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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
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.
Claims 1-13 are 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.
Specifically, independent Claims 1 and 8 recite: (Claim 1, line 10; Claim 8, line 12) “constructing a set of test objects based on one or more three-dimensional scans associated with the clustered anthropometric features.”
The language shown in bold emphasis renders the limitation indefinite, since it is not clear what was scanned, and said scan is “associated” with “clustered anthropometric features”, with no explanation for how the numerical results of a clustering algorithm is “associated” with a scan. If the intention is that a scan is used to collect anthropometric features, it is not made apparent in claim limitation. Examiner notes that specification references “three-dimensional scans” with “identifying a plurality of anthropometric landmarks” in at least [0008], or [0026], but claim limitation does not recite sufficient detail regarding how three-dimensional scan is “associated” with the “clustered anthropometric features”. Applying broadest reasonable interpretation (BRI) and plain meaning, the “clustered anthropometric features” is interpreted to mean a data set resulting from mathematical analysis (“regression”, “correlation”, and “clustering”) of anthropometric data, i.e. (“features”), where source or method for obtaining anthropometric data is no specified in Claim 1 limitations.
Claims 2 and 9 recite the limitation “obtaining, from each human subject, a data set” in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim 2 depends to Claim 1 limitations, where there is no instance of the term “human subject”. Examiner understands the use of the term “anthropometric” is inherently directed to human-centered actions. However, based on limitations as discussed above for Claim 1, human subjects are not mentioned for obtaining anthropometric “data set”; see Claim 1 above, reciting “determining…and an amount of acoustical leakage associated with a first set of headphones”, wherein an element of “human subject” is not recited, and acoustical leakage data may be obtained, for example, from a database.
Claims 5 and 12 recite: “determining, for a plurality of human subjects, a first set of acoustical-related measurements associated with a second set of headphones, wherein the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage;”. Examiner notes that the term “a first set of acoustical -related measurements” is initially recited in Claim 1 and followed with “the first set of acoustical-related measurements are associated with a plurality of human subjects”. As used in Claim 5, it is not clear as to whether the term refers to the same “first set” or is a different “first set”, and as such renders the claim limitation indefinite. For examination purposes, the limitation in Claim5 is interpreted to mean an additional “first set” associated with measurements using a second set of headphones.
Claims 3-4, 6-7, 10-11, and 13 are rejected based on dependency to Claims 1-2, 5, 8-9, and 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.
Claims 1-20* rejected under 35 U.S.C. § 103(a) as being unpatentable over STIRNEMANN (US 20070217639 A1) in view of GROON (Groon, et al., “Air-Leak Effects on Ear-Canal Acoustic Absorbance”, EAR & HEARING, VOL. 36, NO. 1, 155–163, Ear and Hearing • Copyright © 2015 Wolters Kluwer Health, Inc), and further in view of ZHANG (Zhang, et al., “3D Statistical Head Modeling for Face/head-Related Product Design: A State-of-the-Art Review”, Computer-Aided Design 159 (2023) 103483)
*Examiner notes that independent Claim 14 rejection relies only on references STIRNEMANN and GROON, but is included here for conciseness, based on references used in claims dependent to Claim 14
With regard to Claims 1 and 8, STIRNEMANN teaches:
(Claim 1) A method ; (Claim 8) A system ;
comprising: a processor; and a non-transitory computer-readable medium including instructions that are executable by the processor, (STIRNEMANN, Abstract: “real ear acoustic coupling quantity representative of the acoustic coupling of a hearing instrument to the user's ear or an anatomical transfer quantity”; and generally well-known computational system components, [0018]: “signal processing unit with a data memory”; Claim 20.)
determining, by a processor using a regression model, (STIRNEMANN,FIG6, with caption and [0063]: “coefficients are obtained (row by row) by a stepwise regression process”), a correlation between a plurality of anthropometric features and an amount of acoustical leakage associated with a first set of headphones; (STIRNEMANN, FIG2 with [0031]: “ear canal transfer function depends on the distance to the ear drum…, the vent path is determined by the vent length and possible concha effects (i.e., “anthropometric features”)…FIG. 2 shows the measured correlation, for a variety of behind-the-ear hearing instruments”; Applying BRI, Examiner interprets “headphones” as analogous to reference, supported by reference [0043]: “term "hearing instrument" or "hearing device", as understood in this text,…as provided by headphones…hearing instrument may also be an ear protector”)
selecting, for clustering, a subset of anthropometric features from the plurality of anthropometric features (STIRNEMANN, FIG.7 with [0065]: “model is based on the clustering and classification approach. When establishing the model (analysis), a number N of experimentally obtained RECD curves are clustered, i.e. classes of the real ear acoustic coupling quantity or anatomical transfer quantity are formed.”)
clustering, by the processor, the selected anthropometric features using a clustering algorithm; (STIRNEMANN, [0065]: “model is based on the clustering and classification approach…well-established procedure of k-means clustering.”; Examiner notes “k-means” is considered as a clustering algorithm”)
STIRNEMANN does not explicitly teach:
selecting, for clustering, based on the determined correlation, a subset of anthropometric features from the plurality of anthropometric features having a highest correlation to the amount of acoustical leakage;
GROON teaches:
selecting, for clustering, based on the determined correlation, a subset of anthropometric features from the plurality of anthropometric features having a highest correlation to the amount of acoustical leakage; (GROON is in same technical field, see Pg155,COL1: “Four acoustic metrics were used for predicting the presence of air leaks and for quantifying these leaks”; teaches correlation methods, see FIG. 7 with “shows the dependence of Δabsorbance on the four air-leak predictor variables”; Examiner interprets “acoustical leakage” to be taught by reference change in absorbance based on sensitivity of absorbance to presence of leaks, as would be understood by of one of ordinary skill in the art, as taught in reference, see Pg160COL2,¶4.; and see Table 1 with Pg160, COL1,last¶-COL2: “correlations between Δabsorbance and variables Alow and φlow…suggest that these two variables may be the best for predicting air leaks in ear-canal measurements” (i.e., “highest correlation”); teaches clustering of predictor variables, see Fig.7, with Pg161,COL2: “All of the predictor variables had the strongest correlations to Δabsorbance in the lowest frequency band ”; and teaches correlation with ear canal measurements, see Fig. 9 with Pg162COL2-Pg163COL1: “Correlations were observed between the WAI-derived measurements and the tympanometric measurements (i.e., “anthromopometric features”)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify STIRNEMANN to include selecting, for clustering, based on the determined correlation, a subset of anthropometric features from the plurality of anthropometric features having a highest correlation to the amount of acoustical leakage as taught by GROON because this would be an efficient and logical next step in a process based on a regression model where correlation coefficients are considered. One of ordinary skill would see an obvious connection between the statistical method of STIRNEMANN, which uses a k-means clustering algorithm, as a more straightforward method of identifying clusters without pre-determination of the number of clusters, since clusters are determined using pair-wise similarity or dissimilarity.
STIRNEMANN, as modified by GROON and taught above, does not explicitly teach:
constructing a set of test objects based on one or more three-dimensional scans associated with the anthropometric features.
ZHANG teaches
constructing a set of test objects based on one or more three-dimensional scans associated with the anthropometric features.(ZHANG is in same technical field, Abstract: “field of face/head-related product design, 3D statistical models (3DSMs) of human heads and faces are widely used to model geometric variations, generate representative models and reconstruct head geometries… state-of-the-art review of 3DSMs of human heads and faces for facewear/headwear design…3D head scan datasets”; and see Pg2,COL1: “capturing accurate head anthropometric data is of
utmost importance to meet ergonomic design requirements for head-related products” and COL2: “3D scans can provide not only simple traditional 1D anthropometric data, but also complex 3D anthropometric information,”
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and taught above, to include constructing a set of test objects based on one or more three-dimensional scans associated with the anthropometric features, as taught by ZHENG because this step would provide the ability to perform accurate predictive tests of headphone performance without the necessity of human testing subjects, and allow for known variability in test subject anatomy as related to acoustic leak occurrence.
With regard to Claim 2 and 9 STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of Claims 1 and 8 as discussed above.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not teach explicitly:
obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof.
GROON further teaches:
obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof. (GROON teaches at least data set including amount of acoustical leakage, FIG7,Pg161 “19 subjects”; and (GROON, Pg155,COL1: “Four acoustic metrics were used for predicting the presence of air leaks and for quantifying these leaks”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof, as further taught by GROON because this method provides a more generalized approach to understanding a range of potential relationships between anthropometric features and acoustic leakage. One of ordinary skill would see this more inclusive data set as an obvious way to arrive at a solution of producing a test model that would be more likely to determine leakage in a headphone testing method.
With regard to Claims 3 and 10, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of Claim 2 and 9, as discussed above.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans; and
identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks.
ZHANG further teaches:
identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans; (ZHANG, Abstract: “facial mesh alignment, facial landmark detection and facial shape correspondence”; and see 1.1.2 “Landmark-based methods” and 3.2 “Landmark detection”; teaches multiple subjects, see 2.2.1: “Head datasets”; and 3D scanning, see Table 2.)
identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks. (ZHANG teaches correspondence of landmarks with features, see Section 3, and FIG.4)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks, as further taught by ZHANG because the inclusion of multiple verified landmarks associated with various reliable anthropometric features would provide an improvement in reliability for the construction of models for testing headphones. One of ordinary skill would see the advantage of relying on an evidenced-based model construction method, as taught by ZHANG to bring an improvement when combined with the disclosure as STIRNEMANN as modified by GROON.
With regard to Claims 4 and 11, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claims 1 and 8 as discussed above.
STIRNEMANN further teaches:
identifying the clustered anthropometric features based on one or more clustering metrics. (STIRNEMANN, as above, FIG.7 with [0065]: “model is based on the clustering and classification approach. When establishing the model (analysis), a number N of experimentally obtained RECD curves are clustered, i.e. classes of the real ear acoustic coupling quantity or anatomical transfer quantity are formed.”; and [0065])
With regard to Claim 5, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claim 1, as discussed above.
STIRNEMANN teaches:
measurements associated with a second set of headphones (STIRNEMANN teaches multiple measured devices, see [0062]: “possible to use measurements obtained with various different hearing instruments”)
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
determining, for a plurality of human subjects, a first set of acoustical-related measurements,
the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and
determining, for the set of test objects, a second set of acoustical-related measurements associated with the second set of headphones, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage.
GROON teaches:
determining, for a plurality of human subjects, a first set of acoustical-related measurements, (GROON, as discussed in Claim 2, FIG7,Pg161 and Pg155,COL1)
the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; (GROON, as above, Pg159,FIG4, where variance is interpreted as above, related to standard deviation.)
determining, for the set of test objects, a second set of acoustical-related measurements, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage.(GROON, as above, Pg158,FIG3, with COL1: “Measurements in a brass tube (i.e., “test object”)”; and teaches multiple test objects, Pg156: “five tubes of
varying length”; and averaging, see as above, Pg155,COL1, and FIG5))
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include the steps of determining, for a plurality of human subjects, a first set of acoustical-related measurements, the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and determining, for the set of test objects, a second set of acoustical-related measurements associated with the second set of headphones, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage, as taught by GROON because additional data from multiple subjects and statistical analysis of acoustic leak data would be seen as a way to improve the disclosure of STIRNEMANN as modified and taught above, by allowing for a more reliable and robust model for comparative analysis to construct test models.
With regard to Claims 6, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claim 5.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements.
GROON teaches, as above,
generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements. (GROON, FIG7,Pg161, and FIG8,Pg162 caption: “linear regression fits to the data of Fig. 7 for frequency bands of 0.1–0.2 and 0.2–0.5 kHz (i.e., “acoustic-related measurements”)” )
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements, as further taught by GROON because this analysis method is a standard and reliable practice when seeking relationships between variables which may have multiple dependencies. Using multiple data sets in a regression model would be seen as an effective way to improve accuracy and reliability when combined with the invention of STIRNEMANN as modified and taught above.
With regard to Claim 7 STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claim 6, as discussed above
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance associated with a general population of humans. GROON teaches:
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance (GROON, Pg159,FIG4, caption “Quantifying air leaks by diameter in a brass cavity using the four predictor variables…diamonds represent the mean results”, and COL1: “error bars represent ±1 SD”, where SD is interpreted to suggest variance, as above; GROON teaches consideration of slope, Pg162,FIG8.))
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include predicting, based on a slope of the regression line, an average acoustical leakage and an average variance, as further taught by GROON since this evaluation method is understood as a reliable way to determine relationships between two measured data sets.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
averages associated with a general population of humans
ZHANG teaches
averages associated with a general population of humans (ZHANG, Section 2.2 “Scan datasets”; Examiner interprets “general population of humans” to mean generally a reasonable and rationale approximation using large and diverse datasets, as is taught by ZHANG, reciting data from thousands of measured human subjects.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include averages associated with a general population of humans because it would be understood that a more expansive and inclusive model would allow for better representation over a range of anthropometric features, and result in a more accurate and reliable set of test models for construction.
With regard to Claim 12, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of Claim 8.
STIRNEMANN teaches:
measurements associated with a second set of headphones (STIRNEMANN teaches multiple measured devices, see [0062]: “possible to use measurements obtained with various different hearing instruments”)
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
determining, for a plurality of human subjects, a first set of acoustical-related measurements wherein the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage;
determining, for the set of test objects, a second set of acoustical-related measurements, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and
generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements.
GROON teaches, as above,
determining, for a plurality of human subjects, a first set of acoustical-related measurements wherein the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; (See parallel limitation, Claim 5, FIG7,Pg161 and Pg155,COL1, and Pg159,FIG4, where variance is interpreted as above, related to standard deviation)
determining, for the set of test objects, a second set of acoustical-related measurements, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; .(GROON, as above, Pg158,FIG3, with COL1: “Measurements in a brass tube (i.e., “test object”)”; and teaches multiple test objects, Pg156: “five tubes of varying length”; and averaging, variance, see as above, Pg155,COL1, and FIG5, with variance interpreted as analogous to standard deviation.)
generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements. (GROON, FIG7,Pg161, and FIG8,Pg162 caption: “linear regression fits to the data of Fig. 7 for frequency bands of 0.1–0.2 and 0.2–0.5 kHz (i.e., “acoustic-related measurements”)” )
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include the steps of determining, for a plurality of human subjects, a first set of acoustical-related measurements wherein the first set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; determining, for the set of test objects, a second set of acoustical-related measurements, wherein the second set of acoustical-related measurements includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and generating a regression line based on a correlation between the first set of acoustical-related measurements and the second set of acoustical-related measurements, as further taught by GROON because these steps are well known as reliable data analysis methods for investigating and determining relationships between variables with multiple relationships.
With regard to Claim 13 STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claim 12, as discussed above
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance associated with a general population of humans. GROON teaches:
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance (GROON, Pg159,FIG4, caption “Quantifying air leaks by diameter in a brass cavity using the four predictor variables…diamonds represent the mean results”, and COL1: “error bars represent ±1 SD”, where SD is interpreted to suggest variance, as above; GROON teaches consideration of slope, Pg162,FIG8.))
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include predicting, based on a slope of the regression line, an average acoustical leakage and an average variance, as further taught by GROON since this statistical analysis method is a reliable way to confirm relationships between two variables in a quantitative manner. One of ordinary skill would see this as a logical combination with the method and system disclosed by STIRNEMANN, as modified above to result in an improved and more reliable method for determining the best anthropometric features to be included in a test model construction.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
averages associated with a general population of humans
ZHANG teaches
averages associated with a general population of humans (ZHANG, Section 2.2 “Scan datasets”; Examiner interprets “general population of humans” to mean generally a reasonable and rationale approximation using large and diverse datasets, as is taught by ZHANG, reciting data from thousands of measured human subjects.)
As stated above, it would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include averages associated with a general population of humans because it would be understood that a more expansive and inclusive model would allow for better representation over a range of anthropometric features, and result in a more accurate and reliable set of test models for construction.
With regard to Claim 14, STIRNEMANN teaches:
A method; associated with a first set of headphones (STIRNEMANN as above, FIG2 with [0031], and [0043])
STIRNEMANN does not explicitly teach:
generating a regression line based on a correlation between a first set of acoustical-related measurements and a second set of acoustical-related measurements,
wherein the first set of acoustical-related measurements are associated with a plurality of human subjects and includes an average acoustical leakage and an average variance associated with the average acoustical leakage, and
wherein the second set of acoustical-related measurements is associated with a set of test objects and includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance associated with a general population of humans,
wherein each of the prediction of the average acoustical leakage and the average variance are associated with a first set of headphones.
GROON further teaches:
generating a regression line based on a correlation between a first set of acoustical-related measurements and a second set of acoustical-related measurements, (GROON, FIG7,Pg161, and FIG8,Pg162 caption: “linear regression fits to the data of Fig. 7 for frequency bands of 0.1–0.2 and 0.2–0.5 kHz (i.e., “acoustic-related measurements”)” )
the first set of acoustical-related measurements are associated with a plurality of human subjects (GROON, FIG7,Pg161 “19 subjects”) and includes an average acoustical leakage (GROON, Pg155,COL1: “Four acoustic metrics were used for predicting the presence of air leaks and for quantifying these leaks: (1) low-frequency admittance phase (averaged over 0.1–0.2 kHz)”), and an average variance associated with the average acoustical leakage, (GROON, FIG5, caption: “shaded region indicates the standard deviation”; Examiner interprets “variance” to be understood by one of ordinary skill as the square of a standard deviation in general and well-known statistical analysis, such GROON analysis including standard deviation would suggest consideration of variance.)
the second set of acoustical-related measurements is associated with a set of test objects (GROON, Pg158,FIG3, with COL1: “Measurements in a brass tube (i.e., “test object”) were made using foam tips without an air leak and foam tips that had been modified to create a calibrated air leak by insertion of the plastic tubing described above.”) and includes an average acoustical leakage and an average variance associated with the average acoustical leakage (GROON, as above, Pg155,COL1, and FIG5)
predicting, based on a slope of the regression line, an average acoustical leakage and an average variance associated with a general population of humans (GROON, Pg159,FIG4, caption “Quantifying air leaks by diameter in a brass cavity using the four predictor variables…diamonds represent the mean results”, and COL1: “error bars represent ±1 SD”, where SD is interpreted to suggest variance, as above; GROON teaches consideration of slope, Pg162,FIG8.)
wherein each of the prediction of the average acoustical leakage and the average variance are associated with a first set of headphones. (GROON teaches sound production equipment, Pg156,COL1: “equipment used in this study was described in detail by Rasetshwane and Neely (2011)…two identical sound sources connected separately…allowed for a pair of measurements to be taken with each probe placement…commercially available tweeters (TW010F1, Audax, France) housed in lightweight steel enclosures”; Examiner notes referenced work by RASETSHWANE, et al., is included below.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN to include generating a regression line based on a correlation between a first set of acoustical-related measurements and a second set of acoustical-related measurements; wherein the first set of acoustical-related measurements are associated with a plurality of human subjects and includes an average acoustical leakage and an average variance associated with the average acoustical leakage, and wherein the second set of acoustical-related measurements is associated with a set of test objects and includes an average acoustical leakage and an average variance associated with the average acoustical leakage; and predicting, based on a slope of the regression line, an average acoustical leakage and an average variance associated with a general population of humans, wherein each of the prediction of the average acoustical leakage and the average variance are associated with a first set of headphones, as taught by GROON, because these steps allow for a reliable method for determining relationship between acoustic leak and anatomic/anthropometric features when analyzing sound producing devices, and as suggested by GROON, provide an improved method for determination of the impact of leaks based on anatomical features.
With regard to Claim 15, STIRNEMANN in view of GROON, teaches the limitations Claim 14, as discussed above
STIRNEMANN teaches as above,
a subset of anthropometric features from a plurality of anthropometric features are clustered by a processor using a clustering algorithm. (STIRNEMANN, FIG.7 with [0065]: “model is based on the clustering and classification approach. When establishing the model (analysis), a number N of experimentally obtained RECD curves are clustered, i.e. classes of the real ear acoustic coupling quantity or anatomical transfer quantity (i.e., “anthropometric features”) are formed.”; and [0065]: “model is based on the clustering and classification approach…well-established procedure of k-means clustering.”; Examiner notes “k-means” is considered as a clustering algorithm”)
STIRNEMANN, as modified by GROON and taught above, does not teach:
constructing the set of test objects based on one or more three-dimensional scans associated with clustered anthropometric features, wherein
ZHANG teaches:
constructing the set of test objects based on one or more three-dimensional scans associated with clustered anthropometric features.(ZHANG, Abstract and Pg2,COL1)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and taught above, to include constructing a set of test objects based on one or more three-dimensional scans associated with the anthropometric features, as taught by ZHENG because this step would provide the ability to perform accurate predictive tests of headphone performance without the necessity of human testing subjects, and allow for known variability in test subject anatomy as related to acoustic leak occurrence.
With regard to Claim 16, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of Claim 15.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
the subset of anthropometric features are selected based on a highest correlation to an amount of acoustical leakage associated with a second set of headphones.
GROON teaches:
the subset of anthropometric features are selected based on a highest correlation to an amount of acoustical leakage associated with a second set of headphones. (GROON, as above, FIG. 7; Examiner interprets “acoustical leakage” as above; and Pg160COL2,¶4.; Table 1 with Pg160, COL1,last¶-COL2; Fig.7, with Pg161,COL2; and Pg162COL2-Pg163COL1: “Correlations were observed between the WAI-derived measurements and the tympanometric measurements (i.e., “anthromopometric features”)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify STIRNEMANN to include selecting, for clustering, based on the determined correlation, a subset of anthropometric features from the plurality of anthropometric features having a highest correlation to the amount of acoustical leakage as taught by GROON because this would be an efficient and logical next step in a process based on a regression model where correlation coefficients are considered. One of ordinary skill would see an obvious connection between the statistical method of STIRNEMANN, which uses a k-means clustering algorithm, as a more straightforward method of identifying clusters without pre-determination of the number of clusters, since clusters are determined using pair-wise similarity or dissimilarity.
With regard to Claim 17, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of Claim 16
STIRNEMANN further teaches:
associated with the second set of headphones
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
the highest correlation is determined by the processor using a regression model based on a determination of a correlation between the subset of anthropometric features and the amount of acoustical leakage
GROON teaches:
the highest correlation is determined by the processor using a regression model based on a determination of a correlation between the subset of anthropometric features and the amount of acoustical leakage (GROON, FIG. 6 FIG8, with Pg160,COL2: “Figure 8 shows simple linear-regression fits to the plots of Δabsorbance”; and see FIG.9 comparison with tympanic values using linear regression model, and Pg160,COL1: “highest correlations for all four predictor variables were seen in the lowest frequency band (0.1–0.2 kHz), where the correlation coefficient was as high as 0.98 for Alow.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify STIRNEMANN to determine the highest correlation based on a determination of a correlation between the subset of anthropometric features and the amount acoustical leakage, as taught by GROON because this would allow for effective analysis to determine the relationship in question, allowing for better planning of which anthropometric features are most impactful in producing acoustic leaks, which would ultimately better inform development of an accurate test model.
With regard to 18, STIRNEMANN, in view of GROON, teaches the limitations of Claim 14, as discussed above.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not teach explicitly:
obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof.
GROON further teaches:
obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof. (GROON teaches at least data set including amount of acoustical leakage, FIG7,Pg161 “19 subjects”; and (GROON, Pg155,COL1: “Four acoustic metrics were used for predicting the presence of air leaks and for quantifying these leaks”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include obtaining, from each human subject of a plurality of human subjects, a data set, wherein the data set includes the amount of acoustical leakage, the one or more three-dimensional scans, or a combination thereof, as further taught by GROON because this method provides a more generalized approach to understanding a range of potential relationships between anthropometric features and acoustic leakage. One of ordinary skill would see this more inclusive data set as an obvious way to arrive at a solution of producing a test model that would be more likely to determine leakage in a headphone testing method.
With regard to 19, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations of 16, as discussed above.
STIRNEMANN, as modified by GROON and ZHANG and taught above, does not explicitly teach:
identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans; and
identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks.
ZHANG further teaches:
identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans; (ZHANG, Abstract: “facial mesh alignment, facial landmark detection and facial shape correspondence”; and see 1.1.2 “Landmark-based methods” and 3.2 “Landmark detection”; teaches multiple subjects, see 2.2.1: “Head datasets”; and 3D scanning, see Table 2.)
identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks. (ZHANG teaches correspondence of landmarks with features, see Section 3, and FIG.4)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify the system and method as disclosed by STIRNEMANN, as modified by GROON and ZHANG and taught above, to include identifying a plurality of anthropometric landmarks associated with each human subject of the plurality of human subjects based on the one or more three-dimensional scans; and identifying the plurality of anthropometric features based on the plurality of anthropometric landmarks. as further taught by ZHANG because this identification method would improve the ability to produce an accurate and reliable test model, based on data from actual human subjects in a wide range of populations, as suggested by ZHANG. Establishment of key landmarks relative to a variety of measurable features also would allow for modifications to accommodate individual anatomical features for testing.
With regard to Claim 20, STIRNEMANN in view of GROON and further in view of ZHANG, teaches the limitations Claims 1, 8 and 15.
STIRNEMANN further teaches:
identifying the clustered anthropometric features based on one or more clustering metrics. (STIRNEMANN, as above, FIG.7 with [0065]: “model is based on the clustering and classification approach. When establishing the model (analysis), a number N of experimentally obtained RECD curves are clustered, i.e. classes of the real ear acoustic coupling quantity or anatomical transfer quantity are formed.”; and [0065])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DE HAAN (US-20230054213-A1) – teaches testing of a hearing system, including headphones using regression analysis for determination of correlation related to acoustic leak.
LIAN (WO-2022247673-A1) – teaches test method for determination of leakage using energy signal and correlation methods.
BEGIN (“Headphone Electroacoustic Measurements”, Application Note, Joe Begin© 2021 Audio Precision, Inc and GRAS Sound & Vibration) – teaches overview of relationship between anthropometric values for ear canal and fit of a hearing device (industrial paper).
MOKHTARU (Mokhtari, et al., "Frequency and amplitude estimation of the first peak of head-related transfer functions from individual pinna anthropometry", J. Acoust. Soc. Am. 137, 690-701, 2015) – teaches analysis of head related transfer functions as correlated with anthropometric measures for concha depth resonance to determine frequency dependence as related to measured anatomical features.
MOUOUDI (Mououdi, et al., “Measuring the External Ear for Hearing Protection Device Design”, Ergonomics in Design The Quarterly of Human Factors Applications 26(3):4-8, March 2018) – teaches anthropometric measurements of external ear dimensions for designing hearing protection devices with method for construction of models based on measured data and statistical analysis.
RASETSHWANE (Rasetshwane, et al., “Measurements of Wide-Band Cochlear Reflectance in Humans” JARO 13: 591–607 (2012)) – teaches methods as referenced in GROON above, regarding data collection of anthropometric features related to hearing instruments.
YAN (Yan, et al., “An earphone fit deviation analysis algorithm”, Sci Rep 13, 1084 (2023)) – teaches accurate method for evaluating the fit of earphones, with detailed statistical analysis method for establishing anatomical features on a model.
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/TONI D SAUNCY/Examiner, Art Unit 2857
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
