NON-FINAL REJECTION
The instant 19/342,362 application is a reissue application of U.S. Patent 11,769,519 B2 to Hartung et al. “the ‘519 Patent”), which issued September 26, 2023 from U.S. Patent Application Ser. No. 17/315,991, filed May 10, 2021 as a continuation application of U.S. Patent Application Ser. No. 16/570,009, filed September 13, 2019 as a continuation application of U.S. Patent Application Ser. No. 15/718,579, filed September 28, 2017 as a continuation application of U.S. Patent Application Ser. No. 14/864,533, filed September 24, 2015. The ‘519 Patent has an earliest possible U.S. filing date of September 17, 2015 based on provisional application 62/220,158.
Claims 1-21 were originally pending in this application. By way of a preliminary amendment filed with the application, claims 22-38 are added. Thus claims 1-38 are pending and are rejected below.
This action is Non-Final.
Reissue
The Examiner has determined that there are no other continuations, reissues, reexaminations, inter partes reviews, or other AIA trials or appeals currently pending with respect to the ‘519 Patent. A litigation search has determined there to be no pending litigation as to the ‘519 Patent.
Applicant is reminded of the continuing obligation under 37 CFR 1.178(b) to timely apprise the Office of any prior or concurrent proceeding in which the ‘519 Patent is or was involved. These proceedings would include interferences, reissues, reexaminations, and litigation. Applicant is further reminded of the continuing obligation under 37 CFR 1.56, to timely apprise the Office of any information which is material to patentability of the claims under consideration in this reissue application. These obligations rest with each individual associated with the filing and prosecution of this application for reissue. See also MPEP §§ 1404, 1442.01 and 1442.04.
Because the instant ‘519 Patent is not deemed to contain claims having an effective date prior to March 16, 2013, the America Invents Act First Inventor to File (“AIA -FITF”) provisions apply, rather than the pre-AIA provisions. See 35 U.S.C. § 100 (note) and 35 U.S.C. § 100 (pre-AIA ). In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of any statutory basis for a 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.
Reissue Amendment
The amendment filed with the instant reissue application is objected to for the following reasons:
The amendment filed with this application does not meet 37 CFR 1.173(c) and (d)(2). This is because no statement of support for the new claims is provided, and the new claims are not underlined in their entirety. See also MPEP § 1453.
Reissue Declaration
The reissue oath/declaration filed with this application is defective (see 37 CFR 1.175 and MPEP § 1414) because of the following:
The declaration does not identify a broadened claim. Further, the newly-added claims are rejected under 35 USC 251 below, and thus the presentation of such claims is not an error that is correctable by reissue.
A new declaration is required in response to this Office action.
Claim Rejections - 35 USC § 251
The reissue claims must be for the same invention as that disclosed as being the invention in the original patent, as required by 35 U.S.C. 251. The entire disclosure, not just the claim(s), is considered in determining what the patentee objectively intended as the invention. The determination of the original patent requirement is "an essentially factual inquiry confined to the objective intent manifested by the original patent." In re Amos, 953 F.2d 613, 618, 21 USPQ2d 1271, 1274 (Fed. Cir. 1991) (quoting In re Rowand, 526 F.2d 558, 560, 187 USPQ 487, 489 (CCPA 1975)) (emphasis added); See also In re Mead, 581 F.2d 251, 256, 198 USPQ 412, 417 (CCPA 1978) ("Thus, in Rowand and similar cases, ‘intent to claim’ has little to do with ‘intent’ per se, but rather is analogous to the requirement of § 112, first paragraph, that the specification contain ‘a written description of the invention, and of the manner and process of making and using it.’").
The "original patent" requirement of 35 U.S.C. 251 must be understood in light of In re Amos, supra, where the Court of Appeals for the Federal Circuit stated:
We conclude that, under both Mead and Rowand, a claim submitted in reissue may be rejected under the "original patent" clause if the original specification demonstrates, to one skilled in the art, an absence of disclosure sufficient to indicate that a patentee could have claimed the subject matter. Merely finding that the subject matter was "not originally claimed, not an object of the original patent, and not depicted in the drawing," does not answer the essential inquiry under the "original patent" clause of § 251, which is whether one skilled in the art, reading the specification, would identify the subject matter of the new claims as invented and disclosed by the patentees. In short, the absence of an "intent," even if objectively evident from the earlier claims, the drawings, or the original objects of the invention is simply not enough to establish that the new claims are not drawn to the invention disclosed in the original patent.
953 F.2d at 618-19, 21 USPQ2d at 1275.
Similarly, the disclosure requirement in Amos must be understood in light of Antares Pharma Inc., v. Medac Pharma Inc. and Medac GMBH, 771 F.3d 1354, 112 USPQ2d 1865 (Fed. Cir. 2014). In Antares Pharma, Inc., the court found "[n]owhere does the specification disclose, in an explicit and unequivocal manner, the particular combinations of safety features claimed on reissue, separate from the jet injection invention." Antares Pharma, Inc., 771 F.3d at 1363, 112 USPQ2d at 1871. Specifically, the court stated "[a]lthough safety features were mentioned in the specification, they were never described separately from the jet injector, nor were the particular combinations of safety features claimed on reissue ever disclosed in the specification." Antares Pharma, Inc., 771 F.3d at 1363, 112 USPQ2d at 1871. In other words, the court found that the patent only disclosed one invention, which was a particular class of jet injectors, due to the clearly repetitive use of "jet injector" in the title, the abstract, the summary of the invention, and the entirety of the specification of the patent. As a result, the claims in the reissue patent to the safety features on a generic injector (e.g., a non-jet injector) were held to violate the original patent requirement of 35 U.S.C. 251.
To satisfy the original patent requirement where a new invention is sought by reissue, "… the specification must clearly and unequivocally disclose the newly claimed invention as a separate invention." Antares Pharma, Inc., 771 F.3d at 1363, 112 USPQ2d at 1871. Accordingly, claims drawn to an invention comprising a newly claimed combination of features that were only disclosed in the original patent as suggested alternatives (and not as a single combination) or only as part of the original invention and not as an invention separate from the original invention would not satisfy the original patent requirement. See also Forum US, Inc. v. Flow Valve, LLC, 926 F.3d 1346, 1352, 2019 USPQ2d 221227 (Fed. Cir. 2019) ("nowhere do the written description or drawings disclose that arbors are an optional feature of the invention. Even if a person of ordinary skill in the art would understand that the newly claimed, arbor-less invention would be possible, that is insufficient to comply with the standard set forth in Industrial Chemicals [315 U.S. 668 (1942)] and Antares."). "The ‘original patent’ standard and the written description requirement are not the same. Where the written description requirement is based on what the skilled artisan would have understood was within the possession of the inventor, recent Federal Circuit case law indicates that the original patent requirement under § 251 requires something more." See Ex parte Sandwick, Appeal No. 2018-008369, op. at 22 (PTAB July 23, 2019) (Rejection under 35 U.S.C. 251 was affirmed because the patent did not describe any fabrication method other than casting. While one of ordinary skill in the art would have understood that other fabrication methods, such as injection molding or 3D printing, were possible or conventional, the reissue claims that did not include casting did not comply with the original patent requirement.)
Claims 22-38 are rejected under 35 U.S.C. §251 as not being for the same invention as that disclosed as being the invention in the original patent.
Here, the Examiner reviews the reissue application to determine whether the original patent requirement is satisfied, by considering if:
(A) the claims presented in the reissue application are described in the original patent specification and enabled by the original patent specification such that 35 U.S.C. 112, first paragraph is satisfied;
(B) nothing in the original patent specification indicates an intent not to claim the subject matter of the claims presented in the reissue application; and
(C) the newly claimed invention is clearly and unequivocally disclosed in the specification as a separate invention with the claimed combination of features.
Claims 22-38 are newly-presented in this reissue and are broadened. Taking claim 22 as exemplary:
22. (New) A method for calibrating an audio playback device, the method comprising: receiving, via an acoustic sensor, data indicative of an acoustic response of the audio playback device within an environment;
determining a calibration profile for the audio playback device based on the received data,
the calibration profile defining at least one audio output characteristic of the audio playback device;
detecting, via a machine learning model, acoustic conditions within the environment that affect the acoustic response of the audio playback device;
generating, using the machine learning model, a modified calibration profile for the audio playback device, wherein the modified calibration profile is generated to at least partially compensate for the detected acoustic conditions when applied to the audio playback device; and
applying the modified calibration profile to the audio playback device.
Claim 22 is broadened in comparison to the issued claims in several aspects. First, matter is removed as to the steps of utilizing a neural network to analyze audio information captured by a microphone, instead claiming generally using a machine learning model to determine conditions, not necessarily based on the received data. Second, matter is removed as to determining the presence of an impairment, instead claiming generally determining a calibration profile and applying it.
Looking to the disclosure, the Examiner determines that this new invention claimed is not clearly and unequivocally disclosed as a separate invention. The ‘519 Patent disclosure is towards using a neural network to analyze audio information for determining the presence of an impairment. ‘519 Patent at Title (“DEVICE IMPAIRMENT DETECTION”); id. at Abstract, 2:29-32 (“[e]mbodiments described herein may involve, inter alia, detecting one or more impairments that might affect calibration of one or more playback devices of a media playback system”); 3:35-47 (“[e]xample embodiments contemplated herein utilize a neural network to detect impairments”); 17:44-48 (“[s]ome example embodiments described here may facilitate training a neural network to detect specific impairments or other conditions when provided input data (e.g., sensor data) characteristic of one or more of the impairments”); FIGS 6 and 10.
While 4:15-33 of the disclosure states generally that the techniques described may have more general use, this does not rise to the level of a clear and unequivocal disclosure of a separate invention. Claims drawn to an invention comprising a newly claimed combination of features that were only disclosed in the original patent as suggested alternatives (and not as a single combination) do not satisfy the original patent requirement. MPEP § 1412.01 I.
Further, the only disclosed analysis of the received data is by way of a neural network. Here, however, a more general “machine learning” is claimed, and there is no mention in the disclosure of machine learning outside of the neural network-based processing of the issued claims.
Claims 1-38 are rejected as being based upon a defective reissue Declaration under 35 U.S.C. 251 as set forth above. See 37 CFR 1.175.
The nature of the defect(s) in the Declaration is set forth in the discussion above in this Office action.
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.
Claims 22, 23, 26-31, and 34-38 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pat. PBPUB 2015/0030165A1 to Risberg et al. (“Risberg”).
As to claim 22, Risberg discloses:
A method for calibrating an audio playback device, the method comprising:
Risberg discloses a method for calibrating an audio playback device. Risberg at ¶3, specifying a consumer electronic device (CED).
receiving, via an acoustic sensor, data indicative of an acoustic response of the audio playback device within an environment;
Risberg discloses receiving by way of a microphone data indicative of an acoustic response of the CED within an environment. Risberg at FIG 7b and at ¶135 (“a method 712 for enhancing audio in a consumer electronics device. The method 712 includes integrating a configurable audio enhancement system into a consumer electronics device 714”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”). Optimization system 101 is described as receiving data via a microphone indicative of a response of the device within an environment. Id. at ¶123 stating that the embodiment of FIG7b also operates according to method 201 of FIG 2; ¶77 (“The audio test unit 110, 210 may be configured to accept audio data 105, 205 from a sample CED”); ¶122 (“The CED 10, 610 may include one or more audio sampling components (e.g. microphones, speakers with dual I/O functionality, etc.). The audio sampling component may be used as a form of feedback for assessing the audio performance of the CED 10, 610 in practice.”).
detecting, via a machine learning model, acoustic conditions within the environment that affect the acoustic response of the audio playback device;
Risberg discloses detecting acoustic conditions that affect the acoustic response of the device. Risberg at ¶71 (“[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”); id. at ¶121 (“[b]y using an optimization system 101, 201 in accordance with the present disclosure, to analyze the frequency response, impulse response, etc. of the consumer electronics device 610 an accurate and compensate able calculation of an acoustic signature for the consumer electronics device 610 may be made. Optimal compensating parameters 510 for an associated audio enhancement system 500 can be derived from the acoustic signature”); ¶52 (“[b]y acoustic signature is meant the audible or measurable sound characteristics of a consumer electronic device dictated by its design and/or manufacturing processes, process variations, etc. that influence the sound generated by the consumer electronic device”).
Risberg discloses that this detecting is performed by way of a machine learning model. Id. at ¶70 (“[t]he adaptive optimization system 201 includes an audio testing component 210, a master design record 220, a parameter generation model 260, and a machine learning algorithm 240, configured to train the parameter generation model 260”);
determining a calibration profile for the audio playback device based on the received data, the calibration profile defining at least one audio output characteristic of the audio playback device;
Risberg discloses determining a set of parameters for the CES based on the received data defining an output characteristic of the device. Risberg at ¶15 (“an audio parameter generator for deriving one or more optimal audio parameters from the audio test dataset”; id. at ¶70-71 (“FIG. 2 shows a schematic of an adaptive optimization system 201 configured to tune and/or optimize one or more audio parameters of a consumer electronics device 10, 11, 610” “[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”). This reads a calibration profile.
generating, using the machine learning model, a modified calibration profile for the audio playback device, wherein the modified calibration profile is generated to at least partially compensate for the detected acoustic conditions when applied to the audio playback device; and
Risberg discloses modifying the calibration profile iteratively using the same method above, thus using the machine learning model. This generates a modified calibration profile. Risberg at ¶71 (“[o]ptionally, the substantially optimal AES parameters 270 may be arrived at iteratively. In aspects, the AES parameters 270 may be directed 275 to the audio testing component 210 for further use during each iteration of the testing procedure”). These parameters compensate for the detected conditions. Id. at ¶56 (“[t]hus an AES in accordance with the present disclosure may be optimized late in the design process, during the development process, in the field, at a retail outlet, and/or during a manufacturing process to compensate for one or more of these, generally negative, influences on the acoustic properties in the fully manufactured device.”)
applying the modified calibration profile to the audio playback device.
Risberg discloses applying the final calibration profile to the device. Ridberg at ¶15 (“a programming unit to program the optimal audio parameters onto the consumer electronics device”); id. at ¶67 (“The tuned audio parameter dataset may then be uploaded to the consumer electronics device”); ¶69 (“derive a tuned audio parameter set for upload to the consumer electronics device”).
Further as to claim 23:
The method of claim 22, wherein detecting the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprise:
providing an input to the machine learning model, the input based on the received data indicative of the acoustic response of the audio playback device within an environment; and determining that the input produced a particular output from the machine learning model, the particular output indicative of the detected acoustic conditions.
Risberg discloses providing an input to the ML model based on the received data indicative of the acoustic response of the CED and determining that the input produced a particular output indicative of the conditions. Risberg at ¶¶¶70-72.
Further as to claim 26:
The method of claim 22, wherein detecting, via the machine learning model, the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprises:
detecting, via the machine learning model, a lack of an impairment within the environment.
Risberg discloses that the parameters are determined based on a difference between the measured data and a reference dataset. Risberg at ¶P67. One of ordinary skill in the art at the time would have understood that if no difference was found, this result would correspond to a lack of an impairment.
Further as to claim 27:
The method of claim 22, wherein detecting, via the machine learning model. the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprises:
detecting, via the machine learning model, environmental conditions of the environment, wherein the environmental conditions comprise one or more of: indoors/outdoors, size of room, type of room, furnishings of the room and finishes of the room.
Risberg discloses that the environmental conditions may include a stand, which reads furnishings. Risberg at ¶9.
Further as to claim 28:
The method of claim 22, wherein the acoustic sensor comprises a microphone, and wherein receiving the data indicative of the acoustic response of the audio playback device within the environment comprises:
recording audio output of the audio playback device within the environment via the microphone.
As noted above in the rejection of claim 22, Risberg discloses the sensor is a microphone. Further, Risberg discloses recording the audio output of the CED. Risberg at ¶103 (“record acoustic feedback from the tests”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”).
Further as to claim 29:
The method of claim 22, wherein generating the modified calibration profile for the audio playback device comprises:
correcting the calibration profile to at least partially offset the detected acoustic conditions.
Risberg discloses that generating the modified profile includes correcting the profile to offset the conditions as noted above in the rejection of claim 22.
As to claim 30, Risberg discloses:
An audio playback device comprising:
Risberg discloses a calibrating an audio playback device. Risberg at ¶3, specifying a consumer electronic device (CED).
an acoustic sensor;
at least one processor; and
at least one non-transitory computer-readable medium comprising program instructions that are executable by the at least one processor such that the audio playback device is configured to:
Risberg discloses the device comprises a microphone. Risberg at ¶122 (“[t]he CED 10, 610 may include one or more audio sampling components (e.g. microphones, speakers with dual I/O functionality, etc.). The audio sampling component may be used as a form of feedback for assessing the audio performance of the CED 10, 610 in practice”). Risberg further discloses a processor in the CED and memory for storing instructions. Id. at ¶¶105, 137-138. Risberg discloses that the CED may perform the sampling and parameter functions claimed below. Id. at FIG 7b and at ¶135 (“a method 712 for enhancing audio in a consumer electronics device. The method 712 includes integrating a configurable audio enhancement system into a consumer electronics device 714”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”).
receive, via the acoustic sensor, data indicative of an acoustic response of the audio playback device within an environment;
Risberg discloses receiving by way of a microphone data indicative of an acoustic response of the CED within an environment. Risberg at FIG 7b and at ¶135 (“a method 712 for enhancing audio in a consumer electronics device. The method 712 includes integrating a configurable audio enhancement system into a consumer electronics device 714”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”). Optimization system 101 is described as receiving data via a microphone indicative of a response of the device within an environment. Id. at ¶123 stating that the embodiment of FIG7b also operates according to method 201 of FIG 2; ¶77 (“The audio test unit 110, 210 may be configured to accept audio data 105, 205 from a sample CED”); ¶122 (“The CED 10, 610 may include one or more audio sampling components (e.g. microphones, speakers with dual I/O functionality, etc.). The audio sampling component may be used as a form of feedback for assessing the audio performance of the CED 10, 610 in practice.”).
determine a calibration profile for the audio playback device based on the received data, the calibration profile defining at least one audio output characteristic of the audio playback device;
Risberg discloses determining a set of parameters for the CES based on the received data defining an output characteristic of the device. Risberg at ¶15 (“an audio parameter generator for deriving one or more optimal audio parameters from the audio test dataset”; id. at ¶70-71 (“FIG. 2 shows a schematic of an adaptive optimization system 201 configured to tune and/or optimize one or more audio parameters of a consumer electronics device 10, 11, 610” “[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”). This reads a calibration profile.
detect, via a machine learning model, acoustic conditions within the environment that affect the acoustic response of the audio playback device;
Risberg discloses detecting acoustic conditions that affect the acoustic response of the device. Risberg at ¶71 (“[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”); id. at ¶121 (“[b]y using an optimization system 101, 201 in accordance with the present disclosure, to analyze the frequency response, impulse response, etc. of the consumer electronics device 610 an accurate and compensate able calculation of an acoustic signature for the consumer electronics device 610 may be made. Optimal compensating parameters 510 for an associated audio enhancement system 500 can be derived from the acoustic signature”); ¶52 (“[b]y acoustic signature is meant the audible or measurable sound characteristics of a consumer electronic device dictated by its design and/or manufacturing processes, process variations, etc. that influence the sound generated by the consumer electronic device”).
Risberg discloses that this detecting is performed by way of a machine learning model. Id. at ¶70 (“[t]he adaptive optimization system 201 includes an audio testing component 210, a master design record 220, a parameter generation model 260, and a machine learning algorithm 240, configured to train the parameter generation model 260”);
generate, using the machine learning model, a modified calibration profile for the audio playback device, wherein the modified calibration profile is generated to at least partially compensate for the detected acoustic conditions when applied to the audio playback device;
Risberg discloses modifying the calibration profile iteratively using the same method above, thus using the machine learning model. This generates a modified calibration profile. Risberg at ¶71 (“[o]ptionally, the substantially optimal AES parameters 270 may be arrived at iteratively. In aspects, the AES parameters 270 may be directed 275 to the audio testing component 210 for further use during each iteration of the testing procedure”). These parameters compensate for the detected conditions. Id. at ¶56 (“[t]hus an AES in accordance with the present disclosure may be optimized late in the design process, during the development process, in the field, at a retail outlet, and/or during a manufacturing process to compensate for one or more of these, generally negative, influences on the acoustic properties in the fully manufactured device.”)
and apply the modified calibration profile to the audio playback device.
Risberg discloses applying the final calibration profile to the device. Ridberg at ¶15 (“a programming unit to program the optimal audio parameters onto the consumer electronics device”); id. at ¶67 (“The tuned audio parameter dataset may then be uploaded to the consumer electronics device”); ¶69 (“derive a tuned audio parameter set for upload to the consumer electronics device”).
Further as to claim 31:
The audio playback device of claim 30, wherein the program instructions that are executable by the at least one processor such that the audio playback device is configured to detect the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
provide an input to the machine learning model, the input based on the received data indicative of the acoustic response of the audio playback device within an environment; and
determine that the input produced a particular output from the machine learning model, the particular output indicative of the detected acoustic conditions.
Risberg discloses providing an input to the ML model based on the received data indicative of the acoustic response of the CED and determining that the input produced a particular output indicative of the conditions. Risberg at ¶¶¶70-72.
Further as to claim 34:
The audio playback device of claim 30, wherein the program instructions that are executable by the at least one processor such that the audio playback device is configured to detect the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
detect, via the machine learning model, a lack of an impairment within the environment.
Risberg discloses that the parameters are determined based on a difference between the measured data and a reference dataset. Risberg at ¶P67. One of ordinary skill in the art at the time would have understood that if no difference was found, this result would correspond to a lack of an impairment.
Further as to claim 35:
The audio playback device of claim 30, wherein the program instructions that are executable by the at least one processor such that the audio playback device is configured to detect the acoustic conditions within the environment that affect the acoustic response of the audio playback device comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
detect, via the machine learning model, environmental conditions of the environment, wherein the environmental conditions comprise one or more of: indoors/outdoors, size of room, type of room, furnishings of the room and finishes of the room.
Risberg discloses that the environmental conditions may include a stand, which reads furnishings. Risberg at ¶9.
Further as to claim 36:
The audio playback device of claim 30, wherein the acoustic sensor comprises a microphone, and wherein the program instructions that are executable by the at least one processor such that the audio playback device is configured to receive the data indicative of the acoustic response of the audio playback device within the environment comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
record audio output of the audio playback device within the environment via the microphone.
As noted above in the rejection of claim 22, Risberg discloses the sensor is a microphone. Further, Risberg discloses recording the audio output of the CED. Risberg at ¶103 (“record acoustic feedback from the tests”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”).
Further as to claim 37:
The audio playback device of claim 30, wherein the program instructions that are executable by the at least one processor such that the playback device is configured to generate the modified calibration profile for the audio playback device comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
correct the calibration profile to at least partially offset the detected acoustic conditions.
Risberg discloses that generating the modified profile includes correcting the profile to offset the conditions as noted above in the rejection of claim 22.
As to claim 38:
A non-transitory computer-readable medium comprising program instructions that are executable by at least one processor such that an audio playback device is configured to:
Risberg discloses a calibrating an audio playback device. Risberg at ¶3, specifying a consumer electronic device (CED). Risberg further discloses a processor in the CED and memory for storing instructions. Id. at ¶¶105, 137-138. Risberg discloses that the CED may perform the sampling and parameter functions claimed below. Id. at FIG 7b and at ¶135 (“a method 712 for enhancing audio in a consumer electronics device. The method 712 includes integrating a configurable audio enhancement system into a consumer electronics device 714”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”).
receive, via an acoustic sensor, data indicative of an acoustic response of the audio playback device within an environment;
Risberg discloses receiving by way of a microphone data indicative of an acoustic response of the CED within an environment. Risberg at FIG 7b and at ¶135 (“a method 712 for enhancing audio in a consumer electronics device. The method 712 includes integrating a configurable audio enhancement system into a consumer electronics device 714”); id. at ¶136 (“[t]he optimization system 101 may run a diagnostic test on the consumer electronics device 10, 610 and record audio output from the device 10, 610 obtained during the diagnostic test”). Optimization system 101 is described as receiving data via a microphone indicative of a response of the device within an environment. Id. at ¶123 stating that the embodiment of FIG7b also operates according to method 201 of FIG 2; ¶77 (“The audio test unit 110, 210 may be configured to accept audio data 105, 205 from a sample CED”); ¶122 (“The CED 10, 610 may include one or more audio sampling components (e.g. microphones, speakers with dual I/O functionality, etc.). The audio sampling component may be used as a form of feedback for assessing the audio performance of the CED 10, 610 in practice.”).
determine a calibration profile for the audio playback device based on the received data, the calibration profile defining at least one audio output characteristic of the audio playback device;
Risberg discloses determining a set of parameters for the CES based on the received data defining an output characteristic of the device. Risberg at ¶15 (“an audio parameter generator for deriving one or more optimal audio parameters from the audio test dataset”; id. at ¶70-71 (“FIG. 2 shows a schematic of an adaptive optimization system 201 configured to tune and/or optimize one or more audio parameters of a consumer electronics device 10, 11, 610” “[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”). This reads a calibration profile.
detect, via a machine learning model, acoustic conditions within the environment that affect the acoustic response of the audio playback device;
Risberg discloses detecting acoustic conditions that affect the acoustic response of the device. Risberg at ¶71 (“[t]he parameter generation model 260 may be configured to accept relative data 235 as well as influence 255 from the machine learning algorithm 240 to produce the AES parameters 270”); id. at ¶121 (“[b]y using an optimization system 101, 201 in accordance with the present disclosure, to analyze the frequency response, impulse response, etc. of the consumer electronics device 610 an accurate and compensate able calculation of an acoustic signature for the consumer electronics device 610 may be made. Optimal compensating parameters 510 for an associated audio enhancement system 500 can be derived from the acoustic signature”); ¶52 (“[b]y acoustic signature is meant the audible or measurable sound characteristics of a consumer electronic device dictated by its design and/or manufacturing processes, process variations, etc. that influence the sound generated by the consumer electronic device”).
Risberg discloses that this detecting is performed by way of a machine learning model. Id. at ¶70 (“[t]he adaptive optimization system 201 includes an audio testing component 210, a master design record 220, a parameter generation model 260, and a machine learning algorithm 240, configured to train the parameter generation model 260”);
generate, using the machine learning model, a modified calibration profile for the audio playback device, wherein the modified calibration profile is generated to at least partially compensate for the detected acoustic conditions when applied to the audio playback device;
Risberg discloses modifying the calibration profile iteratively using the same method above, thus using the machine learning model. This generates a modified calibration profile. Risberg at ¶71 (“[o]ptionally, the substantially optimal AES parameters 270 may be arrived at iteratively. In aspects, the AES parameters 270 may be directed 275 to the audio testing component 210 for further use during each iteration of the testing procedure”). These parameters compensate for the detected conditions. Id. at ¶56 (“[t]hus an AES in accordance with the present disclosure may be optimized late in the design process, during the development process, in the field, at a retail outlet, and/or during a manufacturing process to compensate for one or more of these, generally negative, influences on the acoustic properties in the fully manufactured device.”)
and apply the modified calibration profile to the audio playback device.
Risberg discloses applying the final calibration profile to the device. Ridberg at ¶15 (“a programming unit to program the optimal audio parameters onto the consumer electronics device”); id. at ¶67 (“The tuned audio parameter dataset may then be uploaded to the consumer electronics device”); ¶69 (“derive a tuned audio parameter set for upload to the consumer electronics device”).
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 24 is rejected under 35 U.S.C. 103 as being unpatentable over Risberg as applied to claim 23 above, and further in view of U.S. Pat. 6,601,049 B1 to Cooper (“Cooper”).
As to claim 24:
The method of claim 23, wherein the machine learning model comprises a neural network including an output layer comprising neurons that correspond to respective acoustic conditions, and wherein determining that the input produced the particular output from the machine learning model comprises:
determining that the input caused one or more neurons of the neural network to fire such that the neural network indicates that the detected acoustic conditions are present.
Risberg discloses claim 23 from which claim 24 depends, but does not disclose that the ML model comprises a neural network in the manner claimed.
Cooper discloses a machine learning model including a neural network. Cooper at Abstract and 1:22-48. Cooper discloses that the neural network includes an output layer comprising neurons corresponding to conditions as well as determining that an input caused a neuron to fire such that it indicates a particular set of conditions implied by the dataset. Id. at 3:22-54 and 12:60-13:56.
Therefore it would have been obvious to one of ordinary skill in the art at the time of Patent Owner’s filing to modify the ML system of Risberg to use such a neural network in accordance with Cooper. Cooper discloses that such a neural network was a known subset of ML algorithms for acoustic processing and provides rapid processing. Cooper at 3:23-27. Further, one of ordinary skill in the art would have understood such a combination of teachings to merely be an example of applying a known technique to improve similar devices/methods in the same way. MPEP §2143 I. C., citing KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As to claim 32:
The audio playback device of claim 31, wherein the machine learning model comprises a neural network including an output layer comprising neurons that correspond to respective acoustic conditions, and wherein the program instructions that are executable by the at least one processor such that the audio playback device is configured to determine that the input produced the particular output from the machine learning model comprise program instructions that are executable by the at least one processor such that the audio playback device is configured to:
determine that the input caused one or more neurons of the neural network to fire such that the neural network indicates that the detected acoustic conditions are present.
Risberg discloses claim 23 from which claim 24 depends, but does not disclose that the ML model comprises a neural network in the manner claimed.
Cooper discloses a machine learning model including a neural network. Cooper at Abstract and 1:22-48. Cooper discloses that the neural network includes an output layer comprising neurons corresponding to conditions as well as determining that an input caused a neuron to fire such that it indicates a particular set of conditions implied by the dataset. Id. at 3:22-54 and 12:60-13:56.
Therefore it would have been obvious to one of ordinary skill in the art at the time of Patent Owner’s filing to modify the ML system of Risberg to use such a neural network in accordance with Cooper. Cooper discloses that such a neural network was a known subset of ML algorithms for acoustic processing and provides rapid processing. Cooper at 3:23-27. Further, one of ordinary skill in the art would have understood such a combination of teachings to merely be an example of applying a known technique to improve similar devices/methods in the same way. MPEP §2143 I. C., citing KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
Allowable Subject Matter
Claims 1-21 comprise allowable subject matter and would be allowed if a proper reissue declaration were filed and a correctable error be presented.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 1 recites a playback device comprising a microphone, a communications interface, at least one processor, and at least one non-transitory computer-readable medium comprising program instructions that are executable by the at least one processor such that the playback device is configured to perform functions comprising:
receiving data indicating a response of a listening environment to audio output of the playback device as captured by the microphone; determining an input vector for a neural network, wherein determining the input vector comprises projecting a response vector that represents the response onto a principle component matrix representing variance caused by one or more environmental conditions; providing the determined input vector to the neural network, the neural network including an output layer comprising neurons that correspond to respective impairments; determining that the input vector caused one or more neurons of the neural network to fire such that the neural network indicates that a particular impairment is present; and sending, via the communications interface to a network device, data indicating that the particular impairment is present.
The prior art of record does not disclose the functions claimed as to specifically determining a particular impairment via the neural network and sending data indicating the particular impairment. Various references teach determining a measured output that varies from a reference (see, inter alia, Risberg cited above as well as U.S. Pat. PGPUB 2015/0223004A1 to Deprez et al.), however these do not teach a determination of a specific impairment and sending of information detailing the specific impairment.
Claims 2-8 are indicated as containing allowable subject matter based on a dependence on claim 1. Independent claims 9 and 17 are indicated as containing allowable subject matter for the same reasons set forth as to claim 1 above. Claims 10-16 and 18-21 are indicated as containing allowable subject matter based on a dependence on claims 9 and 17, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Charles Craver whose telephone number is (571) 272-7849. The Examiner can normally be reached on Monday - Friday 8:30-5:30 PT Pacific Time.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Andrew J. Fischer can be reached on 571-272-6779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Signed,
/CHARLES R CRAVER/Reexamination Specialist, Art Unit 3992
Conferees:
/ROBERT J HANCE/Reexamination Specialist, Art Unit 3992 /M.F/Supervisory Patent Examiner, Art Unit 3992
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