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 .
Response to Arguments
Applicant’s arguments and amendment have persuasively overcome the specification objection, the claim objections, almost all of the 112(b) rejections and the prior art rejections. The 112(a) rejections have been withdrawn due, in part, to a present lack of guidance.
The remaining issues are addressed below.
101
Applicant argues:
Similarly, the human mind is not equipped to "segment[] the first contrast image to obtain a segment of the lesion in the first contrast image;" "define[e] an inner region of the lesion and a region exterior to the lesion relative to the segment of the lesion in the first contrast image [for determination of first and second attributes," and "determining, by the model, whether the lesion has the one or more lesion characteristics associated with a LIRADS score, based on the first attributes and the second attributes," as recited in amended claim 1.
Examiner responds:
The background section says that this can be performed manually.
Applicant argues:
Further, such determination and input of attributes as targeted input parameters (instead of input of a full image) improves the operation of the machine learning model.
Examiner responds:
The background section says “The LI-RADS score for a liver lesion is allocated based on presence (or absence) of various lesion characteristics,” meaning that this claim language is well-understood, routine, conventional and that it is done mentally.
Applicant argues:
The additional limitations of claim 1 indeed contributes an inventive concept that improves the field of medical imaging to diagnose, categorize, and treat liver lesions.
Examiner responds:
Any benefits are the result of automation.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
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.
Claims 1-12, 14, 15, and 18-20 (all claims) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 14, and 15 recite “an inner region of the lesion and a region exterior to the lesion,” but it is not clear whether the capsule is considered the inner, the exterior, both, or neither. See, e.g., Fig. 7a showing the capsule as the border.
Claims 1 and 14 recite “the one or more lesion characteristics, associated with a LIRADS score,” but this lacks sufficient antecedent basis because the prior recitation of lesion characteristics did not recite this association.
Claim 15 recites “one or more lesion characteristics, associated with a LIRADS score,” but this raises two issues. First, it is not clear if the characteristics are associated with LIRADS scores generally (see, e.g., specification p. 3) or if the characteristics are associated with a particular LIRADS score. Second, the association is subjective because different people can have different associations for an LIRADS score.
Dependent claims are likewise rejected.
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.
Claims 1-12, 14, 15, and 18-20 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claim1 (and its dependents) recite methods, and processes satisfy Step 1 of the eligibility test.
Claim 15 (and its dependents) recite an apparatus, and machines satisfy Step 1 of the eligibility test.
Claim 14 recites a non-transitory computer readable medium, and manufactures satisfy Step 1 of the eligibility test.
Step 2A, prong one: All of the elements of the claims are a mental process because a person can look at a liver image and decide if there’s a lesion (or determine a LI-RADS score). Further, the various models are also mental processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
Note that the background section shows that the advantage of this invention is that it is done with AI to avoid the laborious process of performing it manually. Specification, p. 1. Recentive Analytics, Inc. v. Fox Corp., 134 F. 4th 1205, 1216 (Fed. Cir. 2025) concludes “Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Applicant’s statements regarding 112(a) support (specifically, the paragraph spanning pp. 10-11) in the remarks of July 29, 2026 are understood as evidence of the well-understood, routine, conventional nature of the technology at issue. MPEP 2106.07(a)(III)(A).
Claim Rejections - 35 USC § 103
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 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 1-12, 14, 15, and 18-20 (all claims) are rejected under 35 U.S.C. 103 as being unpatentable over US20200380675A1 (“Golden”) in view of US20210233645A1 (“Morard”)
1. (Currently Amended) A method of determining whether a lesion has one or more lesion characteristics, from a first contrast image of a liver, the method comprising: (Golden, abstract “This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.”)
segmenting the first contrast image to obtain a segment of the lesion in the first contrast image; (Golden, claim 10, “segmented lesion”)
determining first attributes in the inner region of the lesion in the first contrast image; (Golden, [0353] “intensity of the pixels in images, response to contrast.” As per claim 2, intensity teaches the claimed first attribute. Golden’s “response to contrast” teaches that this is a contrast image. See also, [0005].)
determining second attributes in the region exterior to the lesion in the first contrast image; and (Golden, [0032] “set the class of each pixel to a background class when the cancerous class probability for the pixel is below a determined threshold.” Golden’s background teaches the claimed second attribute, and that it is the background teaches the claimed region exterior.)
inputting the first attributes and second attributes to a model trained using a machine learning process; and
determining, by the model, whether the lesion has the one or more lesion characteristics, associated with Liver Reporting and Data System (LI-RADS) score, based on the first attributes and the second attributes. (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence, and then calculates the corresponding LI-RADS score.”)
Golden is not relied on for the below claim language.
However, Morard teaches defining an inner region of the lesion and a region exterior to the lesion relative to the segment of the lesion in the first contrast image; (Morard, [0100] “APHE, WO, and CAP are detected based on a contrast between the lesion and its surroundings.” Morard’s surroundings teach the claimed region exterior.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Morard to the teachings of Golden such that Morard’s characterization of medical images is used with Golden’s images for the purpose of implementation details (compare the cited section of Morard to Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence, and then calculates the corresponding LI-RADS score.”
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
2. (Currently Amended) A method as in claim 1 wherein the first attributes and the second attributes are (i) statistical measures of intensity values of pixels or voxels in the respective regions or (ii) radiomics features of intensity values of pixels or voxels in the respective regions. (Golden, [0353] “intensity of the pixels in images”)
3. (Currently Amended) A method as in claim 2 wherein the statistical measures or radiomics features comprise one or more of:
an energy, (Golden, Fig. 10, right hand scale)
interquartile range, kurtosis,
maximum diameter, (Golden, Fig. 48, linear dimension 4804 and orthogonal dimension 4806.)
mesh volume, (Golden, [0138] “The internal volume of the polygonal mesh is then calculated.”)
minor axis, (Golden, Fig. 48, linear dimension 4804 and orthogonal dimension 4806.)
length, or (Golden, Fig. 48, linear dimension 4804 and orthogonal dimension 4806.)
sphericity of the respective region. (Golden, [0033] “set the class of each pixel to a background class when the pixel is not part of a central fully-connected segmentation.” Golden’s not connected teaches being not spherical.)
4. (Currently Amended) The method as in claim 1, wherein the lesion characteristics are at least one of: a hyper-enhancement, a hypo-enhancement, a capsule, or washout. (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence.” Golden’s enhancement teaches the claimed hyper and hypo (see, e.g., [0323], and Golden’s corona is another term for the claimed capsule.)
5. (Currently Amended) The method as in claim 1 further comprising:
determining third attributes in an inner region of the lesion in a second contrast image at a different contrast phase to the first contrast image; (Golden, [0379]-[0382]. These paragraphs list different times and amounts of contrast, which as per paragraph [0378] (cited below), are used for machine learning.)
determining fourth attributes in a region exterior to the lesion in the second contrast image; and (Golden, [0379]-[0382]. These paragraphs list different times and amounts of contrast, which as per paragraph [0378] (cited below), are used for machine learning.)
wherein the step of using a model trained using a machine learning process is further based on the third attributes and the fourth attributes. (Golden, [0378] “the series displayed will be those that inform LI-RADS scoring.” Golden’s LI-RADS scoring teaches the claimed use of machine learning.)
6. (Currently Amended) The method as in claim 5, wherein the method determines whether the lesion has the characteristic of washout, and (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence.” Additionally, this claim limitation is an intended use.)
the first contrast image is an arterial phase image, and (Golden, [0380] “2. As contrasts enters the liver” Contrast enters the liver through an artery)
the second contrast image is a portal venous image; or (Golden, [0381] “3. As contrast exits the liver” The portal vein connects to the liver, and takes blood away.)
the first contrast image is a portal venous image, and (Golden, [0381] “3. As contrast exits the liver” The portal vein connects to the liver, and takes blood away.)
the second contrast image is a delayed image. (Golden, [0382] “4. One or more scans after contrast has exited the liver” Golden’s “after” teaches the claimed delayed.)
Claim 7 is rejected as per claim 5.
8. (Currently Amended) The method as in claim 7,wherein the method determines whether the lesion has a capsule and (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence.” Golden’s corona is another term for the claimed capsule.)
wherein the first contrast image is a portal venous image, and (Golden, [0381] “3. As contrast exits the liver” The portal vein connects to the liver, and takes blood away.)
the second contrast image is a delayed image. (Golden, [0382] “4. One or more scans after contrast has exited the liver” Golden’s “after” teaches the claimed delayed.)
9. (Currently Amended) The method as in claim 8, further comprising:
(i) transforming at least one of the first contrast image or the second contrast image by mapping the cartesian co-ordinates of the respective images into polar coordinates so as to convert the circular structure of the lesion into a linearized form in each respective contrast image; and (Golden, [0188] “Linear transformations and elastic transformations describe the two main classes of registration algorithms.” These claim limitations are nonfunctional descriptive material because they are for human benefit, see specification, p. 14, ll. 9-10, “has enhanced the visual ability”)
(ii) defining the inner region, the region exterior to the lesion and the boundary region with respect to bands visible in the linearized form of the lesion in each respective transformed image. (Golden, [0188] “Linear transformations and elastic transformations describe the two main classes of registration algorithms.” These claim limitations are nonfunctional descriptive material because they are for human benefit, see specification, p. 14, ll. 9-10, “has enhanced the visual ability”)
Claim 10 is rejected as per claim 9.
11. (Currently Amended) The method as in claim 1, wherein the method determines whether the lesion has the characteristic arterial phase hyper enhancement and (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement” The claimed APHE can be “at once envisaged,” MPEP 2131.02(III). Additionally, this claim limitation is an intended use.)
the first contrast image is an arterial phase image. (Golden, [0380] “2. As contrasts enters the liver” Contrast enters the liver through an artery)
12. (Currently Amended) The method as in claim 1,further comprising:
determining a Liver Imaging Reporting and Data System (LI-RADS) score for the lesion from the one or more characteristics. (Golden, [0391] “The system can automatically define features of liver lesions in the different series, comprising enhancement, washout, and corona presence, and then calculates the corresponding LI-RADS score.”)
Claims 14, 15, and 18-20 are rejected as per the above claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160171708 A1 – Fig. 4B
US 20180321345 A1 – Fig. 5
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
