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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119 (e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/076,384, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, the identification of sub-volumes as recited in the independent claims as: “identifying a sub-volume of the volume of tissue within the stack of 2D acoustic images and comprising the tissue type, wherein the identifying is based at least in part on the one or more acoustic parameters…” is not clearly supported in parent Application No. 17/076,384. Accordingly, an effective filing date of 10 December 2024 is accorded to the claims of the instant application in this office action.
Status of Claim(s)
Claim(s) 1 is cancelled.
Claims 7, 21 and 22 are 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(s) 2-6, 8-20 and 23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roy (US 20220084203 A1).
Claim Objections
Claims 7, 21 and 22 are 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 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.
(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.
Claim(s) 2-6, 8-20 and 23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roy (US 20220084203 A1).
Regarding Claim 2:
Roy teaches: A computer implemented method for detecting a region of interest (ROI) in a volume of tissue, the method comprising (Abstract, “A system as described herein may comprise: a plurality of parameters associated with image characteristics related to one or more images of a volume of tissue”):
(a) receiving a stack of two dimensional (2D) acoustic images, wherein the stack of 2D acoustic images comprises a representation of sound propagation through the volume of tissue (Paragraph 110, “An acoustomechanical parameter may comprise at least one of, for example, sound speed, sound attenuation, and sound reflection. Each rendering may be formed from one or more “stacks” of 2D images corresponding to a series of “slices” of the volume of tissue for each measured acoustomechanical parameter at each step in a scan of the volume of tissue.”);
(b) receiving a first ROI boundary of a first ROI on a first 2D acoustic image of the stack of 2D acoustic images (Paragraph 130, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”); and
(c) extracting one or more acoustic parameters associated with a tissue type within the first ROI boundary (Paragraph 93, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”); and
(d) applying an ROI detection algorithm to the first ROI, wherein applying the ROI detection algorithm comprises (Paragraph 93, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”:
(i) identifying a sub-volume of the volume of tissue within the stack of 2D acoustic images and comprising the tissue type, wherein the identifying is based at least in part on the one or more acoustic parameters (Paragraph 111, “The two-dimensional sound speed renderings may be associated with slices (e.g. coronal slices) through a volume of tissue. An acoustic sound speed rendering may comprise a three-dimensional (3D) acoustic sound speed rendering that is a volumetric representation of the acoustic sound speed of the volume of tissue. The sound speed rendering can characterize a volume of tissue with a distribution of one or more of: fat tissue (e.g., fatty parenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymal tissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue, fibroadenomas, etc.), and any other suitable tissue type within the volume of tissue.”); and
(ii) generating a second ROI boundary of a second ROI comprising the tissue type, on at least a second 2D acoustic image of the stack of 2D acoustic images (Paragraph 148, “A parameter of a set of parameters an image or data set may comprise one or more of the margin boundary score, the mean enhanced reflection, the relative mean of the enhanced reflection interior and exterior to the ROI, the standard deviation of the enhanced reflection, the mean sound speed, the relative mean sound speed interior and exterior to the ROI, the standard deviation of the sound speed, the mean attenuation, the standard deviation of the attenuation, the mean of the attenuation corrected for the margin boundary score, and the standard deviation of the attenuation corrected for the margin boundary score.”).
Regarding Claim 3:
Roy teaches: The method of claim 2, further comprising at (d), generating the second ROI boundary on the first 2D acoustic image of the stack of 2D acoustic images (Paragraph 130, “For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc”).
Regarding Claim 4:
Roy teaches: The method of claim 3, further comprising at (d), comparing the first ROI to the second ROI based at least in part on the one or more acoustic parameters (Paragraph 130, Interior and exterior Roi’s are based on relation to each other which are calculated based on acoustic data.).
Regarding Claim 5:
Roy teaches: The method of claim 2, wherein the tissue type is at least one of: a cyst, a fibroadenoma, a cancer (Paragraph 6, “In some embodiments, the classification of lesion may comprise a cancer, a fibroadenoma, a cyst, a nonspecific benign mass, or an unidentifiable mass.”), peritumoral tissue (Paragraph 129, “In some embodiments, parameters may also be extracted from an expanded region known as the peritumoral region surrounding the ROI.”), parenchymal tissue, adipose tissue (Paragraph 123, “The stiffness rendering can characterize a volume of tissue with a distribution of one or more of: fat tissue (e.g., fatty parenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymal tissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue, fibroadenomas, etc.), and any other suitable tissue type within the volume of tissue.”), or skin tissue.
Regarding Claim 6:
Roy teaches: The method of claim 2, wherein the first ROI boundary is generated manually, semi- automatically, or automatically (Paragraph 98, “The extraction may use a user selected, computer selected, or computer aided selection of a region of interest (ROI).”).
Regarding Claim 8:
Roy teaches: The method of claim 2, further comprising at (d), generating an ROI mask based at least in part on the one or more acoustic parameters; and wherein (i) further comprises applying the ROI mask to the stack of 2D acoustic images (Paragraph 150, “For example, a rendering may be formed from one or more “stacks” of 2D images corresponding to a series of “slices” of the volume of tissue for each measured acoustomechanical parameter at each step in a scan of the volume of tissue. Each slice may comprise an image or layer of the rendering. Each layer, subset of layers, classification of layers, and/or ROI may have one or many associated parameters, for example, any type of parameter associated with image characteristics as described herein.”).
Regarding Claim 9:
Roy teaches: The method of claim 8, wherein the ROI mask is generated manually, semi- automatically, or automatically (Paragraph 98, “The extraction may use a user selected, computer selected, or computer aided selection of a region of interest (ROI).”).
Regarding Claim 10:
Roy teaches: The method of claim 8, wherein generating the ROI mask comprises outlining the first ROI boundary on the first 2D acoustic image of the stack of 2D acoustic images (Paragraph 150, “For example, a rendering may be formed from one or more “stacks” of 2D images corresponding to a series of “slices” of the volume of tissue for each measured acoustomechanical parameter at each step in a scan of the volume of tissue. Each slice may comprise an image or layer of the rendering. Each layer, subset of layers, classification of layers, and/or ROI may have one or many associated parameters, for example, any type of parameter associated with image characteristics as described herein.”).
Regarding Claim 11:
Roy teaches: The method of claim 10, further comprising at (ii), applying the ROI mask to at least the second 2D acoustic image of the stack of 2D acoustic images (Paragraph 150, “For example, a rendering may be formed from one or more “stacks” of 2D images corresponding to a series of “slices” of the volume of tissue for each measured acoustomechanical parameter at each step in a scan of the volume of tissue. Each slice may comprise an image or layer of the rendering. Each layer, subset of layers, classification of layers, and/or ROI may have one or many associated parameters, for example, any type of parameter associated with image characteristics as described herein.”) [Wherein the ROI mask is applied multiple times to multiple image slices (such as interior and exterior ROI’s described in Paragraph 148)].
Regarding Claim 12:
Roy teaches: The method of claim 2, further comprising at (ii), expanding the second ROI boundary to encompass a peripheral tissue volume adjacent to the sub-volume of the volume of tissue within the stack of 2D acoustic images (Paragraph 128, “In some embodiments, a set of parameters may be associated with a region of interest (ROI). An ROI may be a two-dimensional ROI. In some cases, an ROI may correspond to a region comprising all or a portion of a tumor. In some cases, the ROI also comprises a peri-tumoral region. An ROI may substantially circumscribe a lesion within a volume of tissue. An ROI may be user selected. In some instances, user selection of an ROI can indicate a starting point, which can be a point or region which may overlap or be in proximity to a tumor or peri-tumor. For example, a user might indicate a ROI as a closed loop, an arc, a circle, a dot, a line, or an arrow.”).
Regarding Claim 13:
Roy teaches: The method of claim 12, wherein the tissue type comprises a tumor, and wherein the peripheral tissue volume comprises a peritumoral region (Paragraph 128, “In some embodiments, a set of parameters may be associated with a region of interest (ROI). An ROI may be a two-dimensional ROI. In some cases, an ROI may correspond to a region comprising all or a portion of a tumor. In some cases, the ROI also comprises a peri-tumoral region.”).
Regarding Claim 14:
Roy teaches: The method of claim 2, further comprising at (ii), shrinking the second ROI boundary to form an inner ROI (Paragraph 130, “A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI.”).
Regarding Claim 15:
Roy teaches: The method of claim 14, wherein the tissue type comprises a tumor and wherein the inner ROI comprises a an inner tumoral region (Paragraph 129, “Parameters may be extracted from a region of a ROI. In some embodiments, parameters may also be extracted from an expanded region known as the peritumoral region surrounding the ROI. Such expanded region can be generated using various methods. An example method may be to add a uniform distance in each direction. Another example method can include finding the radius of the circle with an equivalent area of the ROI. This radius can be expanded by some multiplicative factor and the difference between the original and expanded radius can be added to each direction of the ROI. Likewise, this method can be modified such that there is a lower or upper threshold for the minimum and maximum radius sizes, respectively. Similarly, such methods can be used to shrink the region of the ROI to generate an inner tumoral ROI.”).
Regarding Claim 16:
Roy teaches: The method of claim 2, wherein second ROI boundary is generated automatically or semi- automatically (Paragraph 98, “The extraction may use a user selected, computer selected, or computer aided selection of a region of interest (ROI).”).
Regarding Claim 17:
Roy teaches: The method of claim 2, further comprising reviewing and optimizing the first ROI boundary (Paragraph 148, “A parameter of a set of parameters an image or data set may comprise one or more of the margin boundary score, the mean enhanced reflection, the relative mean of the enhanced reflection interior and exterior to the ROI, the standard deviation of the enhanced reflection, the mean sound speed, the relative mean sound speed interior and exterior to the ROI, the standard deviation of the sound speed, the mean attenuation, the standard deviation of the attenuation, the mean of the attenuation corrected for the margin boundary score, and the standard deviation of the attenuation corrected for the margin boundary score.”).
Regarding Claim 18:
Roy teaches: The method of claim 2, wherein the stack of 2D acoustic images comprise at least one of: sound speed data, reflection data, or attenuation data (Paragraph 149, Image data of ultrasound/acoustomechanical images involve sound speed data, reflection margins, and attenuation of data).
Regarding Claim 19:
Roy teaches: The method of claim 2, wherein the one or more acoustic parameters comprise a pixel intensity value of the tissue type (Paragraph 118, “The distribution of acoustic reflection signals may characterize a relationship (e.g., a sum, a difference, a ratio, etc.) between the reflected intensity and the emitted intensity of an acoustic waveform, a change in the acoustic impedance of a volume of tissue, or any other suitable acoustic reflection parameter. A stack of 2D acoustic reflection images may be derived from changes in acoustic impedance of the tissue and may provide echo-texture data and anatomical detail for the tissue.”).
Regarding Claim 20:
Roy teaches: The method of claim 2, further comprising at (c), determining an acoustic threshold value (Paragraph 113, “The sound speed rendering may be a waveform sound speed image. Such a method may comprise generating an initial sound speed rendering in response to simulated waveforms according to a travel time tomography algorithm. The initial sound speed rendering may be iteratively optimized until ray artifacts are reduced to a pre-determined a threshold for each of a plurality of sound frequency components.”), and
wherein (i) identifying the sub-volume of the volume of tissue within the stack of 2D acoustic images is based at least in part on the acoustic threshold value (Paragraph 113 and 114, Images are made from sound speed waveforms that have a pre-determined threshold. This is further described in paragraph 114 where attenuated acoustic sound slices are used to characterize multiple different volumes of tissues and parts of a tissue).
Regarding Claim 23:
Roy teaches: A computer implemented system comprising at least one processor, a memory, and instructions executable by the at least one processor to perform operation for detecting a region of interest (ROI) in a volume of tissue, the operations comprising (Abstract, “A system as described herein may comprise: a plurality of parameters associated with image characteristics related to one or more images of a volume of tissue”):
(a) receiving a stack of two dimensional (2D) acoustic images, wherein the stack of 2D acoustic images comprises a representation of sound propagation through the volume of tissue (Paragraph 110, “An acoustomechanical parameter may comprise at least one of, for example, sound speed, sound attenuation, and sound reflection. Each rendering may be formed from one or more “stacks” of 2D images corresponding to a series of “slices” of the volume of tissue for each measured acoustomechanical parameter at each step in a scan of the volume of tissue.”);
(b) receiving a first ROI boundary of a first ROI on a first 2D acoustic image of the stack of 2D acoustic images (Paragraph 130, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”); and
(c) extracting one or more acoustic parameters associated with a tissue type within the first ROI boundary (Paragraph 93, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”); and
(d) applying a ROI detection algorithm to the first ROI, wherein applying the ROI detection algorithm comprises (Paragraph 93, “Quantitative parameters may comprise, for example, a mean, a median, a mode, a standard deviation, and volume-averages thereof of any acoustic data type. A quantitative parameter may be calculated from a combination of data types. For example, a quantitative parameter may comprise a difference of parameters between a region in the interior of the ROI and in the exterior of the ROI. In another example, a quantitative parameter may comprise a difference between regions of interest, layers, classification of layers, etc.”):
(i) identifying a sub-volume of the volume of tissue within the stack of 2D acoustic images and comprising the tissue type, wherein the identifying is based at least in part on the one or more acoustic parameters (Paragraph 111, “The two-dimensional sound speed renderings may be associated with slices (e.g. coronal slices) through a volume of tissue. An acoustic sound speed rendering may comprise a three-dimensional (3D) acoustic sound speed rendering that is a volumetric representation of the acoustic sound speed of the volume of tissue. The sound speed rendering can characterize a volume of tissue with a distribution of one or more of: fat tissue (e.g., fatty parenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymal tissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue, fibroadenomas, etc.), and any other suitable tissue type within the volume of tissue.”); and
(ii) generating a second ROI boundary of a second ROI comprising the tissue type, on at least a second 2D acoustic image of the stack of 2D acoustic images (Paragraph 148, “A parameter of a set of parameters an image or data set may comprise one or more of the margin boundary score, the mean enhanced reflection, the relative mean of the enhanced reflection interior and exterior to the ROI, the standard deviation of the enhanced reflection, the mean sound speed, the relative mean sound speed interior and exterior to the ROI, the standard deviation of the sound speed, the mean attenuation, the standard deviation of the attenuation, the mean of the attenuation corrected for the margin boundary score, and the standard deviation of the attenuation corrected for the margin boundary score.”).
Relevant Prior Art Directed to State of Art
Duric (US 20180153502 A1)
ヴィオン ミシェル (JP 2012502682 A)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J SHERRILLO whose telephone number is (703)756-5605. The examiner can normally be reached 1st week of bi-week: Mon-Wed 7am-5:30pm PST, Thurs: 7am-4:30pm PST, Fri off / 2nd week of bi-week: Mon-Wed 7am-5:30pm PST, Thurs-Fri: 7am-4:30pm PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.J.S./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
Prosecution Insights