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 .
Summary
Claims 1, 2, 4, 5, and 8-13 are pending. Claims 1, 2, 4, 5, and 8-13 are rejected herein. This is a Non-Final Rejection after the Request for Continued Examination dated 29 June 2026 to enter the amendment and arguments (hereinafter “the Response”) dated 15 June 2026.
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.
Claim(s) 1, 2, 5, and 8-13 is/are rejected under 35 U.S.C. 102(a1) as being anticipated by CUNNAH et al. (US 20220299354).
Regarding claim 1: CUNNAH discloses: A method for determining a fill level of a liquid in a container, said method comprising the steps of: attaching a sensor device (105, 107 in FIG. 1) to the container (30; “in contact” in para. 80); providing to a computer processor (110) via a communication interface (all arrows into and out of computing device 110 in FIG. 1) a digital representation of the container (“features” of container in para. 154); wherein the digital representation of the container comprises data selected from at least one of: data on the filling volume of the container, data on the content of the container, data on the initial filling level, filling date (para. 13, 20), data on the location of the container (para. 13, 15), data on the age of the container (para. 154), data on the use cycle of the container (para. 15), data on the maintenance intervals of the container, data on the maximum life time of the container, expiry date of container content, and any combination thereof; acoustically stimulating (by speaker 105) the container by means of the sensor device to generate at least one audio signal being indicative of the fill level of the liquid in the container (para. 80-81); detecting the at least one generated audio signal by at least one microphone of the sensor device resulting from the acoustic stimulation of the container (by microphone 107); processing the at least one detected audio signal; said processing comprising: digitally sampling with the computer processor the at least one audio signal, calculating with the computer processor a Fourier spectrum of the at least one digital audio sample (conversion from the time domain to the frequency domain in para. 54), and extracting at least one predefined feature from the Fourier spectrum (Averaging of acoustic profiles, which are in the frequency domain, is discussed in para. 128.); selecting with the computer processor at least one data driven model (algorithm 150 in para. 82) based on the provided digital representation of the container (choosing between different models based on whether the container is in use in para. 156; choosing the model based on the type of container or its contents in para. 38, 53, 85), parameterized on historical audio signals, historical fill levels of liquids in containers and historical digital representations of the containers (training on data from many containers in para. 101); determining, with the computer processor, the fill level of the liquid in the container based on the provided digital representation of the container, the at least one feature extracted from the Fourier spectrum (para. 28), and the at least one selected data driven model (output as an indication of the fill level of the container in para. 123); and providing via the communication interface the determined fill level of the liquid in the container (step B6 in FIG. 3).
Regarding claim 2: CUNNAH discloses: the fill level of the liquid in the container is a classifier corresponding to the container being empty or the container not being empty (para. 125).
Regarding claim 5: CUNNAH discloses: acoustically stimulating the container to generate at least one audio signal being indicative of the fill level of the liquid in the container includes beating on the outer wall of the container by means of the actuator (105 in FIG. 1) of the sensor device to induce the at least one audio signal (para. 80-81).
Regarding claim 8: CUNNAH discloses: the audio samples are aligned prior to calculating a Fourier spectrum of the at least one digital audio sample (temporal normalization as described in para. 96).
Regarding claim 9: CUNNAH discloses: the at least one predefined feature is selected from the group consisting of frequency with the highest energy, the normalized average frequency (Averaging of acoustic profiles, which are in the frequency domain, is discussed in para. 128.), the normalized median frequency, the standard deviation of the frequency distribution, the skew of the frequency distribution the deviation of the frequency distribution from the average or median frequency in different L spaces, the spectral flatness, the normalized root-mean-square, fill-level specific audio coefficients, the fundamental frequency computed by the yin algorithm, the normalized spectral flux between two consecutive frames and any combinations thereof.
Regarding claim 10: CUNNAH discloses: the at least one selected data driven model is derived from a trained machine learning algorithm (para. 121).
Regarding claim 11: CUNNAH discloses: The method according to claim 10, wherein the machine learning algorithm is trained by selecting inputs and outputs to define an internal structure of the machine learning algorithm, applying a collection of input and output data samples to train the machine learning algorithm, verifying the accuracy of the machine learning algorithm by applying input data samples of known fill levels and comparing the produced output values with expected output values, and modifying the parameters of the machine learning algorithm using an optimization algorithm in case the received output values are not corresponding to the known fill level (para. 121, 147-149).
Regarding claim 12: CUNNAH discloses: providing the determined fill level of the liquid in the container via the communication interface comprises displaying the determined fill level of the liquid of the container on the screen of a display device (“Remote monitoring by customers” as discussed in para. 140 inherently involves displaying data on a screen.).
Regarding claim 13: CUNNAH discloses: A system (FIG. 1) for determining a fill level of a liquid in a container, comprising: a container (30); a sensor device (para. 105, 107) for generating and detecting at least one audio signal being indicative of the fill level of the liquid in the container (para. 80); a data storage medium (what processor 110 uses) storing a plurality of data driven models parameterized on historical audio signals (para. 101; multiple models are discussed in para. 48, 53, 85), historical fill levels of liquids in containers and historical digital representations of the containers (para. 154); a communication interface (120) for providing the digital representation of the container (para. 154), and the at least one detected audio signal to the computer processor (para. 81-82); wherein the digital representation of the container comprises data selected from at least one of: data on the filling volume of the container, data on the content of the container (para. 53), data on the initial filling level, filling date (para. 13, 20), data on the location of the container (para. 13, 15), data on the age of the container (para. 154), data on the use cycle of the container (para. 15), data on the maintenance intervals of the container, data on the maximum life time of the container, expiry date of container content, and any combination thereof; a computer processor (CP) (110 and where 150 is stored) in communication with the communication interface and the data storage medium, the computer processor programmed to: receive via the communication interface the digital representation of the container and the at least one detected audio signal (para. 81-82); process the at least one received audio signal by digitally sampling the at least one received audio signal (para. 80-81), calculating a Fourier spectrum of the at least one digital audio sample (conversion from the time domain to the frequency domain in para. 54), and extracting at least one predefined feature from the calculated Fourier spectrum (Averaging of acoustic profiles, which are in the frequency domain, is discussed in para. 128); select at least one data driven model stored in the data storage medium based on the provided digital representation of the container (choosing between different models based on whether the container is in use in para. 156; choosing the model based on the type of container or its contents in para. 38, 53, 85) determine the fill level of the liquid in the container based on the received digital representation of the container, the at least one feature extracted from the Fourier spectrum (para. 28), and the at least one selected data driven model (para. 123); and provide the determined fill level of the liquid in the container via the communication interface (para. 140).
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) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over CUNNAH in view of LAVRA et al. (US 20160098673).
Regarding claim 4: As best understood, CUNNAH does not disclose storing information on an attached tag.
LAVRA however does teach attaching an identification tag to the container (abstract), retrieving the digital representation of the container stored on said attached tag or obtaining the digital representation of the container based on the information stored on said attached tag (“inventory interrogation and data collection means” in para. 11), and providing the obtained digital representation of the container to the computer processor (control unit in para. 7) via the communication interface. Please note that the RFID tag of LAVRA uniquely identifies the container and provides information about the container such as volume, gas composition, pressure etc. (para. 12).
One skilled in the art at the time the application was effectively filed would be motivated to use the information storing tag of LAVRA on the containers of CUNNAH so that information can be determined about a container, quickly and easily, at point of use, even if the container is misplaced.
Response to Amendment/Arguments
Please note that page 8 of the Response states that claims 1-4 and 8-13 are pending and under examination, however the current claim set has claims 1, 2, 4, 5, and 8-13 under examination.
The replacement drawings are acknowledged and the previous objections thereto are accordingly withdrawn.
The amendment to claim 9 to overcome the previous objection is acknowledged and said objection is accordingly withdrawn.
The amendments to the claims to overcome the previous rejections under 35 U.S.C. 112(b) are acknowledged and said rejections are accordingly withdrawn.
The Applicant has argued (page 10 of the Response) that CUNNAH does not disclose selecting a data driven model based on a digital representation of the container where the digital representation comprises data selected from at least one of all the parameters listed in former section (ii) of claim 1. This argument has been fully considered and is not persuasive. The Applicant states “Cunnah is silent regarding using a digital representation of a container to select at least one data driven model to be employed in determining a fill level of that container. At most, Cunnah describes using ‘features’ of cylinders for training models.” The Examiner respectfully disagrees, CUNNAH discloses using an in-use flag to chose between different models in para. 156. CUNNAH also states that different types of containers will have different models (para. 53, 85). CUNNAH also discloses that the model can depend on the contents of the container (para. 38). These features will be part of the digital representation of the container, therefore CUNNAH discloses selecting the data model based on the digital representation of the container.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHANIEL J KOLB whose telephone number is (571)270-7601. The examiner can normally be reached M-F 9-5 EST.
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/NATHANIEL J KOLB/Examiner, Art Unit 2855
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