DETAILED ACTION
In the response to this office action, the examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the examiner in prosecuting this application.
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 .
Claim Objections
Claim 2 and 14 are objected to because of the following informalities:
Claim 2 recites the limitation “an audio playback setting”. It is unclear if this is the same audio playback setting as previously mentioned in claim 1. Use of “the audio playback setting” is suggested unless they are intended to be different, in which case “first and second” playback settings is suggested.
Claim 14 is objected in an analogous manner.
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 therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the limitation of determining, based on a set of DI features, a placement of each of the plurality of loudspeakers relative to a listener, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic trained machine-learning model. That is, other than reciting “by a trained machine-learning model” and the claimed adjusting step, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a trained machine-learning model” language, “determining” in the context of this claim encompasses the user manually determining where left, right, center, etc. speakers are to be placed for audio reproduction based on collected data. The adjusting step is an extra solution activity. The adjusting is only mentioned at a high level of generality with no technical detail. It is nothing more than applying an adjustment decided upon in the mind of a user. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, the limitations of “determining human directivity index (DI) pattern data corresponding to a location of a listener” is a mathematical calculation of a ratio of acoustical energy which can be calculated mentally or with pencil and paper given input data as stated, “based on vocalization recorded by a microphone at each of a plurality of loudspeakers” is an insignificant data gather step as is collecting pattern data through measurement via a microphone. Further the “extracting a set of DI features from the DI pattern data” is a mental process of selecting DI values. Note the DI values from the table in paragraph [0019] from which a person may select/extract DI values for reasons such as grouping only the L speaker data (the claimed “feature”) to determine the placement of the L speaker. Also note in paragraph [0021] DI values can be features. “Providing the set of DI features to a trained machine-learning model” is an insignificant extra solution activity of providing the value to generic computer components at a high level generality.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of using a trained machine-learning model and the adjusting step. The model is only recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic machine-learning component. The adjusting step is only mentioned at a high level of generality with no technical detail. A generic adjustment does not provide any technical improvement. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic machine-learning model to perform the determining step amounts to no more than mere instructions to automate the determining of the placement using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 12 and 13 are rejected in an analogous manner to claim 1, given the recited media, loudspeakers, and microphones are just conventional items used in a conventional manner.
Claims 2-9 and 14-20 only further define the mental processes or the DI pattern data.
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.
Claim(s) 1-4, 6, 8, 9, 12-16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al. (US 20220335937 A1) in view of Bharitkar et al. (US 20220159401 A1).
Regarding claim 13, Thomas discloses a system (see at least figures 1B and 2) comprising:
a plurality of loudspeakers (103 and 105 of figure 1B, paragraph [0056], 204 of figure 2);
a plurality of microphones (104 and 106 of figure 1B, paragraphs [0059] to [0061], 205 of figure 2), each microphone co-located with at least one of the plurality of loudspeakers such that each of the plurality of loudspeakers is co-located with at least one of the plurality of microphones (see figure 1B); and
one or more non-transitory computer readable storage media (paragraph [0173]) storing instructions; and one or more processors (paragraph [0173]) coupled to the one or more non-transitory computer readable storage media and operable to execute the instructions to:
determine human directivity index (DI) pattern data (volume levels, paragraphs [0073] to [0075], “[0075] In some examples, the acoustic features may include a wakeword confidence metric, a wakeword duration metric and/or at least one received level metric. The received level metric may indicate a received level of a sound detected by a microphone and may correspond to a level of a microphone's output signal”) representing a directivity pattern of a human voice (the more directed toward a speaker, the louder the received sound) and corresponding to a stationary location of a listener (location of user 101 of figure 1B), based on a vocalization (102 of figure 1B) made by the human voice at the stationary location of the listener (see figure 1B) as recorded by a microphone at each of a plurality of loudspeakers (see paragraphs [0056] to [0064]);
extract a set of DI features from the DI pattern data (select which acoustic features to feed to the classifier, paragraphs [0087] to [0088], see paragraphs [0076] to [0086] for selections such as frequency band or frames of audio);
provide the set of DI features to a trained machine-learning model (step 145 of figure 1D, paragraphs [0127], [0151], and [0157]); and
determine, by the trained machine-learning model and based on the set of DI features, a location of a user (step 150 of figure 1D, paragraph [63] teaches microphone signals may be used for the classifier, the abstract and paragraph [0087] teach the classifier may be based on acoustic features, the abstract and paragraph [0091] teach the classifier may be used to estimate a location/zone of the user relative to the loudspeakers);
Thomas does not expressly disclose adjusting an audio playback setting based on a location.
Bharitkar discloses a system comprising:
a plurality of loudspeakers (figure 1, items 104-114);
further comprising one or more processors (paragraph [0019]) configured to execute the instructions to adjust, based on the determined placement of at least one of the plurality of loudspeakers relative to the listener, an audio playback setting of one or more of the plurality of loudspeakers (paragraphs [0032] to [0033]).
It would have been obvious to a person of ordinary skill in the art to use the adjustment of Bharitkar in the system of Thomas for the benefit of optimizing the delivered sound field. Therefore, it would have been obvious to combine Bharitkar with Thomas, for the benefits above, to obtain the invention as specified in claim 13.
Regarding claim 14, Bharitkar discloses further comprising one or more processors (paragraph [0019]) configured to execute the instructions to adjust, based on the determined placement of each of the plurality of loudspeakers relative to the listener, an audio playback setting of one or more of the plurality of loudspeakers (paragraphs [0032] to [0033]).
Regarding claim 15, Bharitkar discloses wherein the audio playback setting comprises a spatial perception correction (paragraphs [0032] to [0033]).
Regarding claim 16, Bharitkar discloses wherein the placement comprises a distance of each of the plurality of loudspeakers relative to the listener (paragraphs [0019] to [0020], 508 of figure 5).
Regarding claim 18, Bharitkar discloses wherein the trained machine-learning model comprises a trained neural network (paragraph [0028], claim 11).
Regarding claim 20, Thomas discloses wherein the DI pattern data comprises a DI value in each of a plurality of frequency bands for each of the plurality of loudspeakers (paragraphs [0076] to [0086] specifically paragraphs [0080], [0081], [0082], and [0083]).
Claim 1 is rejected in an analogous manner to claim 13.
Claims 2-4, 6, and 8 are rejected in an analogous manner to claims 14-16, 18, and 20 respectively.
Regarding claim 9, Dyonisio discloses further comprising:
detecting a predetermined command vocalization by the listener; and determining the human DI pattern data in response to detecting the predetermined command vocalization (paragraphs [0076] to [0086], specifically [0077], [0078], [0079] and [0086], also see paragraph [0045]).
Claim 12 is rejected in an analogous manner to claim 13.
Allowable Subject Matter
Claim 10 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed June 18th, 2026 have been fully considered but they are not persuasive.
Applicant’s amendments have overcome the previous drawing objections.
In general applicant argues that the amendments to the previous claims are not found in prior art (see applicant’s arguments dated June 18th, 2026, starting page 8). A new ground of rejection has been established as above.
Applicant argues “In addition, Dyonisio tracks a user location as the user moves about several zones, but Dyonisio does not disclose any "stationary location of the listener," as amended Claim 1 recites. Finally, Dyonisio discloses determining a user location as a user moves about Dyonisio's zones, but Dyonisio does not disclose "determining a placement of each of the plurality of loudspeakers relative to the stationary location of the listener at which the vocalization occurred," as amended Claim 1 requires” (see applicant’s arguments dated June 18th, 2026, page 10). Applicant’s invention and claims do not require the user to vocalize from the same spot every time. Each measurement is from the stationary location the user is located at the time. Similarly, in the Thomas reference (and the Dyonisio) reference, the user is free to move, but measurements are taken and locations are determined based on the stationary spot the user is in at the time of the vocalization.
Regarding the 35 USC 101 rejection, applicant argues “the claims are directed to a technical improvement in the field of loudspeakers; specifically, to techniques improving the sound field created by poor speaker placement” (see applicant’s arguments dated June 18th, 2026, page 10), however this is not in the claim language. The claim only contains a generic adjusting step, without explaining what is adjusted or how it is adjusted.
Applicant further agues “In addition, the independent claims require determining human directivity index pattern data based on a vocalization as recorded by a microphone. The human mind is not a microphone, nor can the human mind interface with a microphone, and therefore these features that form the basis by which speaker placement is determined do not recite any mental process” (see applicant’s arguments dated June 18th, 2026, page 10). However no microphone is required by the scope of the claim. The claims require a determination, not a measurement. The determined DI pattern data is based on a recorded signal. After a signal has been recorded it is just data.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS JOHN SUTHERS whose telephone number is (571)272-0563. The examiner can normally be reached M-F, 8 am -5 pm.
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/DOUGLAS J SUTHERS/Examiner, Art Unit 2695
/VIVIAN C CHIN/Supervisory Patent Examiner, Art Unit 2695
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