step 2A Prong 2 – Claim 1 is not found to integrate the abstract idea into practical application. The additional elements relate to the computerized aspects of the method and potentially the machine learning aspects. However, these limitations are recited in an “apply it” manner. For the machine learning limitations, see also Recentive v. Fox (Fed. Cir. 2025), where such limitations were found to be directed to abstract idea.
Accordingly, when viewed alone and in ordered combination, these limitations are found to be directed to abstract idea.
Step 2B – Claim 1 is not found to include significantly more. The additional element analysis of Step 2A Prong 2 is equally applied in Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. See MPEP 2106.05(d).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) 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.”);
Accordingly, when viewed alone and in ordered combination, claim 1 (and 14) is found to be directed to abstract idea.
Dependent Claims – Claim 2-8 recite more abstract idea. See MPEP 2106.04(a)(2)(I); MPEP 2106.04(a)(2)(III). Claim 15 recites more abstract idea performed in “apply it” manner.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 4-8, 14, 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US2022/0121902 to Hann (“Hann”)
With regard to claims 1, 14, Hann discloses the claimed computerized method for determining the reliability of a prediction output of at least one machine-learned or deep-learned first prediction model (Hann predicts accuracy of individual neural-network outputs [0055-58] [0060-61] Figs. 4-5, where the predicted accuracy is the proposed mapping to reliability), the method comprising:
a prediction model computerized module implements
the at least one first prediction model to obtain a prediction output according to the first prediction model (Hann [0060-61] In this example the system comprises a plurality of neural networks 11, 12, 13 which operate in parallel. A neural network is an example of a data processing operation that accepts an input, performs a number of calculations on the input and finally outputs an output based on the operations performed. Each neural network 11, 12, 13 may be arranged in a number of layers 100, 200, 300, each of which comprises a plurality of nodes at which a mathematical operation is performed on the data.; where neural network 11 processing input and produces an output, its parameters are learned through training), and
at least one other prediction model to obtain a prediction output according to this other prediction model (Hann, additional neural networks 12 and 13 operate in parallel to neural network 11 and produce respective outputs [0060] [0069-70] Figs. 5-6),
a similarity computerized module implements a similarity scoring method to determine a similarity score between:
the prediction output of the at least one first prediction model and
the prediction output of the at least one other prediction model (Hann calculates similarity between respective outputs using the Dice similarity coefficient DSC [0074-76] Fig. 8; For each of the set of outputs, the degree of similarity between that output and the other outputs may be calculated. The degree of similarity may be calculated by any method that is suitable for calculating a difference between two sets of data, such as the range or standard deviation of the at least two outputs. The degree of similarity may be obtained using the Dice similarity coefficient (DSC), which is defined by the following equation.),
a reliability computerized module implement an reliability model which determines the reliability of the prediction output of the at least one first prediction model based on the determined similarity score (Hann [0076-79] Fig. 8, step 55 – a trained linear regression Rm receives inter-output similarity scores and predicts accuracy for the particular output m.).
8. With regard to claim 4, Hann further discloses
[[-]] the at least one first prediction model and
[[-]] the at least one other prediction model, wherein the at least one other prediction model is a machine-learned or deep-learned individual prediction model (e.g. Hann [0060-61] [0082-83] Figs. 5-6 multiple individual neural networks are independently trained and generate their own segmentation outputs),
the method comprising
[[-]] the reliability computerized module implements the reliability model which determines the reliability of the prediction outputs based on the determined similarity score (e.g. Hann [0076-79]).
With regard to claim 5, Hann further discloses where the at least one first prediction model and the at least one other prediction model have a different model architecture (see Hann Fig. 5, first neural network 11, second neural network 12, and third neural network 13, with expressly different layers and/or skip connections. These are structural differences in architecture.).
With regard to claim 6, Hann further discloses a aggregation computerized module aggregates the prediction outputs into an aggregated prediction output (Hann [0071-72] Fig. 6 step 44).
With regard to claim 7, Hann further discloses where the aggregation computerized module aggregates the prediction outputs into an aggregated prediction output using a truth discovery method such as: majority voting, weighted voting (Hann [0071] Fig. 6 step 44, discloses an agreement map that treats each network’s pixel classification as a vote, counts the agreement among the predictions, and assigns the aggregated pixel classification when a predetermined agreement threshold is met), bayesian method, or information retrieval method.
With regard to claim 8, Hann further discloses where the at least first prediction model and the at least one other prediction model are implemented on input datas from measurements or acquisitions made by one or more measurement or acquisition system (Hann [0068-69] Fig. 6, step 41, 42).
With regard to claim 15, Hann further discloses determining the reliability of a prediction output of at least one machine-learned or deep-learned first prediction model, wherein the computer program is adapted, when run on a processor, to cause the processor (Hann [0033-34] claim 20-21, [0060-61] [0074-79]).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
17. Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hann in view of Ronneberger (2015). U-Net: convolutional networks for biomedical image segmentation. Cited as arXiv:1505.04597 (referred to as “Ronneberger”).
With regard to claim 2, Hann further discloses [0070] Fig. 6 where each neural network outputs a candidate segmentaion classifying each pixel as belonging to the feature or not; Further Hann compares the segmentation outputs using DSC [0074-75]. Hann does not disclose the flattening the image into a vector. See Ronneberger section 2, section 3 – produces class probabilities at each pixel, the K probabilities form a simplex probability vector because they are nonnegative and sum to one, section 4 – expressly describes a predicted membrane probability map whose pixel probabilities can be represented as an N-component vector. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hann to include such N-dimentional vector, as described in Ronneberger, as this reference was incorporated by reference by Hann at [0060], where Hann states the following:
A more specific example of a data analysis system using machine learning or artificial intelligence as the data processors is shown in FIG. 5. This may be used in an image segmentation task as mentioned above. This system is based on the U-net architecture disclosed in “U-Net: Convolutional Networks for Biomedical Image Segmentation” by O. Ronneberger et al. (arXiv:1505.04597v1 [cs.CV] 18 May 2015) which is hereby incorporated by reference. In this example the system comprises a plurality of neural networks 11, 12, 13 which operate in parallel. . . .
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hann in view of US2022/0101626 to Lenga (“Lenga”).
With regard to claim 3, Hann discloses where the similarity scoring method uses the Dice Method. Hann permits any suitable method for calculating differences between outputs. Hann does not disclose the various methods recited. See Lenga at [0020-21] published claim 1, teaches KL divergence for confidence assessment and describes straightforward implementations for categorical-result distribution. This supports selecting KL when comparing probabilistic or categorical predictions. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hann with Lenga’s KL divergence, where this is beneficial in that helps facilitate straightforward implementations for categorical-result distributions. Lenga, throughout, and [0020-21], published claim 1.
Conclusion
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/PETER LUDWIG/ Primary Examiner, Art Unit 3627
