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 .
Receipt of Applicant’s Amendment filed November 24, 2025, is acknowledged.
Response to Amendment
Claims 1, 17, and 24 have been amended. Claims 4, 5, 7, and 19 have been canceled. Claims 1-3, 6, 8-18, and 20-24 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed November 24, 2025, have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues 35 USC §101 Rejection, starting pg. 10 of Remarks:
Claim Rejections - 35 U.S.C. § 101
The pending claims were rejected under 35 U.S.C. §101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Applicant respectfully asserts that claims as a whole are not directed to an abstract idea, and further asserts that the claims comprise a practical application and amount to significantly more than an abstract idea, and thus are directed to statutory subject matter under 35 U.S.C. § 101.
In the previous Amendment, Applicant set forth extensive arguments evidencing why the claims are not directed to an abstract idea, how the claims incorporate any alleged abstract idea into a practical application, and how the claims recite significantly more than an abstract idea. These arguments are hereby incorporated into this response in their entirety. Below, Applicant addresses the current Office Action and the current claim set.
Step 2A, Prong 2 of the Alice Analysis
Under Step 2A, Prong 2 of the subject matter eligibility analysis under 35 U.S.C. 101, the Patent Office must "evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception." If a claim applies, relies on, or uses "the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception," then the claim is directed to patent eligible subject matter under Step 2A, Prong 2 and the Alice/101 analysis is complete (i.e., it does not proceed to Step 2B). 2019 Eligibility Guidance, pg. 54.
The Patent Office asserts that the claims are directed to a mental process and/or certain methods of organizing human activity:
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in non-bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the "Mental Processes" grouping of abstract ideas. A person can identify occurrences of radiology findings and uncertainty indicators, assign scores based on the indicators that includes mapping the indicators to scores using a look-up table, correlate findings in non-radiology reports, and provide a representation of uncertainty scores with the radiology findings. Claim 24 recites a machine, which is a generic machine (see MPEP 2106.04(a)(2) III C, where using a generic computer with a judicial exception was found to be abstract). Accordingly, the claim recites an abstract idea. Claims 1 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claim displays uncertainty scores and radiology findings, the claim is also abstract under Certain Methods of Organizing Human Activity. Giving the claim it's broadest reasonable interpretation, providing uncertainty scores and radiology findings is managing personal behavior by teaching the uncertainty or certainty of radiology findings to a user (e.g., see paras. [0005] and [0033] where degree of uncertainty/certainty (uncertainty score) is important information for a physician).
Applicant respectfully asserts that the claims as a whole are not directed to the abstract idea of a mental process or certain methods of organizing human activity.
A. Mental Processes
Applicant asserts that the claims comprise elements that are not performed in the human mind alone, and thus are not directed to the abstract idea of a mental process. For example, the claims comprise:
correlating the identified occurrences of radiology findings with clinical findings in companion non-radiology reports by, for a radiology finding under analysis, training a machine learning (ML) component to output a likelihood value of occurrences of the radiology finding under analysis being confirmed as a function of the uncertainty scores associated with the occurrences of the radiology finding under analysis, wherein the training uses as training data the assigned uncertainty scores for the identified occurrences of the radiology finding under analysis and uses the correlated clinical findings as ground truth values
and
displaying, on a user interface (UI) of a display device operatively connected with the at least one electronic processor: (i) a representation of the uncertainty scores correlated to the occurrences of radiology findings and (ii) a visualization of the computed radiologist calibration relationship as a plot of confirmation likelihood versus uncertainty score.
Applicant notes that this is not something that a human being can accomplish in their mind, with or without pen and paper. The human mind cannot "train[] a machine learning (ML) component to output a likelihood value of occurrences of the radiology finding under analysis being confirmed as a function of the uncertainty scores associated with the occurrences of the radiology finding under analysis," and cannot display anything on a user interface.
The claim does not provide any details about how the training a machine learning model operates to output a likelihood value as it is recited at a high level of generality. There is also no indication of an improvement to machine learning technology itself. A person can output a likelihood value of occurrences of radiology findings.
B. Certain Methods of Organizing Human Activity
Applicant asserts that the claims as a whole are not directed to the abstract idea of certain methods of organizing human activity. The Patent Office asserts that the claims are directed to the following sub-category of organizing human activity: managing personal behavior or relationships or interactions between people. Although the Patent Office does not identify which grouping within the managing personal behavior or relationships or interactions between people sub-category of organizing human activity (i.e., which of social activities, teaching, and following rules or instructions), Applicant assumes the Patent Office points to "following rules or instructions."
Teaching was cited (pg. 11 of Non-Final Rejection). Providing radiology findings is diagnosing (teaching) and identifying a radiologist that is overly confident or unduly cautious is teaching about the radiologist.
Applicant asserts that the claims as a whole are not directed to a method or system of managing personal behavior or relationships or interactions between people (i.e., following rules or instructions)).
The steps in Claim 24 does not even require a computer, just a machine learning component and a user interface. Presumably a person can perform all of the functions other than training a machine component and display information by a user interface. A person can even display information using pen and paper.
MPEP § 2106.04(a)(2)(II)(C) provides numerous examples of claims and applications that recite "following rules or instructions." While the very specific facts of these examples do not align perfectly with the claims at issue (which notably is not a requirement for examples, otherwise they would not be useful as examples), they are extremely informative. The claims at issue do not align with any of the examples provided in MPEP § 2106.04(a)(2)(II)(C), as the claims at issue do not recite "following rules or instructions." For example, the claimed method "for use in training a convolutional neural network using a training data set" is not similar to the provided examples of "rules for playing games," or "assigning hair designs to balance head shape," or "a series of instructions of how to hedge risk."
Respectfully, not sure why Applicant is arguing rules or instructions above.
For example, MPEP § 2106.04(a)(2)(II)(C) utilizes In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1161, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018) as an example:
The patentee claimed a method of playing a dice game including placing wagers on whether certain die faces will appear face up. 911 F.3d at 1160; 129 USPQ2d at 1011. The Federal Circuit determined that the claims were directed to the abstract idea of "rules for playing games", which the court characterized as a certain method of organizing human activity. 911 F.3d at 1160-61; 129 USPQ2d at 1011.
Thus, In re Marco Guldenaar Holding B.V. recited simply rules for a human to follow, and thus recited "following rules or instructions" pursuant to "certain methods of organizing human activity." The present claims are not a set of rules, such as rules for a game or any other rules.
MPEP § 2106.04(a)(2)(II)(C) also provides "[o]ther examples of following rules or instructions recited in a claim," which include:
i. assigning hair designs to balance head shape, In re Brown, 645 Fed. Appx. 1014, 1015-16 (Fed. Cir. 2016) (non-precedential); and
ii. a series of instructions of how to hedge risk, Bilski V. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1004 (2010).
Claim 1 of In re Brown is very clearly directed to a series of steps that a human being would follow when cutting a person's hair:
1. A method of cutting hair comprising;
a) defining a head shape as one of balanced, horizontal oblong or vertical oblong by determining the greater distance between a first distance between a fringe point and a low point of the head and a second distance between the low point of the head and the occipital bone;
b) designating the head into at least three partial zones;
c) identifying at least three hair patterns;
d) assigning at least one of said at least three hair patterns to each of the said partial zones to either build weight or remove weight in at least two of said partial zones; and
e) using scissors to cut hair according to said assigned hair pattern in each of the said partial zones.
In other words, in order to cut hair a human being would define the head shape, designate the head into zones, identify hair patterns, assign the hair pattern to the zones, and use scissors to cut the hair. There is nothing recited in the claims that a human being could not do to cut hair, when following instructions.
In contrast to the claims of In re Brown, the present claims recite steps that are not instructions to a human being to follow when analyzing radiology reports. For example, among other aspects, a human being does not "correlat[e] identified occurrences of radiology findings with clinical findings in companion non-radiology reports by, for a radiology finding under analysis, training a machine learning (ML) component to output a likelihood value of occurrences of the radiology finding under analysis being confirmed as a function of the uncertainty scores associated with the occurrences of the radiology finding under analysis, wherein the training uses as training data the assigned uncertainty scores for the identified occurrences of the radiology finding under analysis and uses the correlated clinical findings as ground truth values." Nor does a human being "display[], on a user interface (UI) of a display device operatively connected with the at least one electronic processor: (i) a representation of the uncertainty scores correlated to the occurrences of radiology findings and (ii) a visualization of the computed radiologist calibration relationship as a plot of confirmation likelihood versus uncertainty score." Thus, the claims do not recite a method that is performed by a human following instructions.
Claim 1 of Bilski V. Kappos is very clearly directed to a series of steps that a human being would follow when making a financial transaction:
(a) initiating a series of transactions between said commodity provider and
consumers of said commodity wherein said consumers purchase said commodity at a fixed rate based upon historical averages, said fixed rate corresponding to a risk position of said consumers;
(b) identifying market participants for said commodity having a counter-risk
position to said consumers; and
(c) initiating a series of transactions between said commodity provider and said market participants at a second fixed rate such that said series of market participant transactions balances the risk position of said series of consumer transactions.
In other words, in order to hedge risk, a human being would initiate a series of transactions between said commodity provider and consumers, identify market participants, and initiate a series of transactions between said commodity provider and said market participants. There is nothing recited in the claims that a human being could not do to hedge risk, when following instructions.
In contrast to the claims of Bilski V. Kappos, the present claims recite steps that are not instructions to a human being to follow when when analyzing radiology reports. For example, among other aspects, a human being does not "correlat[e] identified occurrences of radiology findings with clinical findings in companion non-radiology reports by, for a radiology finding under analysis, training a machine learning (ML) component to output a likelihood value of occurrences of the radiology finding under analysis being confirmed as a function of the uncertainty scores associated with the occurrences of the radiology finding under analysis, wherein the training uses as training data the assigned uncertainty scores for the identified occurrences of the radiology finding under analysis and uses the correlated clinical findings as ground truth values." Nor does a human being "display[], on a user interface (UI) of a display device operatively connected with the at least one electronic processor: (i) a representation of the uncertainty scores correlated to the occurrences of radiology findings and (ii) a visualization of the computed radiologist calibration relationship as a plot of confirmation likelihood versus uncertainty score."
Thus, the claims do not recite a method that is performed by a human following instructions. Thus, Applicant has compared the present claims to the only provided examples (i.e., In re Marco Guldenaar Holding B.V., In re Brown, and Bilski V. Kappos identified by the Patent Office), to show how the present claims are unlike these examples of "following rules or instructions" by a human being. Accordingly, Applicant respectfully asserts that that the claims are directed to patent-eligible subject matter under the Alice framework.
The rejection is not based on following instructions but on teaching. Providing radiology information and information about radiologist is teaching which is abstract under managing personal behavior and interactions between people.
2A. Step 2A, Prong 2 of the Alice Analysis
Under Step 2A, Prong 2 of the subject matter eligibility analysis under 35 U.S.C. 101, the Patent Office must "evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception." If a claim applies, relies on, or uses "the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception," then the claim is directed to patent eligible subject matter under Step 2A, Prong 2 and the Alice 101 analysis is complete (i.e., it does not proceed to Step 2B). 2019 Eligibility Guidance, pg. 54.
Applicant affirms the conclusion above that the claims are not directed to a judicial exception, and respectfully asserts that the claims do indeed incorporate any judicial exception into a practical application. Even assuming, arguendo, that the claims are directed to an abstract idea, the elements of the independent claims integrate the exception into a practical application.
The claims have been amended herein to recite "for each individual radiologist associated with the plurality of radiology reports: (i) aggregating the assigned uncertainty scores and correlated clinical findings from the companion non-radiology reports; and (ii) computing an individual radiologist calibration relationship that relates uncertainty score to confirmation likelihood value," and "identifying, using the displayed visualization of the computed radiologist calibration relationship, a radiologist that is overly confident or unduly cautious in reporting radiology findings."
The claims recite a meaningful field-specific application. For example, the claimed method and system transforms heterogeneous clinical narratives and companion clinical findings into a per-radiologist calibration model that quantifies how each radiologist's uncertainty language relates to actual confirmations (see, e.g., paragraphs [0021]-[0024], and [0033]-[0035]). The specification explains the technical effect of this quantification, namely enabling targeted quality control and remediation (e.g., re-analysis by more experienced radiologists) and improving training data selection and imaging workflow predictions (see, e.g., paragraphs [0020], [0011]-[0013],and [0030]). The abstract idea is integrated into a practical application addressing the technical problem of radiologist-to-radiologist variability in uncertainty expression-a problem peculiar to the radiology reporting domain-not merely organizing human activity or displaying information.
Evaluating radiologists’ reports to identify if a radiologist is overly confident or unduly cautious in reporting is broadly analyzing information to teach about a radiologist. This would be abstract. The machine learning is recited at a high level of generality.
Thus, Applicant respectfully asserts that that the claims are directed to patent-eligible subject matter under the Alice framework. Applicant respectfully requests that the rejections under 35 U.S.C. § 101 be withdrawn, and respectfully submits that the claims are in condition for allowance.
The rejection is respectfully maintained but modified for the claims amendments.
Applicant argues 35 USC §103 Rejection, starting pg. 16 of Remarks:
Based on the claim amendments, the prior art rejection is withdrawn. However, the amendments have resulted in new 35 USC 112 rejections.
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 1-3, 6, 8-18, and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3, 6, 8-18, and 20-24 are directed to a product, system, or method, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 24 as the claim that represents the claimed invention for analysis and is similar to product Claim 1 and system Claim 17.
Claim 24 recites the limitations of:
A radiology report analysis method, comprising:
identifying occurrences of radiology findings and associated uncertainty indicators in a plurality of radiology reports, wherein the identifying includes associating the uncertainty indicators with corresponding radiology findings based on grammar and/or word proximities in text of the radiology reports;
assigning uncertainty scores on a numerical scale to the identified occurrences of radiology findings based on the associated uncertainty indicators comprising mapping the uncertainty indicators;
correlating the identified occurrences of radiology findings with clinical findings in companion non-radiology reports by, for a radiology finding under analysis, training a machine learning (ML) component to output a likelihood value of occurrences of the radiology finding under analysis being confirmed as a function of the uncertainty scores associated with the occurrences of the radiology finding under analysis; and
for each individual radiologist associated with the plurality of radiology reports: (i) aggregating the assigned uncertainty scores and correlated clinical findings from the companion non-radiology reports; and (ii) computing an individual radiologist calibration relationship that relates uncertainty score to confirmation likelihood value;
displaying, on a user interface (UI): (i) a representation of the uncertainty scores correlated to the occurrences of radiology findings, and (iii) a visualization of the computed radiologist calibration relationship as a plot of confirmation likelihood versus uncertainty score; and
identifying, using the displayed visualization of the computed radiologist calibration relationship, a radiologist that is overly confident or unduly cautious in reporting radiology findings.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in non-bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. A person can identify occurrences of radiology findings and uncertainty indicators, assign scores based on the indicators that includes mapping the indicators to scores, correlate radiology findings with clinical findings, aggregate uncertaniity scores and correlated findings and compute an individual radiologist relationship that relates uncertainty score to confirmation likelihood, display (using pen and paper) representation of uncertainty scores and plot confirmation likelihood versus uncertainty scores, and identify a radiologist that is overly confident or unduly cautious. Claim 24 recites a machine, which is a generic machine (see MPEP 2106.04(a)(2) III C, where using a generic computer with a judicial exception was found to be abstract). Accordingly, the claim recites an abstract idea. Claims 1 and 17 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
The claim is also abstract under Certain Methods of Organizing Human Activity. In as much as the claim is identifying radiology findings, displaying a representation of uncertainty scores of radiology findings and a visualization of radiologist calibration relationship as a plot of confirmation likelihood versus uncertainty score, and identifying a radiologist that is overly confident or unduly cautious, the claims are diagnosing and teaching, therefore abstract as managing personal behavior and interactions between people. Giving the claim it’s broadest reasonable interpretation, providing uncertainty scores and radiology findings is managing personal behavior by teaching the uncertainty or certainty of radiology findings to a user (e.g., see paras. [0005] and [0033] where degree of uncertainty/certainty (uncertainty score) is important information for a physician).
This judicial exception is not integrated into a practical application. In particular, the claims only recite: non-transitory computer readable medium, electronic processor, machine learning component, user interface, display device (Claim 1); display device, electronic processor, machine learning component, user interface (Claim 17); machine learning component, user interface (Claim 24). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See Applicant’s specification para. [0025] about implantation using a computer, where it appears to be using existing generic computers and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. The machine learning component is a generic machine recited at a high level of generality. Training a machine learning component is applying training to a generic machine at a high level of generality. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 17, and 24 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as providing (transmitting) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 17, and 24 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2, 3, 6, 8-16, 18, and 20-23 further define the abstract idea that is present in their respective independent claims 1 and 17 and thus correspond to Mental Processes and Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claim 21 recites training a machine learning model at a high level of generality and claims 10 -12 and 22 recite natural language processing at a high level of generality, and where it is equivalent to applying the model and processing to the abstract concept. Therefore, the claims 2, 3, 6, 8-16, 18, and 20-23 are directed to an abstract idea. Thus, the claims 1-3, 6, 8-18, and 20-24 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 6, 8-18, and 20-24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 24 recites “for each individual radiologist…aggregating the assigned uncertainty scores and correlated clinical findings” where no teaching of aggregating uncertainty scores and clinical findings can be found for a radiologist.
From Applicant’s specification…
“In another aspect, a radiology report analysis method includes: identifying occurrences of radiology findings and associated uncertainty indicators in a plurality of radiology reports; assigning uncertainty scores on a numerical scale to the identified occurrences of radiology findings based on the associated uncertainty indicators; correlating the identified occurrences of radiology findings with clinical findings in companion non-radiology reports; and providing a UI that displays a representation of the uncertainty scores assigned to the occurrences of radiology findings.” [0009]
Therefore, no teaching of aggregating scores and correlated clinical findings can be found. Claims 1 and 17 have a similar problem.
Claim 24 recites “(ii) computing an individual radiologist calibration relationship that relates uncertainty score to confirmation likelihood value…” where no teaching of computing a radiologist calibration relationship can be found in the specification.
From Applicant’s specification…
“To analyze a radiology report 32 for a patient who underwent a radiology examination (e.g., patient A) with corresponding comparison documents 34, the at least one electronic processor 20 configured to implement a trained ML component 46 to correlate findings in the radiology report with clinical findings in the companion document(s). The ML component 46 can be trained with training data constituting the assigned uncertainty scores 40 for the identified occurrences of the radiology finding 36 under analysis and uses the correlated clinical findings as ground truth values. The ML component 46 is configured to output a likelihood value 48 of occurrences of the radiology finding 36 under analysis being confirmed as a function of the uncertainty scores 40 associated with the occurrences of the radiology finding under analysis, which can be displayed on the representation 44. The trained ML component 46 could also be employed to automatically select high quality training data sets for training a computer-aided diagnostic (CADx) system. To do so, the training data set is automatically selected by using as training data those occurrences of radiology findings for which the ML component 46 outputs a high likelihood value (e.g. above a preset threshold), while not including in the training data set those occurrences of radiology findings for which the ML component 46 outputs a likelihood value below the preset threshold. In this way, the CADx system is trained on examples for which there is a high likelihood the radiology finding is correct.” [0030]
Therefore, output likelihood of radiology finding confirmed as a function of uncertainty scores. Claims 1 and 17 have a similar problem.
Claim 24 recites “identifying, using the displayed visualization of the computed radiologist calibration relationship, a radiologist that is overly confident or unduly cautious in reporting radiology findings” where there is no teaching of identifying a radiologist using displayed visualization.
From Applicant’s specification:
“In other embodiments disclosed herein, a dashboard or other GUI is configured to visualize the results, for example by plotting the distribution of uncertainty indicators (or scores) over all radiology reports, or over some subset such as all radiology reports generated by a specific radiologist or radiology work shift. Or, the user can select to compare report features of the reports with most versus least uncertainty, to possibly identify ways to increase confidence in the findings. Visualization of comparisons of report uncertainty with pathology or physician-authored findings may permit the analyst to identify areas in which radiologists are overly confident in their findings, or unduly cautious in reporting the radiology findings.” [0024]
Therefore, areas are identified, not radiologist. Claims 1 and 17 have a similar problem.
Claims 2, 3, 6, 8-16, 18, and 20-23 are further rejected as they depend from their respective independent claim.
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 1-3, 6, 8-18, and 20-24 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 24 recites ““for each individual radiologist…aggregating the assigned uncertainty scores and correlated clinical findings” where it is indefinite as to aggregating scores and correlated clinical findings for a radiologist (e.g., how is correlation data aggregated?). For examination purposes, this is interpreted as adding scores and adding correlation data. Claims 1 and 17 have a similar problem.
Claims 2, 3, 6, 8-16, 18, and 20-23 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Prior Art Rejection
A prior art search was conducted but does not result in a prior art rejection at this time. The best prior art found to date is Pub. No. US 2016/0267226 to Xu et al. Xu teaches correlating radiology findings with clinical data, however they fail to teach specifics of claimed features such as training machine learning and aggregating scores and correlated findings. The non-patent literature of Isenegger teaches scoring uncertainty language but fails to teach training machine learning and aggregating scores and correlated findings.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684