Detailed Action
1. Claims 1-13 are pending in this Application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The Examiner Note
3. The Examiner attempted to contact Applicant’s representative Attorney David Schreiber (Reg. 35,672) on 9/10/2026 and 10/10/2026 to propose an Examiner’s Amendment that would place the application in condition for allowance. Because no response or return call was received, the merits of the application are further considered, and this Non-Final Office Action is being issued.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claim 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim10 as a whole define “ A computer program comprising code means for implementing the method of …..”. The specification in paragraphs [0013]-[ .[0104] states “The memory 520 can include any one or combination of volatile memory elements (e.g.,random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), disk, diskette, cartridge, cassette or the like, etc.).”. This statement of the specification may include transitory and non- transitory propagation signals, and “a transitory, propagating signal … is not a “process, machine, manufacture, or composition of matter.” Those four categories define the explicit scope and reach of subject matter patentable under 35 U.S.C. § 101; thus, such a signal cannot be patentable subject matter.” Therefore, the examiner suggests: Amending the claim to embody the program on “non- transitory computer-readable storage medium” storing a program or equivalent that excludes computer readable medium as a “signal”, “carrier wave”, or “transmission medium” which are deemed non-statutory.
Any amendment to the claim should be commensurate with its corresponding disclosure
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claim 1, 11 and 12 is rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. The reasons of the rejection are discussed as follow:
Specifically claims 1, 11 and 12 recites the limitations " the first CNN” in line 13 , 9 and 13 of the claims respectively. There is insufficient antecedent basis for these limitations in the claims.
Allowable Subject Matter
6. Claims 1, 11 and 12 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112, 2nd paragraph, set forth in this Office action.
7. Claims 2-9 and 13 are objected to as being dependent upon a rejected base claims1, 11 and 12, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
8. Regarding independent claims 1, 11 and 12 no prior art is found to anticipate or render the flowing limitation obvious:
“wherein the pixel values in the background portions of the input image have been modified; converting the background-modified image to a heatmap, wherein the value of a pixel in the heatmap describes a pixel intensity value change of the pixel with respect to its neighboring pixels; and processing the converted background-modified image with a machine-learning algorithm to obtain a prediction of the volume of liquid in the at least partially transparent container by providing the converted background-modified image as an input to a convolutional neural network, CNN, the first CNN being trained to predict, for the converted background-modified image, the volume of liquid in the at least partially transparent container.”
Prior arts are not used in rejections but pertinent to the claims or disclosure.
a. “METHOD FOR DETERMINING REMAINING FLUID LEVEL OF OPEN CONTAINER” US 20210270662 A1, pub. 09/02/2021, by Grimm et al. disclosed:
A method for estimating a volume of liquid in an at least partially transparent container, the method comprising:(Fig. 1, [0018], , [0076]: "a method of contactless estimation of a volume of liquid in a real-world container" "where a transparent or semi- transparent container is being profiled")
processing an input image of an at least partially transparent container containing liquid with an image background removal algorithm to obtain a background-modified image, the background-modified image being a modified version of the input image wherein the pixel values in the background portions of the input image have been modified (Fig. 1, refs. 104, [0051]: "remove background", 106: "Replace/Fill background with high-contrast color" "removing the background (Block 104) (or isolating the container from the background). The background can then be replaced or filled with a high contrast color (e.g., black, bright blue, or bright green) (Block 106)")
converting the background (Fig. 1, refs. 108, 110, [0051]: "the container image can be hidden by removing the image of the container (Block 108), or converting these non- background pixels to a common type (e.g., to all white pixels)" & [0052]: Non-liquid parts of the masked container can then be filled with the high-contrast background color (e.g., black) (Block 110)’);
b. “ The Levelshred Method: A Solution to Fluid Level Detection in Partially-Obstructed Containers” IEEE Xplore, pub., 29 December 202111/15/2022, Zachary Preston et al.,
Dept. of Mechatronics Engineering, University of Canterbury, Christchurch, New Zealand
disclosed:
Uses luminosity-variance methods to find the image rows with the highest density of vertical intensity change. Obstruction. Overcomes visual barriers that block even when partial obstructions hide up to 70% of the container width. Successfully detected fluid levels within a ±2% tolerance in 86.4% of all tested containers. Achieved higher success rates of 91.7% for coloured fluids and 93.3% for coloured surfaces.
c. “Visual Closed-Loop Control for Pouring Liquids:, 2017 IEEE International Conference on Robotics and Automation, by Connor Schenck et al., disclosed:
Given color images, we train a convolutional neural network (CNN) to label each pixel as liquid or not-liquid (we use the thermal camera to acquire the ground truth labeling). The network is fully-convolutional, that is, all the learned layers are convolution layers with no fully-connected layers. The output of the network is a heatmap over the image, with real values in the range [0, 1], where higher values indicate a higher likelihood of liquid. In [7] we tested 3 network structures and found that a recurrent network utilizing a long short-term memory (LSTM) layer [20] performs the best of the 3. Here we use the LSTM-CNN from that paper, which is shown in the top row of Figure 3. We refer the reader to [7] for more details ( see Section III B. Pixel-Wise Liquid Detection)
Contact Information
Any inquiry concerning this communication or earlier communication from the examiner should be directed to Mekonen Bekele whose telephone number is (469) 295-9077.The examiner can normally be reached on Monday -Friday from 9:00AM to 6:50 PM Eastern Time.
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/MEKONEN T BEKELE/Primary Examiner, Art Unit 2699
