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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/12/2026 has been entered.
Status of the Application
Claims 1-6, 8-19, 22-24, 26-27, 30-33 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendment to the Claims and Remarks filed on 08/03/2026.
Claims 1, 9-10, 14-16, 18-19, 22-24, and 26-27 are currently amended.
Claims 7, 20-21, 25, and 28-20 are canceled and not considered at this time.
Claims 30-33 are newly added.
Claim Objections
Claim 33 is objected to because of the following informalities: Claim 33 recites the method according to claim 1. However, Claim 1 is an electronic system. Examiner notes that this should either read the method of claim 16 or the electronic system of claim 1. Because this is a duplicate of Claim 31 which is dependent on claim 1, Examiner interprets the claim to read the method according to Claim 16 and will examine the claims below as such. Appropriate correction is required.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-6, 8-19, 22-24, 26-27, 30-33 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-6, 8-15, 23-24, 26, and 30-31 fall within the statutory category of an apparatus or system. Claims 16-19, 22, 27, and 32-33 fall within the statutory category of a process.
Step 2A, Prong One
As per Claims 1 and 16, the limitations of determine intermediate gain values based on the input associated with the individual user hearing characteristic and determine an individual gain value based on the intermediate gain values, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The steps of determining intermediate gain values based on individual user hearing characteristic and determining an individual gain value based on the intermediate gain values are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The claims also recite that the neural network fitting models receive input associated with the individual user hearing characteristic. The steps of inputting data, here the user hearing characteristic, into a neural network fitting model is a mathematical concept because it applies a mathematical equation/algorithm (neural network model) to the input to determine an output. Therefore, the claim limitations also fall into the “Mathematical Concepts” abstract idea. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional elements – an input interface, a processing unit, and an output interface. The system in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The input interface, processing unit, and output interface are recited as electronically performing the functions of the abstract idea which does not provide an improvement in computer functionality, as per MPEP 2106.05(a)(I) which describes the mere automation of a processing using a generic computer to be found by the courts to not show an improvement. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite an ensemble of neural network fitting models for determining intermediate gain values which is part of the abstract idea. The use of neural network fitting models is a mathematical algorithm being applied on a general purpose computer to perform the abstract idea, which as per MPEP 2106.05(f)(2), amounts to mere instructions to apply the exception. The claims also recite the additional elements of obtaining an input associated with an individual user hearing characteristic, providing the determined gain value, obtaining the intermediate gain values that are provided by the neural network fitting models that are different from each other, and transferring the individual gain value to a hearing device to configure the hearing device based on the individual gain value that is attributable to the intermediate gain values respectively provided by the neural network fitting models, which amount to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of obtaining an input associated with an individual user hearing characteristic and obtaining the intermediate gain values are mere data gathering and the steps of providing the determined gain value and transferring the individual gain value to a hearing device are mere data outputting in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). The claim recites that the individual gain value is transferred for the intended purpose of configuring the hearing device based on the individual gain value that is attributable to the intermediate gain values respectively provided by the models. Under BRI of the claim, the limitation merely amounts to transferring the individual gain value. The claim does not positively recite the configuring of the hearing device, as this is merely the intended use of the transferring step. The claims also include the description of the neural network fitting models having different respective neural network architecture or having different respective sets of nodal weights in order to allow the ensemble of models to provide values that are different from each other, which is merely a description of ensemble learning itself. Therefore, this description of the neural network fitting models is merely providing the definition of ensemble machine learning. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including an input interface, a processing unit, and an output interface are recited at a high level of generality and are recited as generic computer components by reciting the input interface as an input transducer (Specification [0033]), a processing unit which is a processor, etc. (Specification, [0037]), and the output interface as an output transducer coupled to the processing unit (Specification [0038]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims recite an ensemble of neural network fitting models. The ensemble of neural network fitting models is a known mathematical algorithm before the filing of the present invention. For additional support, a showing can be made that an additional element is well-understood, routine, and convention, and therefore does not amount to significantly more than the recited abstract idea, by citing multiple prior publications that teach or disclose the limitation (MPEP § 2106.07(a)(III)). In light of Dunne et al. (US 2020/0272899 A1), hereinafter Dunne, which discloses ensemble neural networks which are a networks that are combined in paragraph 67 and combine an ensemble of neural networks in which each model weights the layers to determine the final training or ensemble output in paragraphs 102-103, and Birke eat al. (US 2017/0344400 A1), hereinafter Birke, which discloses a set of neural networks using different algorithms making up an ensemble method for making predictions in paragraphs 35-36, the limitation of an ensemble of neural network fitting models is demonstrably well-understood, routine, and conventional in the art. Therefore, it cannot rise to significantly more than the established abstract idea (MPEP § 2106.05(d)). The claims also include the additional elements of obtaining an input associated with an individual user hearing characteristic, providing the determined gain value, obtaining the intermediate gain values that are provided by the neural network fitting models that are different from each other, and transferring the individual gain value to a hearing device to configure the hearing device based on the individual gain value that is attributable to the intermediate gain values respectively provided by the neural network fitting models, which are elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as presenting offers and gathering statistics, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(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) and (iv) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). The claims also include the description of the neural network fitting models having different respective neural network architecture or having different respective sets of nodal weights in order to allow the ensemble of models to provide values that are different from each other, wherein the values contribute to a spread of the range of output values, which is merely a description of ensemble learning itself. As evidenced in Vaghela, et al., “Boost a Weak Learner to a Strong Learner Using Ensemble System Approach”, ensemble learning constructs a set of base models from the training data and uses the prediction of each model for the classification/prediction (Page 1432, Col. 2). In boosting ensemble learning methods, a series of multiple models are built from different model builders, i.e. architecture, associating a weight with each entity in the dataset, and adjusting/increasing the weights for the next model which reduces the variance of the result/prediction (Page 1434, Col. 1). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-15, 17-22, and 23-24 add further limitations which are also directed to an abstract idea as described below.
Claims 2-4 provide further description of the processing unit as being “a part of” a fitting instrument, a computer, or a hearing device. However, claim language does not positively recite the fitting instrument, computer, or hearing device and does not provide for how the processing unit is “part of” these devices. Therefore, this is merely descriptive and does not provide any functional limitations beyond the abstract idea and the claims are therefore directed to the same abstract idea as the independent claims.
Claim 5 further specifies and limits the neural network of the independent claim and is therefore directed to the same abstract idea.
Claims 6 and 22 include determining an individual gain value based on a mean value of the intermediate gain values which is a mental process for the same reasons as the independent claims.
Claim 8 includes applying a set of parameters to the ensemble of the neural network fitting models which is a mathematical calculation and thus falls into the abstract idea of mathematical concepts. The claim also includes retrieving a set of parameters of a machine learning algorithm form a remote physical location which is mere data gathering that is insignificant extra-solution activity for the same reasons as the independent claims.
Claim 9 includes the use of a statistical algorithm to determine a statistical value based on the intermediate gain values which is a mathematical calculation and falls in the abstract idea of mathematical concepts.
Claim 10 includes determining the gain value range using a gain value algorithm which is directed to a mental process and mathematical concepts for the same reasons as the independent claims.
Claims 11-13 and 17 further specifies and limits the input of the independent claim and is therefore directed to the same abstract idea.
Claims 14 and 18 includes determining a mean value and/or a standard deviation of the intermediate gain values which is directed to a mental process for the same reasons as the independent claims. This can also fall into the abstract grouping of mathematical concepts since determining a mean value and determining a standard deviation are mathematical calculations.
Claims 15 and 19 further specifies and limits the user hearing characteristic and is therefore directed to the same abstract idea.
Claim 23 includes limitations similar to those in Claim 1 and are directed to the same abstract idea.
Claim 24 further specifies and limits the intermediate gain values and is therefore directed to the same abstract idea as the independent claims.
Claims 26 and 27 include determining additional intermediate gain values and determining an additional individual gain value for another frequency band based on the additional intermediate gain values which fall into the mental processes group of abstract ideas for the same reasons as the independent claims. The claims also include the use of neural network fitting models and processing unit to execute steps of the abstract idea, where the models are mathematical algorithms applied to carry out steps of the abstract idea which amounts to mere instructions to apply the exception. The use of the processing unit to execute steps of the abstract idea also amounts to mere instructions to apply the exception and does not integrate the abstract idea into a practical application. The claim also provides a mere description of the individual gain value and additional individual gain value as different from each other, which does not add any functional elements to the claims.
Claims 30 and 32 include training the neural network models with data from a data pool. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the ML is considered to be part of the abstract idea because they fall under data manipulations that humans perform and thus are part of mental process.
Claims 31 and 33 further specifies and limits the neural network fitting models as being part of a hearing device fitting model to carry out the step of fit the hearing device based on the individual user hearing characteristic, which falls into the abstract grouping of a mental process for the same reason as the independent claims. This can also fall into the abstract grouping of certain methods of organizing human activity because this is activity which is performed by a physician when treating a patient with a hearing device. The use of a neural network amounts to mere instructions to apply the exception, for reasons similar to the independent claims, as above.
Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Response to Arguments
Applicant’s arguments, see Pages 8-9, “Claim Rejections under 35 U.S.C. §101”, filed 08/03/2026 with respect to claims 1-29 have been fully considered but they are not persuasive.
Applicant argues that the amended claims integrate the abstract idea into a practical application because the claim recites the electronic system is configured to cause the individual gain value to be physically transferred to a hearing device to configure the hearing device based on the individual gain value that is attributable to the intermediate gain values respectively provided by the neural network fitting models. Examiner respectfully disagrees. The step of transferring the individual gain value to a hearing device to configure the hearing device based on the individual gain value that is attributable to the intermediate gain values respectively provided by the neural network fitting models is an additional element which amounts to insignificant extra-solution activity because transferring a value to a hearing device is mere data outputting of the result of the abstract idea. The BRI of the claim language amounts to transferring the value to a hearing device for the purpose of configuring the hearing device. The claim does not actually positively recite the configuring of the hearing device. Additionally, there is no physical action that occurs as a result of the transferring of the gain value, which would be needed to have a practical application of the abstract idea. The claim merely transmits data which is well-understood, routine, and conventional activity as per MPEP 2106.05(d)(II). The claim does not recite the actual configuration of the device nor any physical action as a result of the configuration of the device. Therefore, the rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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