DETAILED ACTION
The examiner would like to note that the claims have been deemed to be containing eligible subject matter under 35 U.S.C. 101 due to the inclusion of steps “acquir[ing] first data indicative of a temporal change of an intensity of sound emitted by an apparatus” and “extracting… a maximum value in each section of a time width corresponding to a resolution at which human voice is unrecognizable” which incorporate concepts which cannot reasonably be performed mentally, i.e. determining a change of intensity of sound emitted by an apparatus and extracting a maximum value are operations which cannot be reasonably be performed mentally, nor are there explicit mathematical operations recited to perform these functions. Therefore, the claims are eligible under 35 U.S.C. 101.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119
(a)-(d). The certified copy has been filed for the parent Application No. JP2022-151140, filed on 09/22/2022.
Information Disclosure Statement
The information disclosure statement(s) submitted on 01/24/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-6 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ikeda (US-20230038457-A1).
Regarding claim 1, Ikeda discloses: an information processing apparatus (Abstract, classification information acquiring unit (13) that acquires classification information of a sound) comprising:
a processor ([0112] one or more processors) configured to:
acquire first data indicative of a temporal change of an intensity of sound emitted by an apparatus ([0024] sound emitted by a biological body such as a human or an object, [0044] The extraction unit 12 can extract a feature of the sound from the sound data… “feature of the sound” may be, for example, a parameter extracted based on at least one of a temporal change in a sound, [Extracting sound features based on temporal change in sound, in view of Fig. 2 which defines a graph of received sound data with intensity (amplitude) represented on the vertical axis ([0055]), indicating the temporal change to be intensity of sound. Further, an object tracks to an apparatus]);
generate second data by extracting, from the first data, a maximum value in each section of a time width corresponding to temporal resolution at which human voice is unrecognizable and discarding values other than the maximum value ([0056] the extraction unit 12 may extract top ten peaks from the envelope waveform to extract values indicated by the peaks on the vertical axis of the graph (hereinafter, also referred to as a peak value) as features, [0058] the extraction unit 12 may extract the top three peaks from the frequency waveform to extract frequency positions of the peaks as features, [Only extracting top three peaks, in view of a larger plurality of ten peaks, indicates the extracted peaks to be extracted from first data containing a larger set of peaks. Further, only extracting peaks indicates all other values/features than the peaks, i.e. maximums, to be discarded. Further still, as the input sound has no dependency upon containing human voice as currently claimed, and the context of Ikeda is in abnormal sound detection of breathing ([0027]), the peaks extracted will inherently not contain recognizable human voice regardless of the temporal rate with which the peaks are extracted]); and,
transmit the second data to an external apparatus ([Fig. 8, Feature Data 21, used for Training Data Creation 14, used for estimating the type of sound 31, which is sent to display apparatus 5], [0061] The control apparatus 1 may display the graphs of the time waveform, the frequency waveform, and the spectrogram waveform on the display unit of the control apparatus 1 or a display apparatus connected to the control apparatus 1, [Estimating sound and outputting that estimation based on extracted peaks from input sound to be displayed in the form of a waveform on display apparatus indicates the peaks to be transmitted to the external display apparatus in order to be appropriately represented on the generated waveform, see time waveform of Fig. 2]).
Regarding claim 5, Ikeda discloses: a non-transitory computer readable storage medium storing a program causing a computer to execute a process ([0112] The computer includes, for example, one or more processors and a computer-readable recording medium that stores the above program), the process comprising:
acquiring first data indicative of a temporal change of an intensity of sound emitted by an apparatus ([0024] sound emitted by a biological body such as a human or an object, [0044] The extraction unit 12 can extract a feature of the sound from the sound data… “feature of the sound” may be, for example, a parameter extracted based on at least one of a temporal change in a sound, [Extracting sound features based on temporal change in sound, in view of Fig. 2 which defines a graph of received sound data with intensity (amplitude) represented on the vertical axis ([0055]), indicating the temporal change to be intensity of sound. Further, an object tracks to an apparatus]);
generating second data by extracting, from the first data, a maximum value in each section of a time width corresponding to temporal resolution at which human voice is unrecognizable and discarding values other than the maximum value ([0056] the extraction unit 12 may extract top ten peaks from the envelope waveform to extract values indicated by the peaks on the vertical axis of the graph (hereinafter, also referred to as a peak value) as features, [0058] the extraction unit 12 may extract the top three peaks from the frequency waveform to extract frequency positions of the peaks as features , [Only extracting top three peaks, in view of a larger plurality of ten peaks, indicates the extracted peaks to be extracted from first data containing a larger set of peaks. Further, only extracting peaks indicates all other values/features than the peaks, i.e. maximums, to be discarded. Further still, as the input sound has no dependency upon containing human voice as currently claimed, and the context of Ikeda is in abnormal sound detection of breathing ([0027]), the peaks extracted will inherently not contain recognizable human voice regardless of the temporal rate with which the peaks are extracted]); and,
transmitting the second data to an external apparatus ([Fig. 8, Feature Data 21, used for Training Data Creation 14, used for estimating the type of sound 31, which is sent to display apparatus 5], [0061] The control apparatus 1 may display the graphs of the time waveform, the frequency waveform, and the spectrogram waveform on the display unit of the control apparatus 1 or a display apparatus connected to the control apparatus 1, [Estimating sound and outputting that estimation based on extracted peaks from input sound to be displayed in the form of a waveform on display apparatus indicates the peaks to be transmitted to the external display apparatus in order to be appropriately represented on the generated waveform, see time waveform of Fig. 2]).
Regarding claim 6, Ikeda discloses: an information processing method (Abstract, classification information acquiring unit (13) that acquires classification information of a sound) comprising:
acquiring first data indicative of a temporal change of an intensity of sound emitted by an apparatus ([0024] sound emitted by a biological body such as a human or an object, [0044] The extraction unit 12 can extract a feature of the sound from the sound data… “feature of the sound” may be, for example, a parameter extracted based on at least one of a temporal change in a sound, [Extracting sound features based on temporal change in sound, in view of Fig. 2 which defines a graph of received sound data with intensity (amplitude) represented on the vertical axis ([0055]), indicating the temporal change to be intensity of sound. Further, an object tracks to an apparatus]);
generating second data by extracting, from the first data, a maximum value in each section of a time width corresponding to temporal resolution at which human voice is unrecognizable and discarding values other than the maximum value ([0056] the extraction unit 12 may extract top ten peaks from the envelope waveform to extract values indicated by the peaks on the vertical axis of the graph (hereinafter, also referred to as a peak value) as features, [0058] the extraction unit 12 may extract the top three peaks from the frequency waveform to extract frequency positions of the peaks as features , [Only extracting top three peaks, in view of a larger plurality of ten peaks, indicates the extracted peaks to be extracted from first data containing a larger set of peaks. Further, only extracting peaks indicates all other values/features than the peaks, i.e. maximums, to be discarded. Further still, as the input sound has no dependency upon containing human voice as currently claimed, and the context of Ikeda is in abnormal sound detection of breathing ([0027]), the peaks extracted will inherently not contain recognizable human voice regardless of the temporal rate with which the peaks are extracted]); and,
transmitting the second data to an external apparatus ([Fig. 8, Feature Data 21, used for Training Data Creation 14, used for estimating the type of sound 31, which is sent to display apparatus 5], [0061] The control apparatus 1 may display the graphs of the time waveform, the frequency waveform, and the spectrogram waveform on the display unit of the control apparatus 1 or a display apparatus connected to the control apparatus 1, [Estimating sound and outputting that estimation based on extracted peaks from input sound to be displayed in the form of a waveform on display apparatus indicates the peaks to be transmitted to the external display apparatus in order to be appropriately represented on the generated waveform, see time waveform of Fig. 2]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ikeda in view of Ueda (US-20230239639-A1).
Regarding claim 2, Ikeda discloses: the information processing apparatus according to claim 1.
Ikeda does not disclose:
wherein the processor is configured to:
determine from a spectrogram of sound emitted by the apparatus whether or not sound expressed by the spectrogram is sound during a normal state of the apparatus.
Ueda discloses:
determine from a spectrogram of sound emitted by the apparatus whether or not sound expressed by the spectrogram is sound during a normal state of the apparatus ([0072] the extraction unit 17 extracts the analysis range corresponding to the inferred frequency range of the spectrogram (steps S150 and S159), and the abnormal sound diagnosis unit 21 of the server 20 diagnoses, based on the information indicating the analysis range extracted in step S159, the cause of the abnormal sound, [Identifying a spectrogram to be containing abnormal sound to be identified indicates a required determination that the sound is not in a normal state in order to be categorized as abnormal]).
Ikeda and Ueda are considered analogous art within spectral analysis of sound signals (G10L25/18) in the context of abnormal sound detection/classification. Therefore, 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 the teachings of Ikeda to incorporate the teachings of Ueda, because of the novel way to diagnose abnormal sound through extracted characteristic frequencies and generation time period, enhancing the accuracy of abnormal sound diagnosis (Ueda, [0079]).
Ikeda further discloses:
generate the first data from the spectrogram in a case where it is determined that the sound expressed by the spectrogram is not sound during the normal state ( [0044] The extraction unit 12 can extract a feature of the sound from the sound data… “feature of the sound” may be, for example, a parameter extracted based on at least one of a temporal change in a sound, [Extracting sound features based on temporal change in sound, in view of Fig. 2 which defines a graph of received sound data with intensity (amplitude) represented on the vertical axis ([0055]), indicating the temporal change to be intensity of sound. Further, an object tracks to an apparatus. Further still, in view of the normal/abnormal state determination of Ueda as applied to the normal/abnormal classification based on extracted features of spectrograms (Fig. 2) of Ikeda, [0027].]).
Claim(s) 3, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ikeda in view of Ueda, further in view of Mishima et al. (US-20220293081-A1), hereinafter Mishima.
Regarding claim 3, Ikeda in view of Ueda discloses: the information processing apparatus according to claim 2.
Ikeda in view of Ueda does not disclose:
wherein the processor is further configured to:
conduct repetition occurrence frequency analysis on the spectrogram in a time axis direction;
find a peak of an intensity that meets a predetermined condition from a result of the repetition occurrence frequency analysis and generate third data indicative of a repetition occurrence frequency and an intensity of the peak thus found; and,
transmit the third data to the external apparatus in association with the second data.
Mishima discloses:
wherein the processor is further configured to:
conduct repetition occurrence frequency analysis on the spectrogram in a time axis direction ([0089] On the left side of FIG. 3, the horizontal axis represents the time frame , [0090] The right side of FIG. 3 shows the histogram that is formed by the noise range estimation unit 103 from the number of times the power appears. The noise range estimation unit 103 counts the number of times each of the powers on the left side of FIG. 3 appears, and calculates the histogram shown on the right side of FIG. 3 as the power distribution);
find a peak of an intensity that meets a predetermined condition from a result of the repetition occurrence frequency analysis ([0091] In FIG. 4, a solid line represents the power spectrum of the moving object sound. As described above, the moving object sound output from the moving object has single peak frequency characteristics. A chain line represents a peak frequency width f.sub.target, and the peak frequency width f.sub.target is stored in the storage unit 102 as the acoustic characteristic information, [The peak of Fig. 4 meets the predefined condition, i.e. fTarget, based on the power, i.e. repetition occurrence frequency analysis of Fig. 3]) and generate third data indicative of a repetition occurrence frequency and an intensity of the peak thus found ([0091] the peak frequency width f.sub.target is stored in the storage unit 102 as the acoustic characteristic information, [Peak frequency is clearly associated with power, i.e. indicative of repetition occurrence frequency, as shown in Fig. 4]); and,
transmit the third data to the external apparatus in association with the second data ([0114] The peak frequency width f.sub.target [the number of frequency bins] is stored in the storage unit 102 as the acoustic feature of the moving object sound, [Storing a peak, i.e. maximum tracking to second data, frequency width indicates a transmission of third data, i.e. the power associated with the frequency, to the storage unit in association with the second data, i.e. maximum value. Further, in view of the previously disclosed external apparatus of Ikeda responsible for displaying waveforms which would inherently contain the peak and frequency width information as determined in Mishima]).
Ikeda, Ueda, and Mishima are considered analogous art within noise estimation/detection. Therefore, 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 the teachings of Ikeda in view of Ueda to incorporate the teachings of Mishima, because of the novel way to monitor the position/location of a device in addition to sounds produced by the device, allowing for improved noise estimation in moving objects (Mishima, [0013]).
Regarding claim 4, Ikeda in view of Ueda discloses: the information processing apparatus according to claim 2.
Ikeda in view of Ueda does not disclose:
wherein the processing is further configured to:
generate, from the spectrogram, fourth data indicative of a distribution of intensities of sound at frequencies in the spectrogram;
transmit the fourth data to the external apparatus in association with the second data.
Mishima discloses:
wherein the processing is further configured to:
generate, from the spectrogram, fourth data indicative of a distribution of intensities of sound at frequencies in the spectrogram ([Fig. 3, Right Side], [0090] The right side of FIG. 3 shows the histogram that is formed by the noise range estimation unit 103 from the number of times the power appears. The noise range estimation unit 103 counts the number of times each of the powers on the left side of FIG. 3 appears, and calculates the histogram shown on the right side of FIG. 3 as the power distribution).
Ikeda, Ueda, and Mishima are considered analogous art within noise estimation/detection. Therefore, 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 the teachings of Ikeda in view of Ueda to incorporate the teachings of Mishima, because of the novel way to monitor the position/location of a device in addition to sounds produced by the device, allowing for improved noise estimation in moving objects (Mishima, [0013]).
Ikeda further discloses:
transmit the fourth data to the external apparatus in association with the second data ([Fig. 8, Feature Data 21, used for Training Data Creation 14, used for estimating the type of sound 31, which is sent to display apparatus 5], [0061] The control apparatus 1 may display the graphs of the time waveform, the frequency waveform, and the spectrogram waveform on the display unit of the control apparatus 1 or a display apparatus connected to the control apparatus 1 [In view of the previously disclosed external apparatus and second data of Ikeda, considering the histogram of Mishima to be representing frequency of power(s), there is indicated that this information could be displayed on the external display apparatus of Ikeda without a change in functionality to Ikeda as Ikeda discloses the display apparatus to be receiving features of particular waveforms, i.e. peaks (with inherent associated powers and frequencies) which would define power categories and associated frequencies of the histogram, to be displayed with the waveforms. Therefore, the display apparatus would display the fourth data of Mishima as part of the feature extracted waveform of Fig. 2 of Ikeda]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen (US-20230030911-A1) discloses “An abnormal sound detection method and apparatus are provided. First, an abnormal sound signal is received. Next, the abnormal sound signal is converted into a spectrogram. Afterwards, image recognition is performed on the spectrogram for obtaining a defect category corresponding to the abnormal sound signal.” (abstract). See entire document.
Oku et al. (US-20230274757-A1) discloses “An apparatus and/or method is proposed that can show evidence for judgment results in an abnormality judgment using a judgment model obtained by machine learning. Pieces of processed data with a mask corresponding to characteristics of waveform data set on a spectrogram of waveform data are created using a judgment model obtained by machine learning by sequentially shifting the mask in a direction corresponding to the mask, a change rate or a change degree of the waveform data of each piece of created processed data from the spectrogram is calculated, each area in which the mask on the spectrogram of the waveform data is set based on the calculated change rate or change degree is colored with a color or concentration corresponding to the change rate or change degree of the processed data when the mask is set so as to draw and display a judgment evidence image” (abstract). Specifically, Oku discloses spectrogram analysis of received sounds through feature extraction in given time intervals before classifying a sound as one particular type of abnormal sound. See entire document.
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/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655