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
Status of the Application
Claims 1-29 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 04/06/2026.
Claims 1 and 16 are currently amended.
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 1-29 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-15, 23-26, and 28 fall within the statutory category of an apparatus or system. Claims 16-22, 27, and 29 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, determine a statistical value based on the intermediate gain values, and determine a gain value range for a frequency band based on the statistical value, wherein the determining the gain value range is for the frequency band, 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, determining intermediate gain values, determining a statistical value based on the intermediate gain values, and determining a gain value range based on the statistical value 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, and enable determining and providing the gain value range for the frequency band based on outputs of the neural network fitting models. The steps of inputting data, here the user hearing characteristic, into a neural network fitting model and the use of the model to determine and output a gain value range 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 and providing the determined gain value which amount to insignificant extra-solution activity, as in MPEP 2106.05(g), because the step of obtaining an input associated with an individual user hearing characteristic are mere data gathering and the step of providing the determined gain value is 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 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. 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 and providing the determined gain value which are both 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 23-25 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.
Claims 7 and 21 include applying the individual gain value to a hearing device which amounts to insignificant extra-solution activity as mere data outputting because the claims does not provide any specific details about how this is applied to the device which would amount to more than outputting the data. This is found to be well-understood, routine, and conventional activity in the field similar to receiving or transmitting data over a network, as per MPEP 2106.05(d)(II), which has been found by the courts to be well-understood, and routine computer functions.
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 the statistical value which is a mathematical calculation and falls in the abstract idea of mathematical concepts.
Claim 10 includes determining the gain value range based on the statistical value which is directed to a mental process 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 statistical value by 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 20 includes determining an individual gain value based on the gain value range which is directed to a mental process for the same reasons as the independent claims.
Claims 23-25 include limitations similar to those in Claim 1 and are directed to the same abstract idea.
Claims 26 and 27 include determining additional intermediate gain values and determining an additional gain value range 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 gain value range and additional gain value range as different from each other, which does not add any functional elements to the claims.
Claims 28 and 29 include a description of the gain value range as defining an upper and lower limit within which an individual gain value is selectable to fit a hearing device for the frequency band. This merely serves to further specify or limit the abstract idea and therefore the claims are directed to the same abstract idea as the independent claims.
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 Page 8, “Claim Rejections under 35 U.S.C. §112”, filed 04/06/2026 with respect to claims 1-29 have been fully considered and they are persuasive. Therefore, the rejection of 01/06/2026 has been withdrawn.
Applicant’s arguments, see Pages 8-9, “Claim Rejections under 35 U.S.C. §101”, filed 04/06/2026 with respect to claims 1-29 have been fully considered but they are not persuasive.
Applicant argues that the features including an ensemble of neural network fitting models configured to electronically determine intermediate gain values based on the input associated with the individual user hearing characteristic and each of the neural network fitting models is configured to electronically receive the input associated with the individual hearing characteristic, wherein the neural network fitting models are different from each other to enable the electronic system to electronically determine and to provide the gain value range for the frequency band based on respective outputs of the neural network fitting models, and wherein the neural network fitting models have different respective sets of nodal weights in order to allow the ensemble of the neural network fitting models to provide the intermediate gain values that are different from each other, wherein the different intermediate gain values contribute to a spread of the gain value range, constitute an improvement in the field of hearing device fitting because they provide a range of gain values which is an improvement over the known fitting solution. Examiner respectfully disagrees that this provides a technical improvement. The determination of intermediate gain values based on the input associated with user hearing characteristic is directed to the abstract idea. Any improvement to determining the intermediate gains is an improvement to the abstract idea itself. An abstract idea need not be old or long-prevalent to be abstract. Even a newly discovered abstract idea is still directed to a judicial exception regardless of its novelty. The use of an ensemble of neural network fitting models configured to electronically carry out the abstract idea of determining intermediate gain values, determine gain value ranges for a frequency band does not provide an inventive concept but rather amounts to applying the algorithm to an abstract idea which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). The description of the neural network fitting models as different from each other and 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 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). Therefore, this description of the neural network fitting models is merely providing the definition of ensemble machine learning.
Examiner also reiterates the previous response with regards to ensemble machine learning models being well-understood, routine, and conventional in the field of art. As described in the rejection above, the concept of an ensemble of neural network fitting models is a well-understood, routine, and conventional concept in the art, as evidenced by 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. Therefore, the limitation of an ensemble of neural network fitting models is demonstrably well-understood, routine, and conventional in the art.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 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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