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
In view of the amendment to the specification submitted 7/9/2025, the objection to the specification is withdrawn.
Applicant's arguments directed to the claim objection of claim 4 have been fully considered but they are not persuasive. Claim 1 instantiates the limitation “multiparametric MRI data” in line 3. The recitation of “the multiparametric MRI” is improper antecedent reference to the instantiated limitation in claim 1. The objection is maintained.
In view of the amendment to claims 17, 21, 25 and 41, the objection to claims 17, 21, 25 and 41 is withdrawn.
Applicant's arguments directed to the 35 USC 112(f) interpretation have been fully considered and are persuasive. The 35 USC 112(f) interpretation and associated 35 USC 112(b) rejection is withdrawn.
In view of the amendment and arguments directed to claim(s) 1, 2, 7, 15 and 21, the 35 USC 112(a) rejection of claim(s) 1-36 is withdrawn.
In view of the amendment directed to claim(s) 1, the 35 USC 112(b) rejection of claim(s) 1 is withdrawn.
In view of the amendment directed to claim(s) 14, the 35 USC 112(b) rejection of claim(s) 14 is withdrawn.
Applicant's arguments directed to the 35 USC 112(b) rejection of claim 24 have been fully considered but they are not persuasive. Claim 15 instantiates the limitation “multiparametric MRI data” in line 3. The recitation of “the MRI data” is improper antecedent reference to the instantiated limitation in claim 15. The 35 USC 112(b) rejection is maintained.
In view of the amendment directed to claim(s) 29, the 35 USC 112(b) rejection of claim(s) 29 is withdrawn.
In view of the amendment directed to claim(s) 32, the 35 USC 112(b) rejection of claim(s) 32 is withdrawn.
In view of the amendment directed to claim(s) 34, the 35 USC 112(b) rejection of claim(s) 34 is withdrawn.
Applicant's arguments directed to the 35 USC 101 abstract idea rejection of claims 1-38 have been fully considered but they are not persuasive. The applicant argues that with the inclusion of the description of the gathered data and its further description of the correspondence of that data is sufficient to overcome this rejection. The Examiner respectfully disagrees. The limitation “… wherein the multiparametric MRI data includes one or more T1-weighted imaging data, T2-weighted imaging data or diffusion weighted imaging data of a patient’s liver and spleen” is merely further describing the received data in the data gathering step. Data gathering is extrasolution activity and is not considered significantly more. The limitation “… wherein the machine learning models, segment the multiparametric MRI data using a U-shaped convolution neural network, providing segmented portions, and integrate radiometric features extracted from the segmented portions with the deep features extracted from the multiparametric MRI data” is descriptive of the mathematical manipulation of data to generate additional data. Additionally, it’s noted that the claims are not directed to the machine learning models or how the image data is processed. The amendment to the independent claims do not change the grounds of the present rejection. The human mind can be trained just like a machine learning model(s) and is capable of identification and correlation of features in presented images through observation and comparison. The amended limitations do not overcome the judicial exception because they are not integrated into a practical application for improving a technological field as these limitations are only for data collection or definitions of the data. Independent claim 15 is similarly discussed.
The applicant’s argument directed to the 35 USC 101 abstract idea rejection of claims 39-41 have been fully considered but they are not persuasive. The limitation “… a deep learning framework” is evaluates the gathered data for features. The human mind can be trained as a neural network (i.e., machine learning model) to evaluate, identify and correlate features present in presented images through observation and comparison. Further, the human mind can be trained in multiple different approaches to evaluation of presented images and is able to quantify observed features in the data mentally with or without physical aid. The claimed limitations of a deep learning framework and ensemble deep learning model is recited at a high level of generality and are not found to integrate the abstract idea into a practical application.
The 35 USC 101 abstract idea rejection is maintained.
In view of the Affidavit-Rule 130(a) submitted 7/9/2025, the prior art rejections of claims 3, 4, 13, 14, 17, 18 and 27-36 in which He et al., is applied are withdrawn.
The applicant’s remarks directed claim 39, and specifically the absence of an explicit citation of the CNN are persuasive. The rejection of claims 39-41 is withdrawn. Additionally, the applicant argues that the disclosure in James of “AI/machine learning algorithms” is not representative of the “ensemble deep learning model” as claimed. The Examiner respectfully disagrees. James discusses a “combination via AI/machine learning algorithms” in at least paragraph 0328. An “ensemble” is where all parts of something is taken as a whole. This appears to be the gist of James as further discussed at paragraph 0440 where “… Combination of diagnostic information has the best chance of revealing underlying pathology in MS, especially given the ability to combine these measurements using AI/machine learning/deep learning algorithms.” Thus, James is interpreted to meet the broadly claimed limitation.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claim 8 recites “wherein the diagnosing step predicts one or more biopsy-derived liver fibrosis stage or liver fibrosis percentage.” It’s noted that the disclosure provides that the prediction includes multiple stage and percentage pairs where these pairs are associated with the particular patient data (see paragraphs 0009, 0013, 0017, 0020, 0058, 0072-0073, 0083-0086, 0089 specification as filed). There is no disclosure where there are one or more stage results or (a) percentage.
Claim 34 recites “… a 3-dimensional residual blocks.” This is improper grammar. Appropriate correction is required.
Claim 39 recites “… using the integration the extracted.” This improper grammar. Appropriate correction is required.
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, 4-13, 15, 18-24, 30-36 and 43-44 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.
Claim 1 recites the limitation "the MRI multiparametric data" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the deep features." There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites ”… wherein the machine learning models, segment the multiparametric MRI data using a U-shaped convolution neural network, providing segmented portions, and integrate radiometric features extracted from the segmented portions with the deep features extracted from the multiparametric MRI data.” It is unclear and indefinite as to the application of the above limitation and in particular to the underlined portions. There has been no extraction of any data accomplished prior to requiring such extracted data to be integrated. Only the segmented data has been provided but it’s not had any further processing accomplished that would provide the necessary extracted features required in the “integrate” step. How is this intended to be accomplished if the necessary data hasn’t been acquired? Are all the claimed models each segmenting the multiparametric MRI data as well as accomplishing the integration of features?
Dependent claims 4-13 and 42-44 are rejected for failing to remedy the indefinite condition of claim 1 from which each ultimately depends.
Independent claim 15 is similarly discussed and rejected as claim 1 above.
Dependent claims 18-24 and 30-36 are rejected for failing to remedy the indefinite condition of claim 15 from which each ultimately depends.
Claim 4 recites “… a convolutional neural network provided with both Short and Long Residual connections (SLRes-U-Net).” However, claim 1 from which claim 4 depends has already instantiated “a U-shaped convolution neural network.” It is unclear and indefinite whether the CNN of claim 4 is intended to refer to a different convolution neural network or the same one now present in claim 1. For purposes of examination, the Examiner interprets the limitation of claim 4 to be referring back to the instantiated convolution neural network of claim 1.
Claims 4, 7, 8, 10 recite the limitation "the diagnosing step." There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the multiparametric MRI." There is insufficient antecedent basis for this limitation in the claim.
Claim 7 instantiates “MRI data” in lines 2 and 4. It is indefinite as to the intent of this instantiation. Is the second instance intended to be different MRI data or the same as the first instance? For purpose of examination, the Examiner interprets the second instance as intending to be referring to the first instance.
Claim 7 recites “clinical data” in line 4. This limitation has been instantiated in claim 1. It is indefinite whether this instantiation is to be different clinical data or should be referring to the clinical data of claim 1. For purpose of examination, the Examiner interprets this instance as intending to be referring to the instance instantiated in claim 1.
Claim 10 recites the limitation "the diagnosis." There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites “clinical data” in line 3. This limitation instantiated again in lines 5 and 6. The “multiparametric MRI data” also included in line 3 is not uniformly coupled with “clinical data (see at least line 9)” for this limitation to be considered properly referred to for antecedent basis. For purpose of examination, the Examiner interprets the second and third instance as intending to be referring to the first instance instantiated.
Claim 15 recites “multiparametric MRI data” in lines 3 and 12-13. It is indefinite and unclear if the second instance is intended to be a different data or should be referring to the first instance. For purpose of examination, the Examiner interprets the second instance as intending to be referring to the first instance instantiated.
Dependent claims 18-24 and 30-36 are rejected for failing to remedy the indefinite condition of claim 15 from which each ultimately depends.
Claim 18 recites “… a convolutional neural network provided with both short and long residual connections.” However, claim 15 from which claim 18 depends has already instantiated “a U-shaped convolution neural network.” It is unclear and indefinite whether the CNN of claim 18 is intended to refer to a different convolution neural network or the same one now present in claim 15. For purposes of examination, the Examiner interprets the limitation of claim 18 to be referring back to the instantiated convolution neural network of claim 15.
Claim 18 recites “jointly segment the liver and spleen.” It is indefinite and unclear if this instance is intended to be a different data or should be referring to the first instance instantiated in claim 15 from which claim 18 depends. For purpose of examination, the Examiner interprets the second instance as intending to be referring to the first instance instantiated.
Claim 18 recites “multiparametric MRI.” It is indefinite and unclear if this instance is a different input or should be referring to the input data instantiated in claim 15 from which claim 18 depends. For purpose of examination, the Examiner interprets the “multiparametric MRI” as intending to be referring to the “multiparametric MRI data” instantiated in claim 15.
Claim 21 recites “MRE data … clinical data.” This is improper antecedent reference to previously instantiated limitations. It is indefinite whether these new instantiations are intended to refer to limitations already instantiated in claims 21 and 15 respectively or different “MRE data … clinical data.” For purpose of examination, the Examiner interprets the “MRE data … clinical data” as intending to be referring to the “MRE data … clinical data” instantiated in claims 21 and 15 respectively.
Claim 24 recites the limitation "the MRI data." There is insufficient antecedent basis for this limitation in the claim.
Claim 30 recites the limitation "the convolutional neural network." There is insufficient antecedent basis for this limitation in the claim.
Claim 31 recites the limitation "the convolutional neural network." There is insufficient antecedent basis for this limitation in the claim.
Claim 32 recites the limitation "the convolutional 3-dimensional convolutional block." There is insufficient antecedent basis for this limitation in the claim.
Claim 34 recites the limitation "the convolutional neural network." There is insufficient antecedent basis for this limitation in the claim.
Claim 35 recites the limitation "the number of features channels." There is insufficient antecedent basis for this limitation in the claim.
Claim 36 recites the limitation "the number of features channels." There is insufficient antecedent basis for this limitation in the claim.
Claim 36 recites the limitation "the convolutional neural network." There is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 18 is an almost duplicate of lines 11-13 of claim 15 from which it depends and does not incorporate any further limiting subject matter. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, 4-13, 15, 18-24, 30-36 and 39-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more. The claim(s) recite(s) collecting information (receiving limitation, segmenting limitation), analyzing the information (diagnosing limitation, quantifying limitation) and providing results (communicating limitation). This judicial exception is not integrated into a practical application because the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the mental process is not integrated into a practical application nor includes limitations that amount to significantly more.
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application.
Under the 2019 Revised Patent Subject Matter Eligibility Guidance, the first step / Prong One of Step One (Step 2A) to determine patent eligibility requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept.
Prong (1) requires the determination of (a) the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and (b) determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated.
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Prong (2) asks does the claim recite additional elements that integrate the judicial exception into a practical application? For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981.
The second step (Step 2B) is to determine whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
Claim 1 is directed to a method for performing a medical diagnosis of liver disease:
A - receiving multiparametric MRI data and clinical data;
B - diagnosing aspects of liver disease by applying one or more machine learning models to the MRI data and clinical data, wherein the one or more machine learning models uses biopsy-derived histologic data as a reference standard; and
C - communicating detected and quantified liver disease aspect information to a user,
D - wherein the multiparametric MRI data includes one or more T1-weighted imaging data, T2-weighted imaging data or diffusion weighted imaging data of a patient’s liver and spleen;
E - wherein the machine learning models, segment the multiparametric MRI data using a U-shaped convolution neural network, providing segmented portions, and integrate radiometric features extracted from the segmented portions with the deep features extracted from the multiparametric MRI data.
Step A is a data gathering step. Step B observes and evaluates the content of step A. Step C formulates a judgment and opinion of step B which can be performed by the human mind. Steps B and C can be performed mentally by the human mind having been trained just like a neural network, with or without physical aid, to assess data of step A while using a (some type comparative) reference. Step D is merely further describing the received data of step A. Step E describes the mathematical manipulation of data to generate the additional data. The human mind so trained can integrate (e.g., combine in some manner) features of multiple image data.
Step 1 – yes, the claim is directed to a statutory category of a method.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The nominal recitation of a machine learning models does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application for improving a technological field as the additional claimed elements are only either for data collection or definitions. Merely using a computer as a tool to perform an abstract idea does not integrate a judicial exception into a practical application. The claimed limitations are not directed to an improvement in the functioning of the convolutional neural network. See MPEP 2106.04(d).
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions. Data gathering is extrasolution activity and does not qualify as significantly more. See MPEP 2106.05.
Claims 4-13 and 42-44 do not include further limitations that overcome the abstract idea rejection of claim 15.
Claim 15 is directed to a system for performing a medical diagnosis of liver disease:
F - one or more sources of multiparametric MRI data and clinical data;
G - a machine learning engine configured to receive the multiparametric MRI data and clinical data and diagnosing aspects of liver disease by applying machine learning models to the multiparametric MRI data and clinical data; and
H - a computerized output communicating detected and quantified liver disease aspect information from the machine learning engine to a user;
I - wherein the multiparametric MRI data includes one or more T1-weighted imaging data, T2-weighted imaging data or diffusion weighted imaging data of a patient’s liver and spleen;
J - wherein the machine learning models include a u-shaped convolution neural network provided with both short and long residual connections to simultaneous take multiparametric MRI data as input to jointly segment the liver and spleen, providing segmented portions, and wherein the machine learning models integrate radiometric features extracted from the segmented portions with the deep features extracted from the multiparametric MRI data.
Step F is a data gathering step. Step G observes and evaluates the content of step F. Step H formulates a judgment and opinion of step G which can be performed by the human mind. Steps G and H can be performed mentally by the human mind having been trained just like a neural network, with or without physical aid, to assess data of step F while using a (some type comparative) reference. Step I is merely further describing the received data of step F. Step J describes the mathematical manipulation of data to generate the additional data. The human mind so trained can integrate (e.g., combine in some manner) features of multiple image data.
Step 1 – yes, the claim is directed to a statutory category of a method.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The nominal recitation of a machine learning models does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application for improving a technological field as the additional claimed elements are only either for data collection or definitions. Merely using a computer as a tool to perform an abstract idea does not integrate a judicial exception into a practical application. The claimed limitations are not directed to an improvement in the functioning of the convolutional neural network. See MPEP 2106.04(d).
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions. Data gathering is extrasolution activity and does not qualify as significantly more. See MPEP 2106.05.
Claims 18-24 and 30-36 do not include further limitations that overcome the abstract idea rejection of claim 15.
Claim 39 is directed to a system for performing a medical diagnosis of the liver:
M - a deep learning framework segmenting liver and spleen image information using a convolutional neural network with both short and long residual connections to extract radiomic and deep features from multiparametric MRI; and
N - an ensemble deep learning model quantifying liver fibrosis stage and percentage using the integration the extracted radiomic and deep features, MRE data, and clinical data.
Step M observes and evaluates existing data (image information) that can be accomplished in the human mind. Step N judges and renders an opinion using the result of step M. Steps M and N can be performed mentally by the human mind having been trained just like a neural network, with or without physical aid, to assess data of step M.
Step 1 – yes, the claim is directed to a statutory category of a system (machine).
Step 2A Prong 1 – yes, the claim is directed to an abstract idea. The nominal recitation of a machine learning models does not take the claim limitation out of the mental processes grouping.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. The mere recitation of a machine learning models does not take the claim limitation out of the mental processes grouping. Using a computer as a tool to perform an abstract idea does not integrate a judicial exception into a practical application. The claimed limitations are not directed to a technological improvement in the functioning of the convolutional neural network. At best, the claimed combination amounts to an improvement to the abstract idea. See MPEP 2106.04(d) & 2106.05(a).
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions. Data gathering is extrasolution activity and does not qualify as significantly more. See MPEP 2106.05.
Claims 40 and 41 do not include further limitations that overcome the abstract idea rejection of claim 39.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Haddad et al., US Patent 11790673, discloses correlating extracted features from multiple images of the same cytological sample with reference data for detection of abnormality.
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 BARBARA D REINIER whose telephone number is (571)270-5082. The examiner can normally be reached M-T 10am - 6pm.
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/BARBARA D REINIER/Primary Examiner, Art Unit 2682