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 .
1. This communication in response to application filed 12/12/2024.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Claim Rejections - 35 USC § 103
3. 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) 1-6, 8, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over OTICON (EP 4 199 542 A1).
Regarding claim 1, OTICON teaches a method for determining an acoustic mass of an earpiece to be plugged into an ear of a user (reads on determining characteristics of a ventilation channel/opening of an ear-wearable hearing device, including acoustic mass, see [0014] and [0017]. Also, OTICON teaches an earpiece/hearing aid worn in the ear, see [0005], [0007] and [0018]), the method comprising:
receiving user data comprising at least an audiogram of the user (reads on data characterizing the hearing impairment of the user including measured hearing threshold versus frequency, e.g., a standard audiogram, see [0032]).
OTICON does not specifically teach “inputting the user data into a machine learning algorithm and determining the acoustic mass by the machine learning algorithm”, but it teaches trained neural networks for classification of current acoustic situation, state of user, and state of hearing aid (see [0056] and [0057]).
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to apply the trained neural network to automate determining the acoustic mass/vent characteristic
Regarding claim 2, OTICON teaches wherein the audiogram comprises at least one of:
an air conductance audiogram (reads on standard audiogram/measured hearing threshold versus frequency, see [0001] and [0032]. Note that a standard air-conduction audiogram would have been an obvious known audiogram used in hearing-aid fitting);
a bone conductance audiogram;
an ipsilateral audiogram of the ear into which the earpiece is plugged;
a contralateral audiogram of the opposite car;
an uncomfortable loudness level (at a plurality of frequencies).
Regarding claim 3, OTICON teaches wherein the earpiece is an earpiece of a hearing aid (reads on earpiece/ITE part/era mold, see [0005], [0007], [0018] and [0061-0064]); wherein the user data additionally comprise at least one of:
an experience level of the user indicative of an experience of the user with the hearing aid;
a fitting formula for the hearing aid (reads on fitting rationales/algorithms including NAL-NL1, NAL-NL2, DSL i/o, and proprietary fitting algorithms, see [0001], [0034] and [0035]).
Regarding claim 4, OTICON teaches wherein the acoustic mass is an acoustic vent mass, which is directly proportional to the length of a vent and inversely proportional to a cross-sectional area of the vent (note that OTICON teaches characteristics of the ventilation channel or opening, including physical dimensions and acoustic mass, see [0014]. Also, OTICON teaches that different length, different cross-section/area and different acoustic mass, see [0017]. OTICON further a Helmholtz vent relationship involving vent area and vent length, see [0093-0096]).
Regarding claim 5, OTICON teaches wherein the machine learning algorithm is an artificial neuronal network (reads on neural network, see [0057]), a Gaussian process, a polynomial regression and/or a regression tree.
Regarding claim 6, OTICON teaches wherein the earpiece is a dome of a hearing aid (see [0117]); and/or wherein the earpiece is a hearing protector.
Regarding claim 8, OTICON teaches determining geometric dimensions of a vent of the earpiece (reads on determining geometric dimensions of a vent of the earpiece because OTICON teaches estimating an effective vent size and teaches that physical dimensions are associated with the vent channel or opening of the hearing-aid body, receiver-in-the-ear body, or dome (see [0118-0123]). Also, OTICON comparing an estimated transfer function with predicated data and obtaining an effective vent size estimate (see [0130-0134])).
Regarding claim 15, OTICON teaches a non-transitory computer-readable medium having instructions stored thereon that, when executed by a computer, cause the computer to perform the method of claim 1 for determining an acoustic mass of an earpiece to be plugged into an ear of a user with a machine learning algorithm (see [0066]-[0069]).
Claim(s) 7, 10-14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over OTICON (EP 4 199 542 A1) in view of Shonibare et al. (Pub.No.: 2023/0039728 A1).
Claim 7 recites “selecting a type of earpiece providing the determined acoustic mass”.
OTICON teaches determining an acoustic mass/vent characteristics of a ventilation channel or opening, including physical dimensions and acoustic mass (see [0014] and [0017]). OTICON also teaches an earpiece of a hearing aid (see [0018] and [0061]-[0064]).
OTICON does not specifically teach “selecting a type of earpiece providing the determined acoustic mass”.
However, Shonibare teaches predicting/ranking a hearing assistance device model or shell for a patient using a machine-learning trained model based on audiological diagnostic data and patent-specific data (see [0013], [0045]-[0049] and [0070]).
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to use Shonibare’s machine-learning model selection technique with OTICON earpiece/acoustic mass determination in order to select an appropriate earpiece type for the user based on the determined acoustic/vent characteristics and user hearing aid data.
Claim 9 recites “generating production data for the earpiece”.
determining an acoustic mass/vent characteristics of a ventilation channel or opening, including physical dimensions and acoustic mass (see [0014] and [0017]).
OTICON does not specifically teach “generating production data for the earpiece”.
Shonibare teaches generating a hearing assistance device for a used based on the output of a machine-learned model (see [0034]). Shonibare further teaches that when a custom hearing assistance device is recommended, specifications may be sent to a manufacturer, and that an ear impression maybe digitized and processed, such as via CAD software, into the desired style pf hearing assistance device in the specification received (see [0035]-[0036]).
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to use Shonibare production/manufacturing process with OTICON earpiece having acoustic vent characteristics to generate production data for the selected earpiece.
Independent claim 10 recites “A method for selecting a dome for a hearing aid to be plugged into an ear of a user, the method comprising:
receiving user data comprising at least an audiogram of the user;
inputting the user data into a machine learning algorithm and determining a dome type for the dome with the machine learning algorithm”.
OTICON teaches a hearing aid having an earpiece/dome like structure to be plugged into an ear of a user (see [0117]) and receiving user data comprising an audiogram, i.e., measured hearing threshold versus frequency for the user (see [0032]).
OTICON does not specifically teach the ML algorithm determining a dome type.
Shonibare teaches receiving patient information including audiological diagnostic data, inputting such data into a machine-learning trained model, and outputting/ranking a hearing assistance device model for the patient (see [0049], [0070], [0074]-[0075]).
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to apply Shonibare’s machine-learning device-selection technique to OTICON’s hearing -aid dome/earpiece in order to determine a suitable dome type based on user’s audiological data, thereby improving individualized fitting of the hearing aid.
Independent claim 11 recites “A training method for training a machine learning algorithm for determining an acoustic mass of an earpiece to be plugged into an ear of a user, the training method
comprising:
receiving a dataset with records of user data, each record comprising at least an audiogram of a user and an earpiece type used by the user;
determining for each record at least one user score of the earpiece type from the user data;
generating a filtered dataset by excluding records from the dataset, wherein a record is excluded from the dataset, if the at least one user score is lower than a threshold;
determining an acoustic mass for each record from the earpiece type;
training the machine learning algorithm with the filtered dataset”.
Note for claim 11, OTICON teaches determining acoustic mass from earpiece/vent characteristics, including physical dimensions and acoustic mass of ventilation channel or opening (see [0014] and [0017]).
Shonibare teaches generating a dataset including audiological diagnostic data and patient-specific data corresponding to returned and retrained hearing assistance devices, accessing a dataset to obtain feature vectors corresponding to hearing assistance device models, and training a machine-learning model based on the dataset and the feature vectors (see [0050]-[0053] and [0084]). Shonibare also teaches that audiological diagnostic data may include audiogram (see [0051], [0075] and [0082]). Shonibare further teaches outputting probabilities/return likelihood information for hearing assistance models (see [0049], [0053] and [0086]- [0087]).
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to receive a dataset with records of user data including audiograms and device/earpiece type information, determining a user score based on return/retention likelihood, filtering records \based in threshold, determining acoustic mass from the earpiece type using OTICON’s vent/acoustic-mass characteristics, and training the machine-learning algorithm with the filtered dataset.
Dependent claim 12 recites “wherein each record of the dataset comprises additionally a wearing time of an earpiece; wherein the user score is a customer satisfaction score determined from the wearing time”.
Note that Shonibare teaches that training dataset may include patient-specific data and hearing assistance device history data, including data corresponding to returned and retrained hearing assistance devices (see [0018], [0020, [0050]- [0053] and [0084]). OTICON further teaches monitoring hearing-aid operation during daily use (see [0136]- [0139]). Thus, the combination teaches or renders obvious records including wearing/use history of the earpiece/hearing-aid, and a customer satisfaction score determined from such wearing/use history.
Dependent claim 13 recites “wherein the user score is an intelligibility score;
wherein each record of the dataset comprises additionally a fitting formula of a hearing aid to be used with the earpiece;
wherein, for at least one frequency, a desired target gain is determined from the fitting formula;
wherein, for the at least one frequency, a feedback threshold limited target gain is determined from the earpiece type;
wherein the intelligibility score is determined from the feedback threshold limited target gain at the at least one frequency”.
Note that Shonibare teaches audiological diagnostic data including word recognition score, speech reception threshold, and audiogram data (see [0020], [0051], [0057] and [0082]).
OTICON teaches fitting rationales/fitting algorithms for determining desired gain for a hearing aid based on the user’s hearing impermanent/ audiogram data (see [0001], [0034] and [0035]). OTICON further teaches feedback-path estimation and feedback control in the hearing aid, including feedback estimation and acoustic/vent characteristics of the earpiece affecting the hearing-aid response (see [0014], [0017] and [0054-0058]), thus, the combination teaches or render obvious determining an intelligibility score based on audiological performance information and feedback-limited target gain associated with the earpiece type.
Dependent claim 14 recites “determining an augmentation score for each record from the at least one user score; generating an augmented dataset by at least duplicating the record, when its augmentation score is higher than a threshold”. Shonibare teaches processing training data, labeling training data. Generating machine-learning feature sets, training the machine-learning model, evaluating the model, gathering feedback, and retraining when a threshold is satisfied (see [0023]-[0028]). Shonibare also teaches using returned/retrained hearing assistance device data and weighting return/not-return data for training the prediction model (see [0043], [0050]-[0053] and [0084]-[0087]).
Dependent claim 16 recites “when executed by a computer, cause the computer to perform the method of claim 11 for training a machine learning algorithm for determining an acoustic mass of an earpiece to be plugged into an ear of a user”. The combination teaches or render obvious the training method of claim 11. OTICON teaches computer readable medium/program code for causing a computer to perform the disclosed method (see [0066]- [0069]). Shonibare teaches a non-transitory machine readable medium storing instruction ...etc. (see [0057]- [0060], [0096] and [0100]).
Conclusion
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rasha S. AL-Aubaidi whose telephone number is (571) 272-7481. The examiner can normally be reached on Monday-Friday from 8:30 am to 5:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ahmad Matar, can be reached on (571) 272-7488.
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/RASHA S AL AUBAIDI/Primary Examiner, Art Unit 2693
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