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 .
Election/Restrictions
In response to Election/Restrictions of 06/10/2026, the applicant elected group I with traverse. In further consideration of the applicant’s arguments, the requirement is withdrawn. Claims 1-20 are considered for examination.
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, 12-13, 16-20 rejected under 35 U.S.C. 102 (a) (2) as being anticipated by Aladahalli [US 20210106314 A1].
As per claim 1, Aladahalli teaches an ultrasound imaging system (Aladahalli Fig 1), comprising:
a processor circuit (Aladahalli Fig 1 item 116) configured to:
store, in a memory in communication with the processor circuit (Aladahalli Fig 1 item 120, Fig 6 step 602 requires storing in memory),
a target parameter representative of a target anatomical scan window (Aladahalli Fig 6 step 602 requires storing in memory target context awareness graph. ¶0043 “The context awareness graph may comprise graphical marks such as geometrical shapes (e.g. circles, points, lines, boxes, and other geometric shapes) overlaid on ultrasound … both relative size and relative orientations or relative positions of the one or more internal features” Hence the context awareness graph (herein referred to as CAG) comprises parameters. Target anatomical scan window implied);
receive a first ultrasound image acquired by a first ultrasound probe with a first anatomical scan window during a first acquisition period (Aladahalli Fig 6 step 604, a first anatomical scan window implied);
determine a first parameter representative of the first anatomical scan window (Aladahalli Fig 6 step 606, CAG for current scan);
retrieve the target parameter from the memory, compare the target parameter and the first parameter (Aladahalli Fig 6, step 610, ¶0111 “This includes comparing the target context awareness graph (generated at 602) and the current context awareness graph (generated at 606)”); and
output a visual representation of the comparison to a display in communication with the processor circuit (Aladahalli Fig 6 step 612).
As per claim 12, Aladahalli further teaches wherein the processor circuit is configured to: receive a user input selecting a target anatomical scan window; and determine the target parameter based on the user input (Aladahalli ¶0027 “Example initial and target scan images are shown at FIG. 8A.” Some kind of user input regarding scan window, is inherently required to generate this image using a probe. Further ¶0071 “the target scan image may be acquired based on user selection and/or input. For example, the user may enter one or more of a desired body … input regarding the imaging protocol”).
As per claim 13, wherein the processor circuit comprises a preprocessor (In view of applicant spec. examiner interprets as perform various functions before transmitting the input images to the deep learning network. Aladahalli ¶0031 “the processor 116 may include other electronic components capable of carrying out processing functions, such as a digital signal processor, a field-programmable gate array (FPGA), or a graphic board. In some embodiments, the processor 116 may be configured as graphical processing unit with parallel processing capabilities…”) and at least one deep learning network (Aladahalli Fig 3, ¶0009).
As per claim 16, Aladahalli further teaches wherein the deep learning network is configured to determine the first parameter (Aladahalli Fig 3 output of DNN is input to the CAG module 209).
As per claim 17, Aladahalli further teaches wherein the deep learning network comprises a convolutional neural network (CNN) (Aladahalli ¶0027 “The segmentation model may a convolution neural network (CNN), such as the CNN shown in FIG. 3”).
As per claim 18, Aladahalli further teaches wherein the visual representation of the comparison comprises: a first indicator when the target parameter and the first parameter are the same (Aladahalli Fig 6 step 612); and a second indicator when the target parameter and the first parameter are different (Aladahalli ¶0114 “method 600 may include, at 616, indicating to the user, via the user interface, that the target scan plane is not achieved”).
As per claim 19, Aladahalli further teaches wherein the processor circuit is configured to: store, in the memory, a second patient physiological condition during the second acquisition period (Aladahalli ¶0108 “ the target scan image may include a right lobe of the liver and a right kidney in a sagittal plane, where the right kidney is positioned at an obtuse angle with respect to the liver.”, anatomical condition of kidney is stored); receive a first patient physiological condition during the first acquisition period; retrieve the second patient physiological condition from the memory (Aladahalli ¶0109 “s, the current scan image may include three internal features, liver, right kidney, and bowel in contrast to the liver and right kidney features desired (target scan image). Thus, the current context awareness graph includes the first node depicting the kidney,”); compare the second patient physiological condition and the first patient physiological condition; and output a visual representation of the comparison between the second patient physiological condition and the first patient physiological condition to the display (Aladahalli Fig 6 step 610, ¶0112 “, if the current context awareness graph shows three nodes and the target context awareness graph shows only two nodes, it may be determined that…” item 612 is indication of comparison).
As per claim 20, it has limitations similar to claim 18 and are rejected for same reasons as above.
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 2-8 are rejected under 35 U.S.C. 103 as being unpatentable over Aladahalli.
As per claim 2, Aladahalli further teaches wherein the target parameter comprises the anatomical scan window of Aladahalli further teaches Fig 6 step 602 is before 604).
Aladahalli does not expressly recite whether or not the target images / parameters are obtained using a second ultrasound probe (interpreted here as one different from first one). However, this is an obvious modification for the following reason. Aladahalli only requires using predetermined target scan images stored in non-transitory memory. The target scan image may be selected from a predetermined set of target scan images based on one or more of a target scan plane, one or more desired internal features to be examined, and a desired condition to be evaluated (See Aladahalli ¶0130 for example). Before the effective filing of the claimed invention, there were only two possibilities, either use same probe as current can or a different probe. Irrespective of whether a same or different probe is used, as long as target scan provides this information, and target CAGs may be generated, the system of Aladahalli may be implemented. Aladahalli also discusses receiving training data sets from ultrasound image data 212, or from sources other than ultrasound image data 212, such as other image processing systems, the cloud, etc.. (Aladahalli ¶0042). Hence using different probes to generating predetermined scans / data using second would have obvious if such other data was to be acquired from different locations, databases, cloud, or scanning sessions.
As per claim 3, Aladahalli further teaches wherein the processor circuit is configured to: store the second ultrasound image in the memory such that the target parameter is associated with the second ultrasound image in the memory (Aladahalli ¶0105 “At 602, method 600 includes acquiring the target scan image and generating target context awareness graph for the acquired target scan image” CAG is associated with target scan image).
As per claim 4, Aladahalli further teaches wherein the processor circuit is configured to: receive the second ultrasound image obtained by the second ultrasound probe; determine the target parameter representative of the second anatomical scan window; and associate the target parameter and the second ultrasound image (Aladahalli Figs 5-6 receiving determination and associating is implied from the steps of Figs 5-6).
As per claim 5, Aladahalli further teaches wherein the processor circuit is configured to: associate the first parameter and the first ultrasound image; store the first parameter and the first ultrasound image in the memory such that the first parameter and the first ultrasound image are associated in the memory (Aladahalli Fig 6 step 606, steps of Fig 5 are performed for current scan image); and retrieve the first parameter from the memory for comparison with a further parameter corresponding to a further ultrasound image (Aladahalli ¶0111 “This includes comparing the target context awareness graph (generated at 602) and the current context awareness graph (generated at 606). The comparison includes comparing a number of nodes, relative sizes of each node, and relative positions of each node between the target context awareness graph and the current context awareness graph”. Comparison requires retrieving from memory).
As per claim 6, Aladahalli further teaches wherein the processor circuit is configured for communication with the first ultrasound probe, and wherein the processor circuit is configured to: control the first ultrasound probe to acquire the first ultrasound image (Aladahalli Fig 6 step 604); and output, to the display, a screen display comprising at least one of the second ultrasound image or the target parameter, during acquisition of the first ultrasound image (Aladahalli Figs 8A-8C, ¶0110 “the current context awareness graph may be displayed independently at a location (e.g., above, below, or adjacent) …”).
As per claim 7, Aladahalli further teaches wherein the processor circuit is configured to output, to the display, a screen display comprising the target parameter, the first parameter, the first ultrasound image, the second ultrasound image, and the visual representation of the comparison displayed simultaneously (Aladahalli Figs 8A-8C, ¶0110 “the current context awareness graph may be displayed independently at a location (e.g., above, below, or adjacent) …”).
As per claim 8, Aladahalli further teaches wherein the first parameter is representative of a first orientation of the first ultrasound probe during the first acquisition period, and wherein the target parameter is representative of a second orientation of the second ultrasound probe during the second acquisition period (Aladahalli ¶0133 “The context awareness representation further includes, in the initial scan image, a first segment 841 connecting a first centroid of the first node 842 and a second centroid of the second node 844…” These segments are representative of orientations of probes. See ¶0078 “Next, method 400 proceed to 426. At 426, method 400 includes displaying initial context awareness graph, target context awareness graph, and one or more real time context awareness graphs … one or more desired transformation graphs including a desired probe movement and a desired scan path”).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Aladahalli in view of Morscher [US 20140198606 A1].
As per claims 14-15, Aladahalli further teaches wherein the preprocessor is configured to: receive a plurality of ultrasound images acquired by the first ultrasound probe during the first acquisition period, wherein the plurality of ultrasound images comprises the first ultrasound image; buffer the plurality of ultrasound images in the memory (Aladahalli Fig 6 buffered output of ultrasound images).
Aladahalli does not expressly teach filter the plurality of ultrasound images; and output a subset of the plurality of ultrasound images to the deep learning network wherein the preprocessor, to filter the plurality of ultrasound images, is configured to: determine a similarity metric for the plurality of ultrasound images buffered in the memory; and remove a portion of the plurality of ultrasound images from the memory based on the similarity metric to yield the subset of the plurality of ultrasound images.
Morscher, in a related field of optoacoustic imaging, teaches filter the plurality of images; and output a subset of the plurality of images wherein the preprocessor, to filter the plurality of images, is configured to: determine a similarity metric for the plurality of (Morscher ¶0082 “e a selection process in which the quality of individual video frames by using metrics, such as blur, mutual information, similarity or image correlation, is assessed”); and remove a portion of the plurality of images from the memory based on the similarity metric to yield the subset of the plurality images (Morscher ¶0082 “Based on these metrics, i.e. criteria, it is an option to discard individual, highly noise or motion affected video frames completely”).
As pe MPEP 2143.I. Examples of rationales that may support a conclusion of obviousness include: (c) Use of known technique to improve similar devices (methods, or products) in the same way. In the instant case, the claims are directed to using similarity metrics to discard / filter out certain images. Morscher uses this to increase overall quality and signal to noise ratio (Morscher ¶0082). Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify apparatus by applying teachings of Morscher to the system of Aladahalli, so as to improve the similar ultrasound devices.
Allowable Subject Matter
Claims 9-11 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. Claim 9 requires using an IMU for determining orientation of probe as in parent claim 8. Examiner does not find it obvious to modify the reference Aladahalli, since one goal in Aladahalli is to reduce “the need to implement sensors and additional electronics for tracking probe movement” (Aladahalli ¶0076). Examiner does not find any further references for teaching all limitations as in claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OOMMEN JACOB whose telephone number is (571)270-5166. The examiner can normally be reached 8:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANNE M KOZAK can be reached at 571-270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Oommen Jacob/ Primary Examiner, Art Unit 3797
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