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 .
Applicant’s election without traverse of claims 1-8 and 14-19 in the reply filed on 09/03/2026 is acknowledged.
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.
Claims 1-8, 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (Deng) (US 2024/0320909 A1) in view of Zhou et al. (Zhou) (US 11,393,229 B2).
Regarding claim 1, Deng discloses an image segmentation method performed by an image segmentation apparatus (e.g., Systems and techniques are described for generating one or more three-dimensional models. According to at least one example, a method is provided for generating one or more three-dimensional models. The method includes: obtaining a plurality of images of an object; obtaining a plurality of segmentation masks associated with the plurality of images, paragraph 3, …909), the image segmentation method comprising:
obtaining a plurality of probability maps by inputting an image into a deep [earning model (e.g., training, using the plurality of images and the plurality of segmentation masks, a machine-learning model to generate one or more semantically-labeled three-dimensional models of the object, paragraph 3); and
identifying a plurality of objects from the image, based on the plurality of probability maps (e.g., generating using the trained machine-learning model, a semantically-labeled three-dimensional model of the object, the semantically-labeled three-dimensional model of the object including at least one label indicative of the at least one segment of the object, paragraph 3).
Deng does not specifically disclose wherein the plurality of probability maps comprises prediction values of the deep learning model indicating a probability that each pixel of the image belongs to a plurality of combinations defined as regions of at least two objects among the plurality of objects.
Zhou discloses wherein the plurality of probability maps comprises prediction values of the deep learning model indicating a probability that each pixel of the image belongs to a plurality of combinations defined as regions of at least two objects among the plurality of objects (e.g., at step 304, one or more priors are generated for each of the training images. According to an advantageous embodiment, distance maps are generated for each of the training images and used as implicit shape priors. The distance map for each training image is generated using the corresponding ground truth segmentation mask. In particular, for a given training image, a distance map is generated by assigning an intensity value for each pixel/voxel corresponding to a distance from that pixel/voxel to a nearest point on the target anatomical structure. Other priors may be generated as well for each training image, paragraph 52).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Deng to include wherein the plurality of probability maps comprises prediction values of the deep learning model indicating a probability that each pixel of the image belongs to a plurality of combinations defined as regions of at least two objects among the plurality of objects as taught by Zhou. It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Deng by the teaching of Zhou to use for a particular application.
Regarding claim 2, Zhou discloses wherein the image is a two-dimensional (2D) medical image or a three-dimensional (3D) medical image, and the plurality of objects are a plurality of human body parts (e.g., FIG. 2 illustrates a method for intelligent autonomous medical image segmentation according to an embodiment of the present invention. Referring to FIG. 2, at step 202, a medical image of a patient is received. The medical image can be a 2D image, 3D image, or 4D (3D+time) image, paragraph 42).
Regarding claim 3, Deng discloses wherein an identifier of each object is defined as a bit string of a certain length, each combination includes regions of objects with a same bit value, based on each digit in the bit string of each object, and the identifying includes identifying the identifier of the object corresponding to the bit string generated by binarizing, based on a predefined threshold value, the probability of each pixel belonging to each combination (e.g., systems and techniques may obtain a number of two-dimensional images (e.g., RGB images, monochrome or grayscale images, etc.) of an object. The systems and techniques may further obtain a number of segmentation masks. Each of the segmentation masks is associated with one of the two-dimensional images of the object. Each of the segmentation masks may include at least one label indicative of at least one segment of the object, paragraph 27).
Regarding claim 4, Deng discloses further comprising segmenting regions of the plurality of objects in the image by using pixels corresponding to each identifier (e.g., a segmentation mask may include a value corresponding to each pixel of an image that is associated with the segmentation mask. Each value of the segmentation mask may indicate a label of the corresponding pixel of the associated image, paragraph 27).
Regarding claim 5, Deng discloses wherein the length of the bit string is greater than or equal to [log2 (the total number of the plurality of objects)] (e.g., the systems and techniques may obtain an image of a person's head Using a trained machine-learning model, the systems and techniques may segment the image to generate a segmentation mask. The segmentation mask may include a value for each pixel in the image. The values of the segmentation mask may indicate which pixels of the image are labeled as “hair,” “eyes,” “nose,” etc, paragraph 27).
Regarding claim 6, Deng discloses wherein the number of probability maps output by the deep learning model is greater than or equal to [log2 (the total number of the plurality of objects)] (e.g., the systems and techniques may obtain an image of a person's head Using a trained machine-learning model, the systems and techniques may segment the image to generate a segmentation mask. The segmentation mask may include a value for each pixel in the image. The values of the segmentation mask may indicate which pixels of the image are labeled as “hair,” “eyes,” “nose,” etc, paragraph 27)
Regarding claim 7, Deng discloses further comprising: receiving a segmentation target object in the image by selection by a user; and visualizing and providing a result of segmentation of an object region corresponding to an object to be segmented among the plurality of objects (e.g., to enable user interaction, computing system 900 includes an input device 922, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 900 can also include output device 924, which can be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 900, paragraph 96).
Regarding claim 8, Deng discloses further comprising training the deep learning model to output the plurality of probability maps indicating the probability that each pixel of the training image belongs to each combination by using training data including a training image and a true mask for each combination (e.g., Each of the segmentation masks is associated with one of the two-dimensional images of the object. Each of the segmentation masks may include at least one label indicative of at least one segment of the object. For example, a segmentation mask may include a value corresponding to each pixel of an image that is associated with the segmentation mask. Each value of the segmentation mask may indicate a label of the corresponding pixel of the associated image. For example, the systems and techniques may obtain an image of a person's head Using a trained machine-learning model, the systems and techniques may segment the image to generate a segmentation mask. The segmentation mask may include a value for each pixel in the image. The values of the segmentation mask may indicate which pixels of the image are labeled as “hair,” “eyes,” “nose,” etc, paragraph 27).
Regarding claim 14, claim 14 is an image segmentation apparatus with limitations similar of limitations of claim 1. Therefore, claim 14 is rejected as set forth above as claim 1.
Regarding claim 15, claim 15 is an image segmentation apparatus with limitations similar of limitations of claim 3. Therefore, claim 15 is rejected as set forth above as claim 3.
Regarding claim 16, Deng discloses further comprising a learning unit configured to train the deep learning model, wherein the learning unit comprises:
a mask definition unit configured to define a plurality of true masks obtained by combining regions of at least two objects among the plurality of objects in a training image (e.g., The systems and techniques may further obtain a number of segmentation masks. Each of the segmentation masks is associated with one of the two-dimensional images of the object. Each of the segmentation masks may include at least one label indicative of at least one segment of the object, paragraph 27);
a map acquisition unit configured to acquire the plurality of probability maps indicating a probability that each pixel of the training image belongs to each true mask, by inputting the training image into the deep learning model (e.g., The mapping from the input layer to the convolutional hidden layer 804 is referred to as an activation map (or feature map). The activation map includes a value for each node representing the filter results at each locations of the input volume. The activation map can include an array that includes the various total sum values resulting from each iteration of the filter on the input volume. For example, the activation map will include a 24×24 array if a 5×5 filter is applied to each pixel (a stride of 1) of a 28×28 input image, paragraph 83); and
a training unit configured to train the deep learning model to minimize a value of a loss function indicating a difference between the plurality of true masks and the plurality of probability maps (e.g., paragraph 46).
Regarding claim 17, Deng discloses wherein the mask definition unit defines identifiers of the plurality of objects as bit strings of a predetermined length and generates a true mask including regions of objects having a same bit value for each digit of the bit string (e.g., segmentation masks 106 may include multiple segmentation masks 106 indicative of segments of the object. Each of segmentation masks 106 may be associated with one of images 104. For example, a given segmentation mask 106 may include a value corresponding to each pixel of an associated image 104. The values may be indicative of a label of the corresponding pixel. For example, the depicted image 104 may include 512-by-512 RGB pixels (e.g., 512-by-512 values indicative of color). The depicted segmentation mask 106 may include values 512*512 values (e.g., which may be arranged in a 512-by-512 grid for case of illustration). The values of the segmentation mask 106 may be indicative of labels of corresponding pixels, paragraph 33).
Regarding claim 18, Deng discloses further comprising an output unit configured to, when a segmentation target object is selected by a user, visually provide a result obtained by segmenting a region of an object corresponding to the segmentation target object in the image (e.g., to enable user interaction, computing system 900 includes an input device 922, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 900 can also include output device 924, which can be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 900. Computing system 900 can include communication interface 926, which can generally govern and manage the user input and system output…, paragraph 96).
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.
Claim 19 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 19 is a computer-readable recording medium on which a computer program is a transitory computer-readable recording medium.
Conclusion
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/QUANG N VO/Primary Examiner, Art Unit 2683
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