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 .
Status of Application
This communication is responsive to the applicant's amendment filed 08/27/2026.
Claims 1-10, 12, 14-17, and 21-25 are pending. Claims 11, 13, and 18-20 have been cancelled.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-10, 12, 14-17, 21-23, and 25 is/are rejected under 35 U.S.C. 102(a)(1) / (a)(2) as being anticipated by Virolainen et al (WO 2020/141261, hereinafter, Virolainen. This reference is presented in IDS filed 05/13/2025).
Regarding claim 1, Virolainen discloses an apparatus (see Figs. 2, 4) comprising: at least one processor; and at least one memory storing instructions that, when executed with the at least one processor (pg 9, line 33-pg 10, line 3), cause the apparatus at least to: obtain input data for a trained machine learning model wherein the input data is based on audio signals from two or more microphones configured for spatial audio capture (fig. 2, 200b, 212, pg 32, line 13-21); determine, using the input data and the trained machine learning model, spatial information relating to one or more sources of at least a category captured within the audio signals (pg 32, line 23-pg 33, line 3); separate, at least partially, a portion of the audio signals corresponding to the one or more sources of at least the category from a remainder of the audio signals (fig. 4, pg 29, line 1-12); and process the portion corresponding to the one or more sources of at least the first category based on the spatial information determined using the trained machine learning model and process at least the remainder of the audio signals based on information in two or more of the audio signals (fig. 4, pg 23, line 25-pg 25, line 14).
Regarding claims 2-4, Virolainen discloses the apparatus to increase the relative volume of the one or more sources of at least the first category compared to the remainder of the audio signals (per claim 2); use the trained machine learning model is-used to separate the portion of the audio signals corresponding to the one or more sources of at least the category from the remainder of the audio signals (per claim 3); and use a different machine learning model to separate the portion of the audio signals corresponding to the one or more sources of at least the category from the remainder of the audio signals (per claim 4) (see pg 22, line 4-34).
Regarding claim 5, Virolainen discloses the apparatus as claimed in claim 1, wherein the one or more sources of at least the category comprise speech (see pg 32, line 33-37).
Regarding claim 6, Virolainen discloses the apparatus as claimed in claim 1, wherein the remainder of the audio signals comprise ambient noise (see pg 29, line 17-19, 24-33).
Regarding claim 8, Virolainen discloses the apparatus as claimed in claim 1, wherein signal transform and conversion generally including the well-known covariance matrix (see pg 24, line 1-pg 25, line 13).
Regarding claim 9, Virolainen discloses the apparatus as claimed in claim 1, see pg 23, line 25-33, wherein the focus direction for the audio signal is the “steering vector”.
Regarding claims 10, and 12, Virolainen discloses the apparatus as claimed in claim 1 that obtain the portion corresponding to the one or more sources of at least the category; or direct a beamformer towards the one or more sources of at least the category (per claim 10), and to apply a filter is to the beamformed signal to emphasize the portion based upon the one or more sources of at least the category and suppress the remainder of the audio signals (per claim 12) (see pg 30, line 16-26).
Method claims 14-16, 21-23, and 25 are rejected for the same reasoning as set forth for the rejection of apparatus claims 1-6, and 8-9 since the apparatus claims perform the same functions as the method claims.
Claim 17 recite “a non-transitory program storage device readable with an apparatus, tangibly embodying a program of instructions executable with the apparatus for performing operation” of method claim 14, and it is rejected for the same reasoning as set forth for the rejection of apparatus claim 1 since the apparatus claim perform the same functions as the method of claim 17.
Allowable Subject Matter
Claims 7, and 24 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Laitinen et al, McElveen et al, and Nongpiur et al disclose various audio processing apparatus for spatial audio processing and control based on audio signal capturing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XU MEI whose telephone number is (571)272-7523. The examiner can normally be reached on Monday-Friday 10-6:30 est.
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/XU MEI/ Primary Examiner, Art Unit 2695 09/29/2026
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