Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . See 35 U.S.C. § 100 (note).
Art Rejections
Obviousness
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.
Claims 1, 2 and 5 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of US Patent Application Publication 2023/0386490 (effectively filed 20 October 2020) (“Namba”) and US Patent Application Publication 2016/0227320 (published 04 August 2016) (“Harvey”).
Anticipation
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)(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.
Claims 1, 2 and 5 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by US Patent Application Publication 2023/0386490 (effectively filed 20 October 2020) (“Namba”).1
Claim 1 is drawn to “an audio quality conversion device.” The following table illustrates the correspondence between the claimed device and the Namba reference.
Claim 1
The Namba Reference
“Claim 1 An audio quality conversion device comprising:
The Namba reference similarly describes a signal processing device to convert a low-quality (i.e., low signal-noise (SN) ratio) audio signal into a high-quality (i.e., high SN ratio) audio signal based on a machine learning algorithm. Namba at Abs., ¶¶ 2–8, 94–133, FIGs.3, 4.
“a controller equipped with an artificial neural network that performs learning using a plurality of pieces of audio data obtained by recording a predetermined audio event in different recording environments and environmental data related to the recording environments corresponding to the audio data; and
Namba’s device similarly includes a combination of a recording device 51 and a learning device 52. Id. at ¶¶ 94–124, FIG.3. Device 52 has multiple different embodiments. In general, each embodiment includes a learning unit 84 formed using a deep neural network (DNN). Id. Learning unit 84 generates a set of coefficients. Id. The learning process involves recording audio signals and additional information. Id. Namba describes several types of additional information, including movement, position and object type. Id. The learning process uses the additional information to label the recorded audio. Id. The learning process further labels recorded audio based on environmental factors, like how much synthetic noise/reverb has been added to simulate noisy recording environments. Id. at ¶¶ 320, 354–362.
“an audio input unit configured to receive external sound and generate audio recording data,
Namba’s device includes a recording device 51 used to record audio for learning and for sound source generation. Id. at ¶¶ 98–100, 191, FIG.3.
“wherein the controller converts the audio recording data generated by the audio input unit based on a result of the learning by the artificial neural network,
Namba’s device further includes a sound source generating device 112 that uses the coefficients generated by the learning process to generate a high SN signal based on the signal recorded by the recording device. Id. at ¶¶ 150–183, FIGs.6–8.
“wherein the environmental data includes at least one of information related to a distance between a location in which the audio event occurs and a microphone configured to record the audio data, information related to spatial reverberation in a location in which the microphone is present, and information related to noise in a location in which the microphone is present,
Namba describes labeling audio data with additional information, or environmental data. Namba at ¶¶ 137, 138, 324, 354–362, FIGs.5, 18. That additional information includes distance, position, noise amount and reverb amount. Id.
“wherein the audio input unit includes a first microphone and a second microphone spaced a predetermined distance from each other and installed on a body of the audio quality conversion device implemented as a portable terminal and the audio data acquired from the first microphone has a different distance tag value allocated thereto than audio data acquired from the second microphone,
Namba describes recording device 51 as including an array of microphones 61 that record the audio used by learning device 52. Namba at ¶¶ 99–102, 191. FIG.3.
Namba further describes providing multiple recording devices 51 (having different location tags by virtue of having different positions recorded by their corresponding position measuring unit 63). Id. at ¶¶ 55, 97, 145, 251.
Namba describes that multiple recording devices 51 may be incorporated into a single object. Id. at ¶ 97. Further, Namba describes embodying the object as a mobile body, or portable terminal as claimed. Id. at ¶¶ 48, 98, 151.
“wherein the artificial neural network performs learning using the audio data acquired from the first microphone and the audio data acquired from the second microphone and the respective distance tag values allocated thereto, and
Namba describes performing learning by integrating audio and data (including ranging/position/distance tags) captured from multiple recording devices 51. Id. at ¶¶ 55, 97, 145, 194–196, 251.
“wherein the artificial neural network performs the learning using first audio data obtained through recording under an environmental condition in which a noise level is smaller than or equal to a preset value and second audio data obtained by synthesizing the first audio data with pre-stored noise data.”
Similarly, Namba’s learning process includes superimposing synthesized noise onto recorded audio that is otherwise assumed to have a low noise level. Namba at ¶¶ 354–362.
Table 1
For the foregoing reasons, the Namba references anticipates all limitations of the claim.
Claim 2 depends on claim 1 and further requires the following:
“further comprising a conversion condition input unit configured to receive information related to the conversion of the audio recording data,
“wherein the information input to the conversion condition input unit includes a variable related to the environmental data indicating a target recording environment,
“wherein the controller converts the audio recording data into sound quality characteristics corresponding to the target recording environment using the artificial neural network based on the information input to the conversion condition input unit.”
Similarly, Namba’s device receives conversion condition information from sensors to instruct the sound source generating device 112 how to convert the audio recorded by recording device 51. Namba at ¶¶ 164, 169–174, 340, FIGs.7, 8, 20. For example, device 112 receives position, location and environment information. Id. Additionally, Namba describes in the learning phase training its DNN how to reproduce a plurality of different environments, or environment conditions that amount to a plurality of target recording environments. Id. at ¶¶ 319–329, FIG.18. Namba further describes during the sound source generating phase acquiring environment condition information (i.e., a variable related to environmental data indicating a target recording environment) in order to reproduce that target recording environment during the sound source generating phase. Id. at ¶¶ 330–337, FIG.19. For the foregoing reasons, the Namba references anticipates all limitations of the claim.
Claim 5 depends on claim 1 and further requires the following:
“wherein the artificial neural network performs learning using first environmental data corresponding to first audio data and second environmental data corresponding to second audio data, the first environmental data and the second environmental data having different values from each other.”
Namba describes training its DNN to model numerous environments corresponding to different training audio. Namba at ¶¶ 320–329, FIG.18. The environments include numerous differing environmental data values, such as absorption coefficients, shoes worn by people in the environment, reverberation characteristics of a space, a type of space (e.g., open or closed), volume, 3D shape, weather, ground surface state and a type of ground surface. Id. at ¶ 320. For the foregoing reasons, the Namba references anticipates all limitations of the claim.
Summary
Claims 1, 2 and 5 are rejected under at least one of 35 U.S.C. §§ 102 and 103 as being unpatentable over the cited prior art. In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Response to Applicant’s Arguments
Applicant’s Reply (18 February 2026) has substantively amended all the claims. This Office action has updated the rejections accordingly.
Applicant’s Reply at 4–8 include comments pertaining to the rejections presented in the previous Final Rejection (18 November 2025). Those comments have been considered, but are unpersuasive.
Regarding claim 1, Applicant comments that the combination of Namba and ProSound is based on hindsight reasoning due to the size differential between a microphone worn on a sports player and a Decca tree. (Reply at 5–6). This comment mischaracterizes Namba as strictly applying to miniature housings worn on a player, see Namba at ¶ 48, and improperly focuses on combining prior art embodiments without considering everything taught by the cited prior art. The comment is also moot in light of the new grounds of rejection presented in this Office action.
Applicant comments that Namba does not describe assigning distinct numerical distance tag values to audio data from local microphones on a single body. (Reply at 6). Applicant further comments that Namba’s position information described in ¶ 50 refers to macro-location tracking of a moving object (e.g., GPS coordinate of a player on a field), not relational distance metadata allocated to an audio waveform for neural network training. (Id.) This comment is unpersuasive because it is premised on an incomplete understanding of Namba. Namba at ¶ 50 describes including multiple types of positioning sensors, including GPS and ranging devices. Namba further explains at ¶ 104 using that GPS and range data to determine the location of an object in a recording environment coordinate system. As one of ordinary skill would understand, plotting an object on a coordinate amounts to the claimed limitation in question, which is providing environmental data that includes at least one of information related to a distance (i.e., coordinate (e.g., X, Y, Z) position of Namba’s recording devices) between a location in which the audio event occurs (i.e., Namba’s the coordinate system) and a microphone configured to record the audio data (i.e., a recording device(s) in one of Namba’s objects).
Regarding claim 2, Applicant comments that Namba does not receive a user-inputted target recording environment, but rather acquires current environmental conditions and passively selects a pre-existing coefficient data corresponding to that reality. (Reply at 7). First, the claim does not require receiving a user-inputted target recording environment; rather, claim 2 simply requires receiving information that includes a variable related to the environmental data indicating a target recording environment. Namba describes a corresponding variable—namely, environment condition information that indicates the environment in which a sound is actually being recorded. Namba at ¶¶ 333–335. This environment information, which may be provided by a user or determined automatically, indicates a target recording environment that should be replicated in the final recording with a quality of sound that matches reality. Id. at ¶¶ 1, 2, 321, 329, 346. Namba may not describe modifying an actual recording environment to simulate a completely different environment, but claim 2 does not require such a feature.
Regarding claim 5, Applicant comments that Namba does not describe the first and second environmental data that respectively correspond to first and second audio data and have different values from each other. (Reply at 7–8). The rejection of claim 5 has been updated to clarify the grounds of rejection. Namba describes training its DNN to model many different environments based on numerous environmental parameters of different value, including absorption coefficients, type of shoes worn by people in the environment, reverberation characteristics, a type of space (e.g., open or closed), a volume of the space, a 3D shape of the space, weather, ground surface state and a type of ground surface. Namba at ¶¶ 319–321.
For the foregoing reasons, Applicant has not persuasively established any error in the Office action. All the rejections will be maintained.
Conclusion
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 WALTER F BRINEY III whose telephone number is (571)272-7513. The examiner can normally be reached M-F 8 am-4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carolyn Edwards can be reached at 571-270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Walter F Briney III/
Walter F Briney IIIPrimary ExaminerArt Unit 2692
3/31/2026
1 Further review of Namba has revealed that Namba describes an embodiment including multiple recording devices (each with its own set of position sensors) located in a single object, which may be implemented as a mobile body, or portable terminal. See Namba at ¶¶ 48, 63, 97, 98, FIG.3.
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