DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
2. The Amendment filed January 22, 2026 has been entered. Claims 11, and 22-23 have been previously cancelled. Claim 1 has been amended. Claims 1-10, and 12-21 remain pending in the application.
CLAIM INTERPRETATION
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
5. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: a computing device in claim 12.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
6. 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 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.
7. 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 of this title, 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.
8. 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.
11. Claims 1-10, and 12-21 are rejected under 35 U.S.C. 103 as being unpatentable over Rumsey et al. U.S. Patent Application Publication 20090238370 (hereinafter, “Rumsey”) (cited by Applicant) in view of Wang et al. “A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network” Mechanical Systems and Signal Processing 45 (2014) pages 255–266, (hereinafter, “Wang”).
Regarding claim 12, Rumsey teaches a system (see computer system on upper left corner Fig. 2a; Referring to FIG. 2a there is shown an illustrative example of the general concept of a system according the second embodiment of the invention using the intrusive approach as described in connection with FIG. 1b above. In the shown example a comparison between the predicted auditory spatial quality of a reference system and of (in this specific example) a more simple reproduction system is carried out, par [0103], see Rumsey) for determining a scoring indicative (this physical measure must be translated to a predicted subjective (i.e. perceived) difference 10 that can for instance be described by means of suitable scales, Fig. 2a, par [0107]; An example of the prediction of listening test scores using this regression model are shown in FIG. 4, par [0168]) of a sound quality of an audio system (It is desirable to be able to evaluate the perceived spatial quality of audio processing, coding-decoding (codec) and reproduction systems without needing to involve human listeners, par [0002], see Rumsey), the system comprising:
at least one test signal generator (corresponds to “reference rendering” 14, Fig. 2a) configured to send at least one test signal (i.e., test signals 1) to at least one audio system (The reference system consists of a standard 5.1 surround sound reproduction system comprising a set-up of five loudspeakers 17 placed around a listening position in a well-known manner. The test signals 1 applied are presented to the loudspeakers 17 in the appropriate 5.1 surround sound format (through suitable power amplifiers, not shown in the figure) as symbolically indicated by the block “reference rendering” 14. The original test signals 1 may, if desired, be authored as indicated by reference numeral 8', Fig. 2a, par [0104], see Rumsey);
at least one frequency response detector to measure (via microphone of artificial listener 16, Fig. 2a, par [0104], see Rumsey):
a first frequency response of a reference audio system to the at least one test signal (The original test signals 1 may, if desired, be authored as indicated by reference numeral 8'. The sound signals emitted by the loudspeakers 17 generate an original sound field 15 that can be perceived by real listeners or recorded by means of an artificial listener (artificial head, head and torso simulator etc.) 16. The artificial listener 16 is provided with pinna replicas and microphones in a well-known manner and can be characterised by left and right head-related transfer functions (HRTF) and/or corresponding head-related impulse responses (HRIR), Fig. 2a, par [0104], see Rumsey); and
a second frequency response of a production audio system (The total "device under test" DUT 2 consists in the example of a processing/codec/transmission path 21 and a reproduction rendering 22 providing the final output signals to the loudspeakers 25. The loudspeakers generate a sound field 24 that is an altered version of the original sound field 15 of the reference system. This sound field is recorded by an artificial listener 16 and the output signals (left and right ear signals) from the artificial listener are provided to means 6'' that utilises appropriate metrics to derive a physical measure 20 that in an appropriate manner characterises the auditory spatial characteristics (in this case the same characteristics or attributes as the means 6') of the sound field 24, Fig. 2a, par [0105], see Rumsey);
at least one input unit (corresponds to means 6') to receive, at least one reference scoring indicative of a sound quality (corresponds to a physical measure 19, Fig. 2a) of the reference audio system (The sound signals (a left and a right signal) picked up by the microphones in the artificial listener 16 are provided (symbolized by reference numeral 18) to means 6' that utilises appropriate metrics to derive a physical measure 19 that in an appropriate manner characterises the auditory spatial characteristics or attributes of the sound field 15, Fig. 2a, par [0104], see Rumsey); and
a computing device (this limitation invokes 112(f), computing device 318, Fig. 3, specification page 9, lines 13-14) see computer system upper left corner Fig. 2a, see Rumsey; where acoustical simulations are manipulated in order to generate the binaural signals, it is possible to run extensive computer predictions and obtain results across an entire listening area, par [0172], see Rumsey) configured to include trained or calibrated interpretation means (10; see predicted spatial fidelity difference grade 10 in Fig. 1b; see also predicted subjective (i.e. perceived) difference 10 in Fig. 2a) for translating said difference (C) to perceptual assessments or ratings characterising either a perceived auditory spatial quality difference as a holistic quantity or one or more specific attributes characterising said perceived auditory spatial quality difference (Fig. 1b, par [0046], see Rumsey); Generally, the regression models (equations) or equivalent means such as a look-up table or artificial neural network used according to the invention weights the individual metrics according to calibrated values (par [0049], see Rumsey). The sound signals (a left and a right signal) picked up by the microphones in the artificial listener 16 are provided (symbolized by reference numeral 18) to means 6' that utilises appropriate metrics to derive a physical measure 19 that in an appropriate manner characterises the auditory spatial characteristics or attributes of the sound field 15. These physical measures 19 are provided to comparing means 9 (see Fig. 2a, par [0104] see Rumsey). The total "device under test" DUT 2 consists in the example of a processing/codec/transmission path 21 and a reproduction rendering 22 providing the final output signals to the loudspeakers 25. The loudspeakers generate a sound field 24 that is an altered version of the original sound field 15 of the reference system. This sound field is recorded by an artificial listener 16 and the output signals (left and right ear signals) from the artificial listener are provided to means 6'' that utilises appropriate metrics to derive a physical measure 20 that in an appropriate manner characterises the auditory spatial characteristics (in this case the same characteristics or attributes as the means 6') of the sound field 24. These physical measures 20 are provided to comparing means 9 where they are compared with the physical measures 19 provided by the metric means 6' in the reference system (see Fig. 2a, par [0105] see Rumsey). This objective measure M is provided to a prediction model 46, which may for instance be implemented as a regression model, lookup table, or an artificial neural network, which prediction model has been calibrated by means of suitable listening tests on real listeners as symbolically indicated by reference numeral 45. The prediction model 46 "translates" the objective measure M into a subjective perceptual measure 47, in the shown example of the envelopment as it would be perceived by human listeners. This subjective perceptual measure 47 can for instance be characterised by a rating on a suitable scale as indicated by 47' in the figure (par [0110] see Rumsey). The following is an example of the use of selected metrics, together with special test signals, also a regression model calibrated using listening test scores derived from human listeners, to measure the reduction in spatial quality of 5-channel ITU BS.775 programme material compared with a reference reproduction, when subjected to a range of processes modifying the audio signals (representative of different DUTs), including downmixing, changes in loudspeaker location, distortions of source locations, and changes in interchannel correlation (par [0105] see Rumsey). The block diagram in FIG. 3(b) furthermore indicates that although one or more of the said metrics may be provided with the full set of raw data 43 (the signals, measurements or quantities I.sub.1, . . . I.sub.5) one or more metrics may be provided with only one or more sub-sets of raw data, as indicated by the three dashed arrows (Fig. 3(b), par [0112], see Rumsey). Referring to FIG. 8e, plot (a) shows--for a given frequency band--a distribution of ITD as a function on azimuth in the range 0 degrees (directly in front of a listener) to 90 degrees (directly to the right of the listener) (par [0197] see Rumsey).
Rumsey teaches the first frequency response, the reference scoring (see above). Rumsey further teaches the use of subjective training data, alternative outputs from the system can be achieved through supervised training. Soundfield features obtained in this way can be used in an overall quality predictor (par [0213] see Rumsey). This objective measure M is provided to a prediction model 46, which may for instance be implemented as a regression model, lookup table, or an artificial neural network (see Fig. 3a, par [0110], see Rumsey).
However, Rumsey does not explicitly disclose supply a training dataset comprising the first frequency response and the reference scoring to an artificial neural network; train the artificial neural network using the training dataset to predict a scoring for the reference audio system; and process, using the artificial neural network, an input dataset comprising the second frequency response to predict a scoring indicative of a sound quality of the production audio system.
Wang teaches a sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network (see Title) in which till now, only the equal-loudness-contours (i.e., frequency response) based Zwicker loudness model is widely accepted and the standard ISO 532B has been applied in some commercial programs. The sharpness model has passed round robin tests (page 256, first paragraph, see Wang). A three-layer ANN with a hidden layer for sound annoyance evaluation of vehicle noise, so-called ANN-SAE, is established, as shown in Fig. 10. The input layer includes three neurons: total loudness, sharpness and roughness. For training the neural network, an input matrix of 3n is defined and constructed.
IN = [LOUDNESS SHARPNESS ROUGHNESS]----T|3n
where, n is the number of sample signals in training. In this work, we take 80 signals from the interior noise database, i.e., let n=80. The rest ten signals are provisionally put aside for verifying the trained ANN model. The output layer with one neuron is the rating score of the subjectively evaluated annoyance (see pages 263, section 6.2 ANN-SAE modeling). Fig. 8 shows the weighting coefficients with respect to modulation frequencies Hi (i=1, 5, 16, 21, and 42) (see Fig. 8, see Wang).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network taught by Wang with the system of Rumsey such that to obtain supply a training dataset comprising the first frequency response and the reference scoring to an artificial neural network; train the artificial neural network using the training dataset to predict a scoring for the reference audio system; and process, using the artificial neural network, an input dataset comprising the second frequency response to predict a scoring indicative of a sound quality of the production audio system as claimed in order to estimate sound quality of the vehicle interior noises, which is very helpful for vehicle acoustical designs and improvements as suggested by Wang in Abstract.
Regarding claim 13, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein the reference scoring is indicative of a subjective sound quality of the reference audio system and/or is determined by at least one human expert evaluator (The system, device and method according to the invention includes descriptions of ecologically valid, or programme like test signals, and sequences thereof, that have properties such that when applied to the DUT and subsequently measured by the algorithms employed by the system, lead to predictions of perceived spatial quality that closely match those given by human listeners when listening to typical programme material that has been processed through the same DUT. These test signals are designed in a generic fashion in such a way that they stress the spatial performance of the DUT across a range of relevant spatial attributes, par [0137], see Rumsey).
Regarding claim 14, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches further comprising a sound recording device, wherein: measuring the first frequency response comprises recording sound emitted by the reference audio system at a recording position relative to the reference audio system using the sound recording device; or measuring the second frequency response comprises recording sound emitted by the production audio system at the recording position relative to the production audio system using the sound recording device (Improved localisation and front-back disambiguation can as mentioned above be achieved by head movement The resolution of human sound localisation is typically most accurate directly in front of the listener, so the location of a stationary source may be refined by turning the face towards the direction of the sound. Equally, such a procedure can be used in the present invention. Another active listening technique from human behaviour that can be incorporated into the present invention is head movement aimed at distinguishing between localisation directions in front of and behind the listener, par [0208], see Rumsey). Fig. 4 shows the sound transmission coefficients of the auricle, cavum conchae and ear canal of human ear (see Fig. 4, see Wang)).
Regarding claim 15, Rumsey in view of Wang teaches the system of claim 14. Rumsey in view of Wang, as modified, teaches wherein the reference scoring is indicative of a subjective sound quality of the reference audio system at the recording position (Fig. 3 shows the microphone positions in the tests: (a) driver, (b) assistant driver and (c) rear passenger, see Fig. 3, see Wang).
Regarding claim 16, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein the at least one test signal comprises noise (Test signal 1: a decorrelated pink noise played through all five channels simultaneously. Test signal 2: thirty-six pink noise bursts, pairwise-constant-power-panned around the five loudspeakers from 0.degree. to 360.degree. in 10.degree. increments. Each noise burst lasts one second, par [0165], see Rumsey).
Regarding claim 17, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein measuring the first frequency response or the second frequency response comprises recording sound in a production environment (Fig. 3 shows the microphone positions in the tests: (a) driver, (b) assistant driver and (c) rear passenger, see Fig. 3, see Wang).
Regarding claim 18, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein: measuring the first frequency response comprises recording sound emitted by the reference audio system, wherein a gain of the reference audio system is set to a predetermined level; or measuring the second frequency response comprises recording sound emitted by the production audio system, wherein the gain of the production audio system is set to the predetermined level (The reference system consists of a standard 5.1 surround sound reproduction system comprising a set-up of five loudspeakers 17 placed around a listening position in a well-known manner. The test signals 1 applied are presented to the loudspeakers 17 in the appropriate 5.1 surround sound format (through suitable power amplifiers (i.e., gain), not shown in the figure) as symbolically indicated by the block "reference rendering" 14. The original test signals 1 may, if desired, be authored as indicated by reference numeral 8'. The sound signals emitted by the loudspeakers 17 generate an original sound field 15 that can be perceived (i.e., predetermined level) by real listeners or recorded by means of an artificial listener (artificial head, head and torso simulator etc.) 16 (see Fig. 2a, par [0104], see Rumsey). A calculation of loudness of each critical band is performed in block 52 as mentioned above and utilised for a final loudness weighting 54. One purpose of this final loudness weighting 54 is to ensure that powerful frequency components actually play the most significant role in the overall prediction and it may even take psychoacoustic masking into account, so that frequency components (the output from given critical bands) that can probably not be perceived are not having a significant impact on predicted azimuth, Fig. 5, par [0153], see Rumsey).
Regarding claim 19, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein the reference audio system and/or the production audio system comprises a vehicle audio system (In objective evaluation of vehicle noises, the A-weighted sound pressure level (SPL) is usually considered, due to its simple application. It has been found that the A-weighted SPL sometimes does not agree with the passenger's feeling of the sound. Thus, the psychoacoustical indices in common use, such as loudness, sharpness and roughness, need to be calculated for more accurate evaluation of the sounds in vehicle engineering, see page 258, section 4, Calculation of psychoacoustical indices, see also Fig, 3, see Wang).
Regarding claim 20, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches wherein: the training dataset further comprises data indicative of one or more of a brand, a model or characteristics of the reference audio system and/or components of the reference audio system; or the input dataset further comprises data indicative of one or more of a brand, a model, or characteristics of the production audio system and/or components of the production audio system (In the case of each database, a range of programme material is chosen that, in the opinion of experts in the field, and a systematic evaluation of the spatial attributes considered important in that field, is representative of the genre. This programme material is subjected to a range of spatial audio processes, based on the known characteristics of the DUTs that are to be tested, appropriate to the field, giving rise to a range of spatial quality variations. It is important that all of the relevant spatial attributes are considered and that as many as possible of the spatial processes likely to be encountered in practical situations are employed, par [0155], see Rumsey).
Regarding claim 21, Rumsey in view of Wang teaches the system of claim 12. Rumsey in view of Wang, as modified, teaches, further comprising a display device configured to display the predicted scoring for the production audio system (see display screen in the left upper corner, Fig. 2a, see Rumsey; Another novel approach of the proposed Envelometer is the scale used to display the measured envelopment. It is proposed to use a 100-point scale (see par [0241], see Rumsey); With reference to FIG. 20 there is shown the results of the calibration. As it can be seen, the correlation between the scores obtained in the listening tests (measured) and the predicted scores by means of the envelometer was equal to 0.9. The average error of calibration was 8.4 points with respect to 100-point scale, par [0274], see Rumsey).
Regarding claim 1, this claim merely reflects the method to the apparatus claim of Claim 12 and is therefore rejected for the same reasons.
Regarding claim 2, this claim merely reflects the method to the apparatus claim of Claim 13 and is therefore rejected for the same reasons.
Regarding claim 3, this claim merely reflects the method to the apparatus claim of Claim 14 and is therefore rejected for the same reasons.
Regarding claim 4, this claim merely reflects the method to the apparatus claim of Claim 15 and is therefore rejected for the same reasons.
Regarding claim 5, this claim merely reflects the method to the apparatus claim of Claim 16 and is therefore rejected for the same reasons.
Regarding claim 6, this claim merely reflects the method to the apparatus claim of Claim 17 and is therefore rejected for the same reasons.
Regarding claim 7, this claim merely reflects the method to the apparatus claim of Claim 18 and is therefore rejected for the same reasons.
Regarding claim 8, this claim merely reflects the method to the apparatus claim of Claim 19 and is therefore rejected for the same reasons.
Regarding claim 9, this claim merely reflects the method to the apparatus claim of Claim 20 and is therefore rejected for the same reasons.
Regarding claim 10, this claim merely reflects the method to the apparatus claim of Claim 21 and is therefore rejected for the same reasons.
Response to Arguments
9. Applicant's arguments filed January 22, 2026 have been fully considered but they are not persuasive.
10. Applicant asserts on pages 7-8, regarding claim interpretation:
“…. Here, a person or ordinary skill in the art reading the specification would understand the term "computing device" to have a sufficiently definite meaning. More specifically, like the claim term examples of "modernizing device" and "computing unit" identified in the MPEP, the term "computing device" when read in light of the specification conveys sufficient definite structure to persons of ordinary skill in the art to preclude application of 112(f). See MPEP § 2181(I)(A). Accordingly, the interpretation of the claim terms under 35 U.S.C. § 112(f) does not apply”.
Applicant has not pointed how each of the functional limitations is described in the specification such the “computing device” would be recognized as sufficiently definite structure to perform the recited functions. Note in Inventio AG v. Thyssenkrupp Elevator Americas Corp.: “The written descriptions also explain the step that the computer program product performs, see id. col.6 l.3–col.7 l.25, as well as the interaction between the computing unit and modernizing device, id. col.7 ll.26–48, and the computing unit and the floor terminals, id. col.8 ll.6–22.”
Unlike Inventio, the instant “computing device” does not explain the steps a program performs as well as the interactions with other elements in the system that one would recognize “computing device” as connoting sufficient definite structure. The specification repeats supplying a training dataset without providing a description on how the dataset is provided to the neural net. The specification describes using known techniques for training an artificial neural network by locally minimizing a loss function may be used, for example backpropagation. The specification does not describe how the processing of the input dataset is achieved. Each of “computing device” functionality is disclosed at a high level of generality that one of ordinary skill in the art would not recognize “computing device” as connoting sufficient definite structure. The black box description of the instant “computing device”
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is similar to the black box of Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1351, 115 USPQ2d 1105
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While portions of the claim do describe certain inputs and outputs at a very high level (e.g., communications between the presenter and audience member computer systems), the claim does not describe how the “distributed learning control module” interacts with other components in the distributed learning control server in a way that might inform the structural character of the limitation-in-question or otherwise impart structure to the “distributed learning control module” as recited in the claim.
Hence the specification does not disclose how the “computing device” interacts with other elements that would inform one of ordinary skill in the art of the structural character of the limitation or impart structure to the “computing device”. Applicant has merely alleged that “computing device” connotes sufficient definite structure without identify written description to support the allegation. Therefore, “computing unit” invokes 35 USC 112 (f) as modified by each of the recited functional limitations.
11. Applicant asserts on pages 9-10, regarding claim 1:
“…. In the rejections, the Examiner maps the training dataset, recited in claim 1, to the training dataset comprising interior noises of a vehicle under different conditions, disclosed in Wang. See Office Action at 12-13. Based on this claim mapping, to teach or suggest the above limitations of claim 1, Wang would have to disclose supplying a training dataset comprising a frequency response of the reference audio system to a test signal and a reference scoring to an artificial neural network. Importantly, Wang contains no such teachings. Instead, Wang merely discloses supplying a training dataset comprising interior noises of a vehicle under different conditions. However, the Wang 3n input matrix only includes scalar inputs representing loudness, sharpness, and roughness. These scalar inputs are not a frequency response of the reference audio system to a test signal. Additionally, Wang does not teach or disclose supplying a training dataset a reference scoring to the neural network, let alone supplying a training dataset comprising a frequency response of the reference audio system to a test signal and a reference scoring to an artificial neural network, as required by the claim language. In view of at least these distinctions, Applicant submits that Wang cannot be properly interpreted as teaching or suggesting the above limitations of claim 1.
Examiner respectfully disagrees since in determining the unobviousness of claim invention, it is formulated rejection based on combinations of references, Rumsey in view of Wang. Thus one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 208 USPQ 871 (CCPA 1981). It is noted that although “the Wang 3n input matrix only includes scalar inputs representing loudness, sharpness, and roughness”, these data representing frequency response data (where, n is the number of sample signals in training. In this work, we take 80 signals (e.g., frequency response) from the interior noise database, i.e., let n=80. The rest ten signals are provisionally put aside for verifying the trained ANN model see page 263, last paragraph, see Fig. 10, page 264, see Wang). As presented above in the Office Action Rumsey in view of Wang teaches “Rumsey teaches the first frequency response, the reference scoring (see above). Rumsey further teaches the use of subjective training data, alternative outputs from the system can be achieved through supervised training. Soundfield features obtained in this way can be used in an overall quality predictor (par [0213] see Rumsey). This objective measure M is provided to a prediction model 46, which may for instance be implemented as a regression model, lookup table, or an artificial neural network (see Fig. 3a, par [0110], see Rumsey).
However, Rumsey does not explicitly disclose supply a training dataset comprising the first frequency response and the reference scoring to an artificial neural network; train the artificial neural network using the training dataset to predict a scoring for the reference audio system; and process, using the artificial neural network, an input dataset comprising the second frequency response to predict a scoring indicative of a sound quality of the production audio system.
Wang teaches a sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network (see Title) in which till now, only the equal-loudness-contours (i.e., frequency response) based Zwicker loudness model is widely accepted and the standard ISO 532B has been applied in some commercial programs. The sharpness model has passed round robin tests (page 256, first paragraph, see Wang). A three-layer ANN with a hidden layer for sound annoyance evaluation of vehicle noise, so-called ANN-SAE, is established, as shown in Fig. 10. The input layer includes three neurons: total loudness, sharpness and roughness. For training the neural network, an input matrix of 3n is defined and constructed.
IN = [LOUDNESS SHARPNESS ROUGHNESS]----T|3n
where, n is the number of sample signals in training. In this work, we take 80 signals from the interior noise database, i.e., let n=80. The rest ten signals are provisionally put aside for verifying the trained ANN model. The output layer with one neuron is the rating score of the subjectively evaluated annoyance (see pages 263, section 6.2 ANN-SAE modeling). Fig. 8 shows the weighting coefficients with respect to modulation frequencies Hi (i=1, 5, 16, 21, and 42) (see Fig. 8, see Wang).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network taught by Wang with the system of Rumsey such that to obtain supply a training dataset comprising the first frequency response and the reference scoring to an artificial neural network; train the artificial neural network using the training dataset to predict a scoring for the reference audio system; and process, using the artificial neural network, an input dataset comprising the second frequency response to predict a scoring indicative of a sound quality of the production audio system as claimed in order to estimate sound quality of the vehicle interior noises, which is very helpful for vehicle acoustical designs and improvements as suggested by Wang in Abstract.”
Conclusion
12. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/C.P.T/Examiner, Art Unit 2695
/VIVIAN C CHIN/Supervisory Patent Examiner, Art Unit 2695