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 .
Status of claims: claims 1-19 and 21 are examined below. Claims 20 and 22-30 are canceled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/26/2024 and 4/15/2025 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 10, 11-13 and 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by YOO et al (US 2018/0373924)
Claim 1:
YOO et al (US 2018/0373924) anticipated the following subject matter:
A system, comprising: one or more data processors (figure 10-11 teaches system, device, processors and memory); and
a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including (0024-0025 teaches non-transitory storage medium with processor):
receiving medical imaging data containing a plurality of ordered image slices (0003 teaches images (slices) that can be static or moving of facial (biological and medical use); 0136 detail moving images to be such as vibration which one ordinary skill in the art view as sequence images with vibration);
accessing a multi-branch model associated with a target (0131 teaches neural network that is configured to be in parallel (multi-branch) using convolutional neural networks), wherein the multi-branch model includes a main branch and an attention branch, wherein the main branch includes a densely connected convolutional network (DenseNet) (figure 11 and 0131 detail a configuration where one branch is a dense convolutional neural network), wherein the attention branch includes a convolutional long short-term memory network (ConvLSTM) (figure 11 and 0131 further teaches the parallel configuration of the convolutional neural network to be long short-term memory), wherein the multi-branch model is trained to receive sequential image slices (paragraph 0131 teaches training of these neural networks, where paragraph 0003 detail images that are moving (sequential images frames/slices))and, for each sequential image slice, output a target mask indicative of one or more target regions within the sequential image slice identified as being the target (paragraph 0004 teaches images with biometric that focuses on facial (target), where paragraph 0005-0009 detail the face area to be the target region of the moving images/slices, where feature values, determined criterion, registration vales of patches, and occlusion regions are outputted);
providing the plurality of ordered image slices to the multi-branch model (0154 detail processing of these images/slice from the branches output images (plurality of images) in order with adjust parameters from the extracted features); and
generating, by the multi-branch model, a plurality of output target masks in response to providing the plurality of ordered image slices to the multi-branch model (0005 teaches obtained from input image generate a plurality of image patches from the plurality of images, where the patches are portions of the detected region of interest (face area as the target area) with each patch due to a set of determined criterion; 0011 teaches generating plurality of patches base on set composition and predefined composition and positions. On ordinary in the art would view the specialized patch with criterion define a filter/mask).
Claim 2:
The system of claim 1, wherein the operations further comprise generating a quantitative score using the plurality of output target masks, wherein the quantitative score is indicative of a severity of a condition associated with the target (0104 teaches determine where the feature satisfies a preset condition, where above detail the feature values, determined criterion, registration vales of patches which are view as the preset conditions).
Claim 3:
The system of claim 2, wherein generating the quantitative score includes: calculating a total target volume using the plurality of output target masks; and generating a quantitative score using the total target volume (005-0009 detail the face area to be the target region (area/volume) of the moving images/slices, where feature values, determined criterion, registration vales (all are quantitative values/score) of patches).
Claim 10:
The system of claim 1, wherein the operations further comprise:
presenting the medical imaging data using a display device, wherein presenting the medical imaging data using the display device includes applying a visually distinguishable feature to the medical imaging data based on the plurality of output target masks such that the one or more target regions visible in the medical imaging data are visually distinguishable from other regions within the medical imaging data (0066 teaches display from the computing apparatus of the image in difference in center area and alignment of the face; 0148 teaches display of the captured face area for visually feedback which mean visually distinguishable feature of the face region (target mask and target region)).
Claim 11:
YOO et al (US 2018/0373924) anticipated the following subject matter:
A computer-implemented method, comprising:
receiving medical imaging data containing a plurality of ordered image slices (0003 teaches images (slices) that can be static or moving of facial (biological and medical use); 0136 detail moving images to be such as vibration which one ordinary skill in the art view as sequence images with vibration);
accessing a multi-branch model associated with a target (0131 teaches neural network that is configured to be in parallel (multi-branch) using convolutional neural networks), wherein the multi-branch model includes a main branch and an attention branch, wherein the main branch includes a densely connected convolutional network (DenseNet) (figure 11 and 0131 detail a configuration where one branch is a dense convolutional neural network), wherein the attention branch includes a convolutional long short-term memory network (ConvLSTM) (figure 11 and 0131 further teaches the parallel configuration of the convolutional neural network to be long short-term memory), wherein the multi-branch model is trained to receive sequential image slices (paragraph 0131 teaches training of these neural networks, where paragraph 0003 detail images that are moving (sequential images frames/slices)) and, for each sequential image slice, output a target mask indicative of one or more target regions within the sequential image slice identified as being the target (paragraph 0004 teaches images with biometric that focuses on facial (target), where paragraph 0005-0009 detail the face area to be the target region of the moving images/slices, where feature values, determined criterion, registration vales of patches, and occlusion regions are outputted);
providing the plurality of ordered image slices to the multi-branch model (0154 detail processing of these images/slice from the branches output images (plurality of images) in order with adjust parameters from the extracted features); and
generating, by the multi-branch model, a plurality of output target masks in response to providing the plurality of ordered image slices to the multi-branch model (0005 teaches obtained from input image generate a plurality of image patches from the plurality of images, where the patches are portions of the detected region of interest (face area as the target area) with each patch due to a set of determined criterion; 0011 teaches generating plurality of patches base on set composition and predefined composition and positions. On ordinary in the art would view the specialized patch with criterion define a filter/mask).
Claim 12:
The computer-implemented method of claim 11, further comprising generating a quantitative score using the plurality of output target masks, wherein the quantitative score is indicative of a severity of a condition associated with the target (0104 teaches determine where the feature satisfies a preset condition, where above detail the feature values, determined criterion, registration vales of patches which are view as the preset conditions).
Claim 13:
The computer-implemented method of claim 12, wherein generating the quantitative score includes:calculating a total target volume using the plurality of output target masks; and generating a quantitative score using the total target volume (0005-0009 detail the face area to be the target region (area/volume) of the moving images/slices, where feature values, determined criterion, registration vales (all are quantitative values/score) of patches).
Claim 21:
YOO et al (US 2018/0373924) anticipated the following subject matter:
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations including (0024-0025 teaches non-transitory storage medium with processor):
receiving medical imaging data containing a plurality of ordered image slices (0003 teaches images (slices) that can be static or moving of facial (biological and medical use); 0136 detail moving images to be such as vibration which one ordinary skill in the art view as sequence images with vibration);
accessing a multi-branch model associated with a target (0131 teaches neural network that is configured to be in parallel (multi-branch) using convolutional neural networks), wherein the multi-branch model includes a main branch and an attention branch, wherein the main branch includes a densely connected convolutional network (DenseNet) (figure 11 and 0131 detail a configuration where one branch is a dense convolutional neural network), wherein the attention branch includes a convolutional long short-term memory network (ConvLSTM) (figure 11 and 0131 further teaches the parallel configuration of the convolutional neural network to be long short-term memory), wherein the multi-branch model is trained to receive sequential image slices (paragraph 0131 teaches training of these neural networks, where paragraph 0003 detail images that are moving (sequential images frames/slices)) and, for each sequential image slice, output a target mask indicative of one or more target regions within the sequential image slice identified as being the target (paragraph 0004 teaches images with biometric that focuses on facial (target), where paragraph 0005-0009 detail the face area to be the target region of the moving images/slices, where feature values, determined criterion, registration vales of patches, and occlusion regions are outputted);
providing the plurality of ordered image slices to the multi-branch model (0154 detail processing of these images/slice from the branches output images (plurality of images) in order with adjust parameters from the extracted features); and
generating, by the multi-branch model, a plurality of output target masks in response to providing the plurality of ordered image slices to the multi-branch model (0005 teaches obtained from input image generate a plurality of image patches from the plurality of images, where the patches are portions of the detected region of interest (face area as the target area) with each patch due to a set of determined criterion; 0011 teaches generating plurality of patches base on set composition and predefined composition and positions. On ordinary in the art would view the specialized patch with criterion define a filter/mask).
Allowable Subject Matter
Claim 4 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 4.
Claim 5 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 5.
Claim 6 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 6.
Claim 7 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 7.
Claim 8 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 8.
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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 9.
Claim 14 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 14.
Claim 15 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 15.
Claim 16 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 16.
Claim 17 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 17.
Claim 18 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 18.
Claim 19 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. At the time of the examination unable to find claim limitations, integrating elements, concept or language alone or in combination of claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Evans et al (Us 2021/039867) teaches MACHINE LEARNING ALGORITHMS FOR DETECTING MEDICAL CONDITIONS, RELATED SYSTEMS, AND RELATED METHODS
AYKUT et al (US 2023/0196567) teaches SYSTEMS, DEVICES, AND METHODS FOR VITAL SIGN MONITORING – paragraph 0312 teach generate filter, 0091 and figure 11A teach CNN such as DenseNet and LSTM where 0108 and figure 3 detail parallel
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656