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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-16, 19-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
Claims 1-16 & 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Alamdari et al, “Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning”, in view of Simonides et al, US Patent Pub. 20170230764 A1. (The Alamdari et al reference is cited in IDS filed 05/31/2024)
Re Claim 1, Alamdari et al discloses a method for fitting a hearing instrument (section II, personalized compression approach), the method comprising: generating, by a processing system, training data based on post-fitting adjustments made to settings of a plurality of hearing instruments and based on profiles of users of the plurality of hearing instruments (section A, compression ratios which are computed from the gains specified by DSL-v5 tables and used as a starting point for the personalized fitting), wherein the post-fitting adjustments are made to the settings of the plurality of hearing instruments after initial uses of the plurality of hearing instruments (section C, adjustment of the compression ratios through human preference interface which are stored in a dataset D used for subsequent training); training, by the processing system, a machine learning (ML) model based on the training data to generate initial fitting suggestions (training of preference/reward predictor by dataset D, see section D, and training of RL agent based on output of preference/reward predictor, see section E, which is used to update the compression ratios, see fig. 3(a), “training mode”); and prior to an initial use of a current hearing instrument by a current user, generating, by the processing system, an initial fitting suggestion for the current hearing instrument by applying the ML model to input that includes a profile of the current user (fig. 3(b), “operation mode” generating personalized compression ratios (CRs); training of preference/reward predictor by dataset D, see section D, and training of RL agent based on output of preference/reward predictor, see section E, which is used to update the compression ratios, see fig. 3(a), “training mode”, wherein updating of the compression ratios implies that the initial compressions ratios are previously updated compression ratios that are utilized as the initial compression ratio for the next compression ratio iteration); but fails to disclose where the current user of the current hearing instrument is not among the plurality of users with stored profiles. Simonides et al discloses a hearing augmentation system where the current user settings are being adjust based on a plurality of other different user profiles (Simonides et al, para 0053). It would have been obvious to modify Alamdari et al such that its fitting suggestion/settings for the current hearing instrument as based on a plurality of different user profiles as taught in Simonides et al for the purpose of making the system more efficient by comparing current user settings with other stored user settings to select optimized user settings.
Re Claim 2, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 1, further comprising configuring the current hearing instrument based on the initial fitting suggestion for the current hearing instrument (Alamdari et al, fig. 3(b), “operation mode” generating personalized compression ratios (CRs); training of preference/reward predictor by dataset D, see section D, and training of RL agent based on output of preference/reward predictor, see section E, which is used to update the compression ratios, see fig. 3(a), “training mode”, wherein updating of the compression ratios implies that the initial compressions ratios are previously updated compression ratios that are utilized as the initial compression ratio for the next compression ratio iteration).
Re Claim 3, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 1, wherein the method further comprises generating an adjustment record that includes data describing a post-fitting adjustment to settings of one or more hearing instruments of a specific user in the plurality of users, the adjustment record further including one or more of: data describing a complaint as subjectively perceived by the specific user that led to the post-fitting adjustment, objective data associated with the complaint, data describing a hearing professional's interpretation of the complaint, or data describing a plan or performed actions for addressing the complaint, and wherein generating the training data comprises generating the training data based in part on the adjustment record (Alamdari et al, section II. C, second paragraph., dataset D as “adjustment record” and feedback level u as “data describing a complaint”; wherein data describing a hearing professional's interpretation of the complaint is selected from the Markush claim language).
Re Claim 4, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 3, further comprising: causing, by the processing system, the adjustment record to be stored in a non-volatile storage device of the one or more hearing instrument of the specific user (Alamdari et al, section II. C, second paragraph., stored in dataset D implies storage device).
Re Claim 5, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 3, further comprising: causing, by the processing system, the adjustment record to be stored in a non-volatile storage system of a server system remote from the one or more hearing instruments of the specific user (Alamdari et al, section III. B, first paragraph., testing of participants was “conducted online” which renders a “remote server” and a “resource identifier” of the adjustment record); and causing, by the processing system, a resource identifier of the adjustment record to be stored on a non-volatile storage device of the one or more hearing instruments of the specific user (Alamdari et al, section III. B, first paragraph., testing of participants was “conducted online” which renders a “remote server” and a “resource identifier” of the adjustment record).
Re Claim 6, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 3, wherein: the method further comprises determining, by the processing system, that the specific user has the complaint without receiving an explicit indication of user input indicating the specific user has the complaint (Alamdari et al, section II. D; fig. 4, “estimated preference” and “estimated reward”), and generating the adjustment record comprises generating the adjustment record in response to determining that the specific user has the complaint (Alamdari et al, section II. D; fig. 4, “estimated preference” and “estimated reward”).
Re Claim 7, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 1, wherein the ML model is a first ML model, the method further comprising: training, by the processing system, a second ML model based on the post-fitting adjustments and the profiles of the plurality of users to determine user support levels that indicate levels of user support associated with the plurality of users (Alamdari et al, fig. 3(a), “reward predictor training” as “first ML model training” and “RL agent training” as “second ML model training”, wherein the number of adjustment actions of a user (see section III. A and figs. 5 and 6) is indicative of a support level for that user); and applying, by the processing system, the second ML model to determine an anticipated user support level for the current user based on the profile of the current user (Alamdari et al, fig. 3(a), “reward predictor training” as “first ML model training” and “RL agent training” as “second ML model training”, wherein the number of adjustment actions of a user (see section III. A and figs. 5 and 6) is indicative of a support level for that user).
Re Claim 8, the combined teachings of Alamdari et al and Simonides et al disclose the method of claim 1, wherein the ML model is a first ML model, and the training data is first training data, the method further comprising: generating second training data based on the post-fitting adjustments made to the settings of the plurality of hearing instruments and based on the profiles of the plurality of users (Alamdari et al, fig. 3(a), “reward predictor training” as “first ML model training” and “RL agent training” as “second ML model training”, wherein the “RL agent” as “second ML model” is applied to generate the personalized CRs as “post-fitting adjustment suggestion” for the hearing instrument, see also fig. 3(b)); training a second ML model based on the second training data to determine post-fitting adjustment suggestions (Alamdari et al, fig. 3(a), “reward predictor training” as “first ML model training” and “RL agent training” as “second ML model training”, wherein the “RL agent” as “second ML model” is applied to generate the personalized CRs as “post-fitting adjustment suggestion” for the hearing instrument, see also fig. 3(b)); and after the initial use of the current hearing instrument by the current user, generating, by the processing system, a post-fitting adjustment suggestion for the current hearing instrument by applying the second ML model to input that includes the profile of the current user (Alamdari et al, fig. 3(a), “reward predictor training” as “first ML model training” and “RL agent training” as “second ML model training”, wherein the “RL agent” as “second ML model” is applied to generate the personalized CRs as “post-fitting adjustment suggestion” for the hearing instrument, see also fig. 3(b)).
Claim 9 has been analyzed and rejected according to claim 1.
Claim 10 has been analyzed and rejected according to claim 2.
Claim 11 has been analyzed and rejected according to claim 3.
Claim 12 has been analyzed and rejected according to claim 4.
Claim 13 has been analyzed and rejected according to claim 5.
Claim 14 has been analyzed and rejected according to claim 6.
Claim 15 has been analyzed and rejected according to claim 7.
Claim 16 has been analyzed and rejected according to claim 8.
Claim 19 has been analyzed and rejected according to claim 1.
Claim 20 has been analyzed and rejected according to claim 3.
Claim 21 has been analyzed and rejected according to claim 7.
Claim 22 has been analyzed and rejected according to claim 8.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/GEORGE C MONIKANG/Primary Examiner, Art Unit 2692 3/9/2026