DETAILED ACTIONNotice 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 8 and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (“HearLiquid: Non-intrusive Liquid Fraud Detection Using Commodity Acoustic Devices”, hereinafter Yang – See IDS filed 11/18/25). Regarding claim 1, Yang discloses an apparatus comprising collecting acoustic waves penetrating a liquid to be tested using a microphone, and extracting acoustic absorption-transmission curve features from the acoustic waves to generate a liquid fingerprint, wherein the acoustic absorption-transmission curve features represent a ratio of an energy of an acoustic signal penetrating the liquid to an energy of an acoustic signal emitted across a plurality of frequencies; and inputting the liquid fingerprint into a trained neural network model for detection processing, and outputting detection results, wherein the trained neural network model is trained with a plurality of sets of data, each of the plurality of sets of data comprises acoustic absorption-transmission curve features and a label identifying a feature in the acoustic absorption-transmission curve features meeting a detection requirement (See Pg. 1, Para. 1, “Introduction” and Pg. 8, Para. 4.1, “Hardware”).
Regarding claim 2, the step of collecting acoustic waves penetrating the liquid to be tested, and extracting acoustic absorption-transmission curve features from the acoustic waves to generate a liquid fingerprint comprises collecting acoustic waves penetrating the liquid to be tested and pre-processing the acoustic waves to obtain an acoustic wave signal with background noise removed; performing a fast Fourier transform on the processed acoustic wave signal and extracting a frequency domain amplitude at each frequency; and dividing the frequency domain amplitude by a corresponding amplitude in a spectrum to obtain the acoustic absorption-transmission curve features (See Pg. 1, Para. 1, “Introduction” and Pg. 2, Para. 2.1, “Liquid’s Absorption of the Acoustic Signal”).
Regarding claim 3, the step of collecting acoustic waves penetrating the liquid to be tested and pre-processing the acoustic waves to obtain an acoustic wave signal with background noise removed comprises pre-processing the collected acoustic waves by a high-pass filter with a cutoff frequency of 18 kHz to remove the background noise (See Pg. 5, Para. 3.2, “Acoustic Signal Generation” and Para. 3.3, “Signal Pre-processing and AATC Extraction”).
Regarding claim 4, before the step of performing a fast Fourier transform on the processed acoustic wave signals and extracting a frequency domain amplitude at each frequency, the method comprises processing the acoustic wave signal with background noise removed by a hamming window function (See Pg. 5, Para. 3.3, “Signal Pre-processing and AATC Extraction”).
Regarding claim 5, in the neural network model, the acoustic absorption-transmission curve features obtained in different containers are processed by a frequency-sensitive regularizer and a variational auto-encoder, to obtain a standard acoustic absorption-transmission curve feature of the liquid to be tested (See Pg. 1, Para. 1, “Introduction”).
Regarding claim 6, the step of inputting the liquid fingerprints into a trained neural network model for detection processing, and outputting detection results, comprises the neural network model comprises a 5-layer fully connected neural network having 32 neurons in each layer of the neural network (See Pg. 6, Para. 3.4, “AATC Calibration: Tackling the Effect of Different Acoustic Devices” and Para. 3.5, “Data Augmentation: Tackling the Effect of Different Relative Device container Positions”).
Regarding claim 7, before the step of inputting the liquid fingerprint into a trained neural network model for detection processing, and outputting detection results, the method further comprises selecting a corresponding neural network model according to a received detection instruction; wherein the detecting instruction comprises detecting a quality of liquid; each set of data for training the neural network model comprises: acoustic absorption-transmission curve features, and a label identifying the quality of liquid in that acoustic absorption-transmission curve features (See Pg. 1, Para. 1, “Introduction” and Pg. 8, Para. 3.6, “Liquid Detection”).
Regarding claim 8, the apparatus comprises a base (liquid container), a sound outputting section (speaker) provided on the base for emitting sound; a sound receiving section (microphone) provided on the base and set opposite the sound outputting section, between the sound receiving section and the sound outputting section a liquid to be tested is placed, the sound receiving section is configured to receive sound waves penetrating the liquid to be tested; and a control computing component, the control computing component electrically connected to the sound outputting section and the sound receiving section (See Pg. 1, Para. 1, “Introduction” and Pg. 8, Para. 4.1, “Hardware”).
Regarding claim 10, the control computing assembly comprises a display and control panel, the display and control panel is provided on the base; a computing unit, the computing unit is provided on the base and electrically connected to the display and control panel (See Pg. 8, Para. 4.1, “Hardware”).
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.6. The following is a statement of reasons for the indication of allowable subject matter: The primary reasons for indicating allowable subject matter is that the prior art does not anticipate or make obvious the provisions of “the base comprises a base plate; a movable section, the movable section is movably provided on the base plate in a first predetermined direction by a slide guide assembly, the sound receiving section is provided on the movable section; a fixed section, the fixed section is fixedly provided on the base plate and spaced apart from the movable section, the sound outputting section is provided on the fixed section; and an adjustment limit member, the adjustment limit member is configured to fix the movable section after position adjustment” in combination with the other limitations presented in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.8. Fang et al. (CN113984741) disclose a liquid identification system and method based on deep learning and sound signal. Thiruvenkatanathan (CN113330185) discloses event detection using machine learning using das characteristics. Panchalan et al. (20210262963) disclose a graphene based chemical sensing device and system. Muldoon et al. (10,705,017) disclose characterization of liquids in sealed containers. Effelsberg et al. (DE102020108063) disclose acquisition of information in containers with liquid medium by means of acoustic waves from the environment.
Guo et al. (CN103487506) disclose a portable liquid food quality ultrasonic wave detector and method. Mifsud et al. (WO9839648) disclose an apparatus and method for characterizing liquids.
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/OCTAVIA HOLLINGTON/Primary Examiner, Art Unit 2855 2/17/26
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