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 .
Election/Restrictions
Applicant's election with traverse of Species I (Fig. 11) in the reply filed on August 17, 2026 is acknowledged. The traversal is on the ground(s) that determined landmark values are subsequently used to perform the operation of the remaining species. This is not found persuasive because an analysis system, determining a type of hearing instrument based on an ear shape and modeling a hearing instrument are not required nor used to predict an aperture plane based on determined landmark values.
The requirement is still deemed proper and is therefore made FINAL.
Claims 9-17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on August 17, 2026.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Claim(s) 1, 5-8 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fred Mcbagonluri et al., EP 2051187A2.
Independent claim 1, Mcbagonluri discloses the discloses a method comprising: obtaining, by one or more processors implemented in circuitry, ear modeling data representing a 3-dimensional (3D) impression of an ear surface of an ear of a patient (i.e. loading the impression data - Para 10); and
determining, by the one or more processors, based on the ear modeling data, values of one or more landmarks of the ear (i.e. The Angular measurements (Bend and Taper) provide impression anatomic variability measurements. – Para 36; The slice characteristics (i)-(iv) provide a classification basis of the cross sectional areas and allows for convergence to characterize shell features such as aperture, first and second bends, and concha – Para 37), wherein determining the values of the one or more landmarks comprises:
predicting, by the one or more processors, an ear aperture plane of the ear (i.e. Variability in eccentricity determines the approximate shape of the cross sectional area of the slices. – Para 26; provide a classification scheme for classifying hearing instruments impression with the objective of being able to adapt algorithmic robustness for the different classes of impressions realizable from these classifications - Para 33);
determining, by the one or more processors, a plurality of cross-sectional planes that are aligned with the ear aperture plane (i.e. The slice characteristics (i)-(iv) provide a classification basis of the cross sectional areas - Para 37);
for each of the cross-sectional planes:
determining, by the one or more processors, an intersection boundary of the cross-sectional plane representing a line of intersection between the cross-sectional plane and the ear (i.e. The cross-section of impression slices 30 is presumed to be that of a conic section for which eccentricities can be computed for successive slices of the impression canal. Variability in eccentricity determines the approximate shape of the cross sectional area of the slices – Para 26, 33); and
determining, by the one or more processors, a centroid of the intersection boundary of the cross-sectional plane (i.e. bending can be observed when a centerline is estimated along the geometric center of the impression – Para 16; the Resolved Bend Angles indicating twists of the impression along the centerline – Para 35); and
determining, by the one or more processors, the values of the one or more landmarks based on the centroids (i.e. Angular Measurements of Orientation along the impression are characterized by: (1) the tapering angle of the impression along the aperture, first, and second bend contour slices; (2) the Resolved Bend Angles indicating twists of the impression along the centerline – Para 35).
Claim 5, Mcbagonluri discloses the method of claim 1, wherein the landmarks include one or more ear canal landmarks (i.e. The canal segment of the impression is sliced, and various parameters are determined according to each slice – abstract; the canal angular shape is determined. The following principal angles are used to characterize changes in ear canal shape towards the inner ear – Para 16).
Claim 6, Mcbagonluri discloses the method of claim 5, wherein the ear canal landmarks include of: a location of a first bend of the car canal, a location of a second bend of the car canal, an angle of the first bend of the ear canal, an angle of the second bend of the ear canal (i.e. the bend angles – Para 16), a center line of the ear canal, a length of the ear canal (i.e. bend angle refers to a change in direction in the canal length – Para 16), or a width of the ear canal.
Claim 7, Mcbagonluri discloses the method of claim 1, wherein the landmarks further include one or more outer ear landmarks (i.e. scanning the impression associated with the outer ear – Para 9) and determining the one or more landmarks further comprising determining, based on the ear modeling data, values of the one or more outer ear landmarks of the ear of the patient (i.e. locating the helix - Para 17; Fig. 4B).
Claim 8, Mcbagonluri discloses the method of claim 7, wherein the one or more outer ear landmarks include a position of a helix of the ear (i.e. locating the helix - Para 17; Fig. 4B), a position of a tragus of the ear, or a volume of a concha of the ear (i.e. concha features – Para 37).
Independent claim 19, the claim is similar in scope to claim 1. Therefore, similar rationale as applied in the rejection of claim 1 applies herein.
Independent claim 20, the claim is similar in scope to claim 1. Therefore, similar rationale as applied in the rejection of claim 1 applies herein.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fred Mcbagonluri et al., EP 2051187A2 as applied to claim 1 above, and further in view of John Grenier et al., WO 2020/198023 A1.
Claim 2, Mcbagonluri discloses the method of claim 1.
Mcbagonluri fails to disclose the wherein predicting the ear aperture plane comprises applying, by the one or more processors, a trained machine learning (ML) model to the ear modeling data to predict the ear aperture plane the predictive model may comprise a series of attributes that are learned from the training data, which Grenier (i.e. the predictive model may comprise a series of attributes that are learned from the training data – Para 32).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to combine Grenier’s known method of predicting the ear aperture plane comprises applying, by the one or more processors, a trained machine learning (ML) model to the ear modeling data to predict the ear aperture plane the predictive model may comprise a series of attributes that are learned from the training data with the method of Mcbagonluri because obtaining a model of an ear provides training data that enables machine learning to apply 3D anatomical scans for other uses (Grenier, abstract). Thus, the combination yields predictable results.
Claim 3, Mcbagonluri discloses the method of claim 2, wherein: the ear modeling data comprises a first point cloud representing the ear surface (i.e. loading the impression data 102, which can be represented according to any 3D modeling standard for defining a volumetric region, such as a point cloud – Para 10; Fig. 1C).
Mcbagonluri fails to disclose, applying the trained ML model comprises:
providing the first point cloud as input to the trained ML model; and obtaining a second point cloud representing the ear aperture plane as output of the trained ML model, which Grenier discloses (i.e. The machine learning model may transform scan data to a predicted model, which may result in modifying the overall shape and appearance of the 3-D representation of a cavity to generate an appropriate final shape for a device to dwell in the cavity – Para 40).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to combine Grenier’s known method of applying the trained ML model comprises:
providing the first point cloud as input to the trained ML model; and obtaining a second point cloud representing the ear aperture plane as output of the trained ML model with the method of Mcbagonluri because machine learning enables shape transformation that provide dimensional or aesthetic modifications that provide the benefit of improving a final shape and appearance for use with component placement (Grenier, Para 40).
Claim 4, Mcbagonluri discloses the method of claim 1.
Grenier discloses wherein predicting the ear aperture plane comprises: aligning, by the one or more processors, each of a plurality of ear shape templates with the ear modeling data, wherein each of the ear shape templates has a predefined ear aperture plane (i.e. Selecting training data to use for machine learning may be done through an automated visual inspection data of fit, which enables selecting paired before and after shapes that fit well – Para 30; the model queries a database to determine if the extracted features match stored features, and may implement specific rules based on whether the extracted features match stored features or not – Para 42);
determining, by the one or more processors, a difference or similarity metric for the aligned ear shape templates (i.e. Neural network training proceeds by taking initial conditions, then calculating a prospective transformation, comparing that to a reference (e.g., the positive data or good result) and then propagating the errors (e.g. the differences from the ideal) back into the network so that next time the prospective transformation is somewhat closer – Para 28);
selecting, by the one or more processors, an ear shape template from the plurality of ear shape templates based on the difference or similarity metric (i.e. Data from past cavity scans, devices and conventionally-generated three- dimensional models and/or customer feedback about devices may be collected with the object of identifying a plurality of datasets including initial three-dimensional scans and three- dimensional representations, or design models, that were good results or resulted in positive data for a final product, such as positive data for one or more of performance, fit, comfort, cost, reliability, manufacturability, or appearance. The plurality of datasets may be selected based on positive data associated with the end resulting device and may be used to train the machine learning model and validate the machine learning model – Para 29); and
predicting, by the one or more processors, the ear aperture plane based on the predefined ear aperture plane of the selected ear shape template (i.e. a learned attribute may be where to expand the model in areas for a tighter fit or better seal dependent on the application (e.g. the canal from aperture through completion of first bend of the ear canal). – Para 32), which Mcbagonluri fails to disclose.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention at the time the invention was made to combine Grenier’s known method of aligning, by the one or more processors, each of a plurality of ear shape templates with the ear modeling data; determining a difference or similarity metric for selecting an ear shape template used to predict an ear aperture plane with the method of Mcbagonluri because machine learning enables shape transformation that provide dimensional or aesthetic modifications that provide the benefit of improving a final shape and appearance for use with component placement (Grenier, Para 40).
Conclusion
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/CHANTE E HARRISON/Primary Examiner, Art Unit 2615
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