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 .
Specification
In the “Cross Related Application” section, the parent application has now issued as a US Patent. Please update the specification. Correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1,4-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,198,715 . Although the claims at issue are not identical, they are not patentably distinct from each other because the additional limitations in the claims of the ‘715 patent are not necessary to realize the functionality of the claims in the instant invention.
Examiner notes the two mapping tables below. The first table, maps claim numbers to claim numbers. The second table contains the claim language of the claims of the instant application and the issued U.S.Patent. Follow the first mapping table to compare the claim language extracted from the second table.
19/016532
12,198,715
1
1
4
3
5
1+2
6
1
7
6
8
4
9
5
10
8
11
9
12
11
13
12
14
13
15
3,6
16
7
17
11-13
18
15
19
16
20
17-20
19/01632
12,198,715
1. A computer-implemented method for generating an impulse response (IR) representing a sound wave propagation from at least one sound source received at a listening point in a room, the method comprising: obtaining a generated impulse response at the listening point in the room from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input, wherein the generated impulse response is generated using the neural network architecture trained according to: obtaining a 3D model of the room comprising the at least one sound source virtually emitting sound in the room; obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; processing the training group of simulated impulse responses to generate a training group of compressed simulated impulse responses; and training an IR neural network using the training group of compressed simulated impulse responses and corresponding positions of the predefined listening points as input.
2. The computer-implemented method according to claim 1, wherein the training group of simulated impulse responses are processed by an unsupervised learning neural network to generate compressed simulated impulse responses.
3. The computer-implemented method according to claim 2, wherein the unsupervised neural network is a neural network configured to use a latent space representation.
4. The computer-implemented method according to claim 3, wherein the neural network configured to use a latent space representation is an autoencoder, and wherein the autoencoder is trained by a training.
5. The computer-implemented method according to claim 4, wherein the training comprises: training an encoder of the autoencoder by using the training group of simulated impulse responses as input in order to obtain a corresponding training group of compressed simulated impulse responses as outputs; and training a decoder of the autoencoder by using the training group of compressed impulse responses as input in order to obtain a corresponding training group of uncompressed simulated impulse responses as outputs.
6. The method according to claim 5, wherein obtaining the generated impulse response further comprises: generating a compressed generated impulse response using the IR neural network; and generating the generated impulse response by using the trained decoder of the autoencoder to decompress the compressed generated impulse response.
7. The method according to claim 5, wherein training the neural network architecture further comprises: obtaining a validation group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room, wherein each of the simulated impulse responses is generated using a wave-based solver, a geometrical acoustics solver or any combinations thereof; and validating the autoencoder and the neural network using the validation group of simulated impulse responses, wherein the validation group of simulated impulse responses is different from the training group of simulated impulse responses.
8. The method according to claim 1, wherein training the IR neural network comprises using the 3D model of the room as input.
9. The method according to claim 1, wherein training the IR neural network comprises using at least one of a position and a directivity of at least one sound source as input.
10. The method according to claim 1, wherein the method further comprises generating a first part of the generated impulse response using a first neural network architecture, wherein a first part of the simulated impulse responses is obtained, and wherein the first part includes a predetermined set of first data points.
11. The method according to claim 10, wherein the predetermined set of first data points corresponds to at least one of a first, second, and third reverberation of the simulated impulse response.
12. The method according to claim 10, wherein the method further comprises generating a second part of the generated impulse response using a second neural network architecture, wherein a second part of the simulate impulse response is obtained, and wherein the second part includes a predetermined set of second data points.
13. The method according to claim 12, wherein the predetermined set of second data points corresponds to a second reverberation following a first reverberation corresponding to the set of first data points.
14. The method according to claim 12, wherein the first part of the generated impulse response and the second part of the generated impulse response are combined into a combined generated impulse response.
15. The method according to claim 1, wherein the simulated impulse responses are generated using a wave-based solver, a geometrical acoustics solver, or any combinations thereof.
16. The method according to claim 1, further comprising generating a reverberating audio signal received at the listening point in the room by: obtaining the generated impulse response; obtaining an anechoic audio signal; and generating the reverberating audio signal received at the listening point by convolving the anechoic audio signal and the generated impulse response.
17. A computer implemented method for training a neural network architecture to generate an impulse response signal for a position in a 3D model of a room, the method comprising: obtaining a 3D model of the room comprising at least one sound source virtually emitting sound in the room; obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; processing the training group of simulated impulse responses to generate a training group of compressed simulated impulse responses; and training an IR neural network using the training group of compressed simulated impulse responses and a corresponding position of the predefined listening points as input.
18. A system for generating an impulse response representing a sound wave propagation from at least one sound source received at a listening point in a room, the system comprising a computer system having processing circuitry coupled to a memory, and a neural network architecture coupled to the computer system, wherein the processing circuitry is configured to: obtain a generated impulse response at the listening point in the room from the neural network architecture by providing to the neural network architecture at least a position of the listening point as input, wherein the generated impulse response is generated using the neural network architecture trained according to: obtaining a 3D model of the room comprising at least one sound source virtually emitting sound in the room; and obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; processing the training group of simulated impulse responses to generate a training group of compressed simulated impulse responses; and training an IR neural network using the training group of compressed simulated impulse responses and corresponding positions of the predefined listening points as input.
19. The system according to claim 18, wherein the processing circuitry is further configured to: generate a compressed generated impulse response using the IR neural network; and generate the generated impulse response by using a trained decoder of an autoencoder to decompress the compressed generated impulse response.
20. The system according to claim 18, wherein the neural network architecture includes a first neural network architecture and wherein the processing circuitry is configured to generate a first part of the generated impulse response using the first neural network architecture, wherein a first part of the simulated impulse responses is obtained, and wherein the first part includes a predetermined set of first data points, and wherein the neural network architecture further includes a second neural network architecture, and wherein the processing circuitry is further configured to generate a second part of the generated impulse response using the second neural network architecture, and wherein a second part of the simulated impulse responses is obtained and wherein the second part includes a predetermined set of second data points.
1. A computer-implemented method for generating an impulse response (IR) representing a sound wave propagation from at least one sound source received at a listening point in a room, the method comprising: obtaining a generated impulse response at the listening point in the room from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input, wherein the generated impulse response is generated using the neural network architecture trained according to: obtaining a 3D model of the room comprising the at least one sound source virtually emitting sound in the room; and obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; training an autoencoder, the training comprising: training an encoder of the autoencoder by using the training group of simulated impulse responses as input in order to obtain a corresponding training group of compressed simulated impulse responses as outputs; and training a decoder of the autoencoder by using the training group of compressed impulse responses as input in order to obtain a corresponding training group of uncompressed simulated impulse responses as outputs; and training an IR neural network using the training group of compressed simulated impulse responses of the autoencoder and the corresponding positions of the predefined listening points as input.
2. The method according to claim 1, wherein obtaining the generated impulse response further comprises: generating a compressed generated impulse response using the IR neural network; and generating the generated impulse response by using the decoder of the trained autoencoder to decompress the compressed generated impulse response.
3. The method according to claim 1, wherein training the neural network architecture further comprises: obtaining a validation group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room, wherein each of the simulated impulse responses is generated using at least a wave-based solver; and validating the autoencoder and the neural network using the validation group of simulated impulse responses, wherein the validation group of simulated impulse responses is different from the training group of simulated impulse responses.
4. The method according to claim 1, wherein training the IR neural network comprises using the 3D model of the room as input.
5. The method according to claim 1, wherein training the IR neural network comprises using at least one of a position and a directivity of at least one sound source as input.
6. The method according to claim 1, wherein the simulated impulse responses are generated using at least a wave-based solver.
7. The method according to claim 1, further comprising: generating a reverberating audio signal received at the listening point in the room by: obtaining the generated impulse response; obtaining an anechoic audio signal; and generating the reverberating audio signal received at the listening point by convolving the anechoic audio signal and the generated impulse response.
8. The method according to claim 1, wherein the method further comprises generating a first part of the generated impulse response using a first neural network architecture, wherein a first part of the simulated impulse responses is obtained, and wherein the first part includes a predetermined set of first data points.
9. The method according to claim 8, wherein the predetermined set of first data points corresponds to at least one of a first, second, and third reverberation of the simulated impulse response.
10. The method according to claim 8, wherein the predetermined set of first data points includes at least 2000 data points.
11. The method according to claim 8, wherein the method further comprises generating a second part of the generated impulse response using a second neural network architecture, wherein a second part of the simulate impulse response is obtained, and wherein the second part includes a predetermined set of second data points.
12. The method according to claim 11, wherein the predetermined set of second data points corresponds to a second reverberation following a first reverberation corresponding to the set of first data points.
13. The method according to claim 11, wherein the first part of the generated impulse response and the second part of the generated impulse response are combined into a combined generated impulse response.
14. A computer implemented method for training a neural network architecture to generate an impulse response signal for a position in a 3D model of a room, the method comprising: obtaining a 3D model of the room comprising at least one sound source virtually emitting sound in the room; obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; training an autoencoder, the training comprising: training an encoder of the autoencoder by using the training group of simulated impulse responses as input in order to obtain a corresponding training group of compressed simulated impulse response as outputs; and training a decoder of the autoencoder by using the training group of compressed impulse responses as input in order to obtain a corresponding training group of uncompressed simulated impulse response as outputs; and training an IR neural network using the training group of compressed simulated impulse responses of the autoencoder and the corresponding position of the predefined listening points as input.
15. A system for generating an impulse response representing a sound wave propagation from at least one sound source received at a listening point in a room, the system comprising a computer system having processing circuitry coupled to a memory, and a neural network architecture coupled to the computer system, wherein the processing circuitry is configured to: obtain a generated impulse response at the listening point in the room from the neural network architecture by providing to the neural network architecture at least a position of the listening point as input, wherein the generated impulse response is generated using the neural network architecture trained according to: obtaining a 3D model of the room comprising the at least one sound source virtually emitting sound in the room; and obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the room; training an autoencoder, the training comprising: training an encoder of the autoencoder by using the training group of simulated impulse responses as input in order to obtain a corresponding training group of compressed simulated impulse responses as outputs; and training a decoder of the autoencoder by using the training group of compressed simulated impulse responses as input in order to obtain a corresponding training group of uncompressed simulated impulse responses as outputs; and training an IR neural network using the training group of compressed simulated impulse responses of the autoencoder and the corresponding positions of the predefined listening points as input.
16. The system according to claim 15, wherein the processing circuitry is further configured to: generate a compressed generated impulse response using the IR neural network; and generate the generated impulse response by using the decoder of the trained autoencoder to decompress the compressed generated impulse response.
17. The system according to claim 15, wherein the processing circuitry is further configured to obtain an anechoic audio signal and to generate a reverberating audio signal received at the listening point by convolving the anechoic audio signal and the generated impulse response.
18. The system according to claim 15, wherein the neural network architecture includes a first neural network architecture and wherein processing circuitry is configured to generate a first part of the generated impulse response using the first neural network architecture, wherein a first part of the simulated impulse responses is obtained, and wherein the first part includes a predetermined set of first data points.
19. The system according to claim 18, wherein the neural network architecture further includes a second neural network architecture, and wherein the processing circuitry is further configured to generate a second part of the generated impulse response using the second neural network architecture, and wherein a second part of the simulated impulse responses is obtained and wherein the second part includes a predetermined set of second data points.
20. The system according to claim 19, wherein the processing circuitry is further configured to combine the first part of the generated impulse responses and the second part of the generated impulse responses into a combined generated impulse response.
Claim Rejections - 35 USC § 102
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.
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.
Claim 17 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sporer (20230164509).
As per claim 17, Sporer (20230164509) teaches a computer implemented method for training a neural network architecture to generate an impulse response signal for a position in a 3D model of a room (as, generating impulse responses based on sound sources in a room– para 0040 – see binaural room impulse responses), the method comprising:
obtaining a generated impulse response at the listening point (as direction of arrival for a given source is used – para 0156, and position—para 0189) in the room from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input (as direction of arrival for a given source is used – para 0156, and position—para 0189), wherein the generated impulse response is generated using the neural network architecture trained according to (in processing the acoustic properties of the room with differing sound sources, using a neural network to process these characteristics – para 0015, operating on characteristics of the sound sources, wherein, using impulse responses based on the audio source – para 0039, 0040),
obtaining a 3D model of the room comprising at least one sound source virtually emitting sound in the room (as 3D modeling – para 0156, 165, and using waveform calculations to reduce computational complexity – para 0169; examiner notes that it is old and notoriously well known in the art of 3D room modeling to use virtual sound sources to measure the sound responses – e.g., see Mahabub et al (20120213375) abstract, para 0084),
- processing the training group of simulated impulse responses to generate a training group of compressed simulated impulse responses (as, simulation of different acoustical conditions – para 0015, para 0156, 165);
- training an IR neural network using the training group of compressed simulated impulse responses of the autoencoder and the corresponding positions of the predefined listening points as input. (as the decoder of encoder-decoder networks (para 0173) within as part of auto-encoder networks – para 0016, para 0166; based on mapping of 3D in a room – para 0156, 0165).
Allowable Subject Matter
Claims 1-16,18-20 are allowed over the prior art of record.
The following is an examiner’s statement of reasons for allowable subject matter:
As per the independent claims, the claim limitations toward “obtaining a generated impulse response at the listening point in the room from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input,”, as well as, claim limitations clarify how the neural network architecture that generates the IR is trained —i.e., by “obtaining a 3D model of the room comprising the at least one sound source virtually emitting sound in the room.”, is not explicitly taught by the prior art of record. Sporer ( 2023/0164509) discloses a technique for performing real-time audio suppression and/or enhancement of various sound sources -- see Sporer, para 0152-0153. As part of the disclosed technique, Sporer obtains a plurality of binaural room impulse responses (BRIRs), para 0065. However, rather than obtain the BRIRs “from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input,” as recited in amended claim 1, Sporer generates the BRIRs at a pair of headphones worn by the user. e.g., Sporer, 0147-0148. As seen in this figure, Sporer provides an analyzer (152) that determines the plurality of BRIRs. So determined, the analyzer then outputs the BRIRs to a loudspeaker signal generator (154). According to Sporer, both the analyzer and the loudspeaker signal generator of Figure 1 form a signal generator (150) integrated into the user’s headphones (e.g., Sporer, [4[0099-0100, 0113]; Figures 4-5B) or into a “remote device” associated with the user (e.g., the user's Smartphone). E.g., Sporer, J§[0114-0115]. Additionally, one or more microphones that record actual sounds in a room are also integrated into the headphones. Based on the recorded sounds, the analyzer in Sporer determines the BRIRs for whichever audio source produced the sounds. According to an embodiment, e.g., the headphone may include at least two
headphone capsules and, e.g., at least one microphone for measuring sound in each of the two headphone capsules, wherein, e.g., the at least one microphone for measuring the sound may be arranged in each of the two headphone capsules. Here, e.g., the analyzer 152 may be configured to perform the determination of the plurality of the binaural room impulse responses by using the measurement of the at least one microphone in each of the two headphone capsules; generating a plurality of BRIRs at a set of headphones worn by a user, as disclosed in Sporer, does not teach “obtaining a generated impulse response at the listening point in the room from a neural network architecture by providing to the neural network architecture at least a position of the listening point as input,” as recited in amended claim 1. Indeed, no one of ordinary skill in the art would equate a pair of headphones and its functionality to that of the neural network architecture recited in amended claim 1. This is especially true given the commonly understood meaning of “neural network architecture,” as well as the discussion of a neural network architecture in Applicant’s specification, p. 15, line 27 —p. 16, line 14. In more detail, Sporer is concerned with a process for removing or suppressing some of the sounds in a room so that a user is able to hear another, different sound of interest. For example, the disclosed system may be used to enhance a parent’s voice over other sounds so that their children can hear them in crowds. E.g., Sporer, para 0249-0250. Additionally, the system of Sporer may be used to suppress selected sounds in the environment so that the user can hear other sounds instead (e.g., the suppression of singing voices in a karaoke setting). E.g., Sporer, [0303]. In other embodiments, the disclosed system can be used in military/battlefield applications to enable soldiers, for example, to communicate over the sounds of battle. E.g., Sporer, [0280]. On the other hand, the claimed embodiments generate impulse responses using a trained neural network architecture for the purpose of simulating sound propagation. Indeed, the noise suppression/enhancement disclosed in Sporer is an entirely different concept than simulating sound propagation, as claimed. In fact, both are used in completely different scenarios. The system of Sporer is used for scenarios requiring the real-time suppression/enhancement of audio sounds in a room. The method of amended claim 1, in contrast, can be used in situations where a user enjoys a high degree of freedom to move around a room. As stated above, the method of amended claim 1 permits such movement while eliminating the need for performing additional simulations to produce impulse responses. Additionally, the method of claim 1 reduces the time needed for performing simulation as well as the load on computing resources, thereby ensuring a near “real-time” impulse response generation within a 3D model of a room. Furthermore to the independent claims, the claim limitations clarify how the neural network architecture that generates the IR is trained —i.e., by “obtaining a 3D model of the room comprising the at least one sound source virtually emitting sound in the room.” E.g., Spec., p. 10, Il. 7-10; p. 10, Il. 24 —p. 11, Il. 2. Sporer also fails to teach this aspect. Sporer does disclose deep learning models for use in detecting audio signals from different audio sources, as well as for sound separation processing. E.g., Spec., q9[0102, 0125]). However, nowhere does Sporer mention that any of the deep learning models are 3D models of a room comprising “at least one sound source virtually emitting sound in the room,” as is now recited in the independent claims. Sporer shows an “artificial audio object” in Figure 8. However, other than acknowledging the existence of such “artificial audio objects” in a user’s personalized listening environment, there is no explanation whatsoever of this object in Sporer. More importantly, Sporer does not teach that such “artificial audio objects” are comprised in an obtained 3D model of a room. Hacihabiboglu et al (20110015924) teaches acoustic source distinguishment with considering reverb parameters (para 0053, 0067) with convolving the anechoic sounds with impulse responses generating reverberant recordings (para 0089). Borgstrom et al (20210074282) teaches the use of neural networks in developing room impulse responses (para 0019). Martinez-Ramirez (20230197043) teaches the use of DNN/RNN, different types networks, for different acoustic processing – para 0024, para 0078, para 0088. However, none of the prior art of record explicitly teach the limitations of the independent claims, as noted above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Please see related art listed on the PTO-892 form.
Additionally, examiner notes the following reference found, to be applicable to the claim/spec features:
In the art of using virtual sound sources to measure sound responses:
Mahabub et al (20120213375) teaches the use of virtual sound source – abstract
Herre et al (20130259243) teaches movement of virtual sound sources to determine response (para 0029).
In the art of using neural networks for learning impulse responses for sound effects:
Borgstrom et al (20210074282) teaches the use of neural networks in developing room impulse responses (para 0019).
Martinez-Ramirez (20230197043) teaches the use of DNN/RNN, different types networks, for different acoustic processing – para 0024, para 0078, para 0088.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 07/04/2026
Prosecution Insights