DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-8 are presented for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings are objected to because (a) in reference character S10, “labeled to body” should be “labeled with body”; and (b) in Figure 7, text is written on a shaded background, see 37 CFR § 1.84(p)(3). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The abstract of the disclosure is objected to because “labeled to body” should be “labeled with body”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 1 is objected to because of the following informalities: the meaning of “HRTF” in should be spelled out in the preamble rather than in the first substantive limitation, and “[t]raining” should not be capitalized. Claims 2-8 are objected to for dependency on claim 1.
Claim 3 is objected to because of the following informalities: “in a multiple way” is awkward phrasing.
Claims 5-7 are objected to because of the following informalities: the parentheses should be deleted and replaced with a comma after each final equation appearing in each claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites “the body information” without more. It is unclear whether this refers to the body information of the learning object or the body information of the target user of claim 1.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bharitkar (WO 2020167309) (“Bharitkar”).
Regarding claim 1, Bharitkar discloses “[a] method for generating a personalized HRTF, the method comprising:
[t]raining, by processor, a neural network model using multi-angle Head-Related Transfer Functions (HRTF) labeled [with] body information of a learning object by means of a processor (compressed values at output of deepest encoder layer are used for training an artificial neural network to perform a function approximation task where the input to the artificial neural network is an angle [body information of the learning object] and the training data at the output of the artificial neural network is a latent representation of the deepest layer encoder output for the corresponding HRTF – Bharitkar, paragraph 27; neural network reconstructs HRTFs for arbitrary angles, for example, every 1 degree [i.e., the HRTF is constructed at multiple angles] – id. at paragraph 12; see also Fig. 6 (showing processor 602)); and
obtaining, by processor, multi-angle HRTFs at a time by inputting body information of a target user into the trained neural network model by means of the processor (direction parameter that may be azimuth and elevation angles [body information of a target user] describing a directionality of sound included in an audio signal are received, and the direction parameter is provided as input to the trained neural network to generate a compressed representation of a set of HRTFs [multi-angle HRTFs] – Bharitkar, paragraphs 45-47 and Fig. 5).”
Regarding claim 2, Bharitkar discloses that “the training of a neural network model includes applying supervised learning to the neural network model by setting the body information of the learning object as input data of the neural network model and setting the multi-angle HRTFs as output data of the neural network model (compressed values at output of deepest encoder layer are used for training an artificial neural network to perform a function approximation task where the input to the artificial neural network is an angle [body information of the learning object] and the training data at the output of the artificial neural network is a latent representation of the deepest layer encoder output for the corresponding HRTF – Bharitkar, paragraph 27; neural network reconstructs HRTFs for arbitrary angles, for example, every 1 degree [i.e., the HRTF is constructed at multiple angles] – id. at paragraph 12).”
Regarding claim 4, Bharitkar discloses that “the training of a neural network model includes training the neural network model such that the neural network model outputs the multi-angle HRTFs with reference to HRTFs of adjacent angles (compressed values at output of deepest encoder layer are used for training an artificial neural network to perform a function approximation task where the input to the artificial neural network is an angle [body information of the learning object] and the training data at the output of the artificial neural network is a latent representation of the deepest layer encoder output for the corresponding HRTF – Bharitkar, paragraph 27; neural network reconstructs HRTFs for arbitrary angles, for example, every 1 degree [i.e., adjacent angles] – id. at paragraph 12).”
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 CFR 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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Bharitkar in view of Li et al. (US 20200184339) (“Li”).
Regarding claim 3, the rejection of claim 1 is incorporated. Bharitkar further discloses that “the neural network model includes:
at least one fully connected layer [utilizing] … the body information (artificial neural network may be a fully-connected neural network – Bharitkar, paragraph 28; compressed values at output of deepest encoder layer are used for training an artificial neural network to perform a function approximation task where the input to the artificial neural network is an angle [body information] and the training data at the output of the artificial neural network is a latent representation of the deepest layer encoder output for the corresponding HRTF –id. at paragraph 27); and
a … layer … outputting preset angle-specific HRTFs (compressed values at output of deepest encoder layer are used for training an artificial neural network to perform a function approximation task where the input to the artificial neural network is an angle and the training data at the output of the artificial neural network is a latent representation of the deepest layer encoder output for the corresponding HRTF [i.e., the layers of the network output HRTFs] –Bharitkar, paragraph 27; decoder reconstructs the HRTFs for arbitrary angles, for example, every 1 degree [so the HRTF outputs are preset to be specific to each possible integer angle] – id. at paragraph 12).”
Bharitkar appears not to disclose explicitly the further limitations of the claim. However, Li discloses that “the neural network model includes:
at least one … layer extracting features from the … information (topic embedding and word embedding are concatenated and fed into a CNN [comprising layers] for word and topic feature extraction – Li, paragraph 49); and
a bidirectional Long Short Term Memory (LSTM) layer receiving the extracted features in a multiple way and outputting [data] (a bidirectional LSTM is employed for sequential [in a multiple way] processing [including receipt] of the extracted features to obtain [output] a sentence representation [data] – Li, paragraph 49).”
Li and the instant application both relate to bidirectional LSTMs and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bharitkar to employ a feature extraction layer that is passed to a bidirectional LSTM, as disclosed by Li, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the data to be sequentially processed. See Li, paragraph 49.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Bharitkar in view of Refaat et al. (US 20220036186) (“Refaat”).
Regarding claim 5, the rejection of claim 1 is incorporated. Bharitkar further discloses that “the neural network model is trained such that a loss function … is minimized, … (where
y
m
is a measured HRTF for an m-th angle and
y
^
m
is a predicted HRTF for the m-th angle) (Bharitkar paragraphs 23-24 disclose the optimization of a cost [loss] function that includes a term corresponding to the sum of squared differences between the encoder input and the decoder output; paragraph 12 discloses that the decoder reconstructs HRTFs for arbitrary angles [i.e., the output of the decoder is the predicted HRTF for the mth angle, meaning that the input of the encoder whose output is the basis of the reconstruction is an original HRTF for the mth angle]).”
Bharitkar appears not to disclose explicitly the further limitations of the claim. However, Refaat discloses that “the neural network model is trained such that a loss function defined as the following [Equation 1] is minimized,
[Equation 1]
L
1
y
,
y
^
=
1
M
∑
m
=
0
M
-
1
{
y
m
-
y
^
m
}
2
(system trains the observation prediction neural network to minimize a mean squared error loss function between predicted observations and corresponding observations – Refaat, paragraph 71 [note that the claimed equation is the mean squared error]) ….”
Refaat and the instant application both relate to loss functions for neural network training and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bharitkar to minimize a mean squared error loss function, as disclosed by Refaat, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the accuracy of the predictions by allowing the network to learn from incorrect guesses. See Refaat, paragraph 71.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Bharitkar in view of McDonald, Mean Absolute Log Error (MALE): A Better “Relative” Performance Metric, Medium, Jan. 18, 2023, https://medium.com/data-science/mean-absolute-log-error-male-a-better-relative-performance-metric-a8fd17bc5f75 (“McDonald”).
Regarding claim 6, the rejection of claim 1 is incorporated. Bharitkar further discloses that “the neural network model is trained such that a loss function … is minimized, … (where
Y
m
is a measured HRTF at a frequency domain for an m-th angle and
Y
^
m
is a predicted HRTF at the frequency domain for the m-th angle) (Bharitkar paragraphs 23-24 disclose the optimization of a cost [loss] function that includes a term corresponding to the sum of squared differences between the encoder input and the decoder output; paragraph 12 discloses that the decoder reconstructs HRTFs for arbitrary angles [i.e., the output of the decoder is the predicted HRTF for the mth angle, meaning that the input of the encoder whose output is the basis of the reconstruction is an original HRTF for the mth angle]; paragraph 34 discloses that the HRTF is computed in the frequency domain).”
Bharitkar appears not to disclose explicitly the further limitations of the claim. However, McDonald discloses that “a loss function defined as the following [Equation 2] is minimized,
[Equation 2]
L
2
Y
,
Y
^
=
1
M
∑
m
=
0
M
-
1
|
l
o
g
10
Y
m
-
l
o
g
10
Y
^
m
|
…. (McDonald, section entitled “… lead to better metrics” discloses the mean absolute log error, defined as
1
N
∑
t
=
1
T
|
l
o
g
(
f
t
y
t
)
|
, where f and y are forecast and observed values, respectively [corresponding to
Y
m
and
Y
^
m
, respectively; note that this is mathematically equivalent to the claimed equation because of the identity log (x/y) = log x – log y]).”
McDonald and the instant application both relate to error functions and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bharitkar to use a mean absolute log error function as the loss function, as disclosed by McDonald, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would make the loss measure fairer by punishing underestimates and overestimates equally harshly. See McDonald, second bullet point under “Better error measures …”.
Regarding claim 7, the rejection of claim 1 is incorporated. Bharitkar further discloses that “the neural network model is trained such that a … loss function[] … is minimized, … (wherein …
Y
m
is a measured HRTF at a frequency domain for an m-th angle and
Y
^
m
is a predicted HRTF at the frequency domain for the m-th angle) (Bharitkar paragraphs 23-24 disclose the optimization of a cost [loss] function that includes a term corresponding to the sum of squared differences between the encoder input and the decoder output; paragraph 12 discloses that the decoder reconstructs HRTFs for arbitrary angles [i.e., the output of the decoder is the predicted HRTF for the mth angle, meaning that the input of the encoder whose output is the basis of the reconstruction is an original HRTF for the mth angle]; paragraph 34 discloses that the HRTF is computed in the frequency domain).”
Bharitkar appears not to disclose explicitly the further limitations of the claim. However, McDonald discloses that “a linear combination of first and second loss functions defined as the following [Equation 1] and [Equation 2], respectively, is minimized,
[Equation 1]
L
1
y
,
y
^
=
1
M
∑
m
=
0
M
-
1
{
y
m
-
y
^
m
}
2
[Equation 2]
L
2
Y
,
Y
^
=
1
M
∑
m
=
0
M
-
1
|
l
o
g
10
Y
m
-
l
o
g
10
Y
^
m
|
(McDonald, section entitled “… lead to better metrics” discloses the mean absolute log error, defined as
1
N
∑
t
=
1
T
|
l
o
g
(
f
t
y
t
)
|
, where f and y are forecast and observed values, respectively [corresponding to
Y
m
and
Y
^
m
, respectively; note that this is mathematically equivalent to the claimed equation because of the identity log (x/y) = log x – log y; note also that this is a linear combination of the two claimed loss functions under the special case where the coefficient corresponding to the first claimed loss function is zero]) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bharitkar to use a mean absolute log error function as the loss function, as disclosed by McDonald, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would make the loss measure fairer by punishing underestimates and overestimates equally harshly. See McDonald, second bullet point under “Better error measures …”.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Bharitkar in view of Milne et al. (US 20200107148) (“Milne”).
Regarding claim 8, the rejection of claim 1 is incorporated. Bharitkar further discloses a “neural network model” and “multi-angle HRTFs”, as shown in the rejection of claim 1.
Bharitkar appears not to disclose explicitly the further limitations of the claim. However, Milne discloses that “the obtaining of … HRTFs at a time includes obtaining … HRTFs at a time by further inputting an ear image of the target user into the … model (a person or an assistant takes one or more 3D images of the person’s ears and/or takes a panoramic set of images of each ear; the photo or photos are processed onboard the device taking the photos by inputting the photographs to a module [model] that generates or otherwise returns HRTFs – Milne, paragraph 43).”
Milne and the instant application both relate to HRTF generation and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bharitkar to generate the HRTFs by taking photographs of the subject’s ears and inputting them into the model, as disclosed by Milne, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would save on equipment costs that would otherwise be incurred using other types of data such as sound data. See Milne, paragraph 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at 571-272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
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