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 .
Response to Remarks
The Office Action has been made issued in response to amendment filed November 13, 2025. Claims 1-7, 9-13, 15-16 and 18-19 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Claim Rejections – 35 USC section § 101
On page 10 of the Response, Applicant argues “The claimed invention is not directed to mental processes because the claims improve technology through practical application of the algorithm. The claim described here involves practical application of an algorithm that prevents the waste of computing resources and the risk of misdiagnosis due to low-quality images. The claimed invention implements pre-execution conditional gating, which is a preemptive conditional control that prevents costly machine learning (ML) operation from running if the quality level of the images is below a standard. The claimed invention also automatically presents two clearly distinct clinical actions (e.g., "treating" versus "future care plan") based on risk. Such practical application of the algorithm improves the technology of medical diagnosis, and hence, the claim is not directed to an abstract idea of mental processes, and the rejection should be withdraw”.
In response, the Examiner disagrees. First, none the stated practical application such as “an algorithm that prevents the waste of computing resources and the risk of misdiagnosis due to low-quality images. The claimed invention implements pre-execution conditional gating, which is a preemptive conditional control that prevents costly machine learning (ML) operation from running if the quality level of the images is below a standard” are recited in the claims. Second, the “obtaining grading…” and “providing at least…” steps are not being performed by the machine learning algorithm.
Further on page 10 of the Response, Applicant argues that the method “Providing the treating guide for the first grade (high risk) and the future care plan guide for the second grade (low risk) is an automation of a clinical decision-making protocol, not merely data classification”.
In response, the Examiner disagrees because, the claims do not mention the automation of a clinical decision-making protocol.
On page 11 of the Response, Applicant states that the “present invention also solves specific technical problems within a medical Al diagnostic system. Problem 1 is the waste of ML computation and the risk of misdiagnosis due to low-quality images, and Problem 2 is the delay and inefficiency in clinical decision-making due to ambiguous guidance. The solution is preemptive quality control and systematic guidance bifurcation”.
In response, the Examiner disagrees because none of the solution to these technical problems are recited in the claims.
On page 12 of the Response, Applicant concluded that the additional items of “
when the obtained quality level information is at or above a predetermined quality level, obtain grade information including a first grade or a second grade based on the result, wherein the first grade indicates a higher risk for the at least one clinical condition than the second grade, provide at least one of first guide information for treating the clinical condition or second guide information for a future care plan for the clinical condition as guide information based on the grade information, and display the result, the quality level information and the guide information using graphical user interface” amounts to “significantly more” than the abstract idea and thus renders the claim eligible.
In response, the Examiner disagrees because as outlined in the rejection below, “obtain grading…” and “providing at least…” steps can be reasonably interpreted as mental steps performed by for example by an ophthalmologist. Lastly, the “display…” is an insignificant pre/post solution activity. Thus, the claim as a whole is directed to an abstract idea and thus is ineligible.
Examiner Notes
The Examiner suggests Applicant amending the claims to include the details of the training of the machine learning and/or the advantages of the claimed method such as “pre-execution conditional gating”.
Claim Rejections – Double Patenting
Applicant has filed a not terminal disclaimer. Thus, the rejection has not being withdrawn.
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 1-7, 9-13, 15-16 and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract without significantly more.
Step 1 Analysis: Claim 1 is directed to a system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1, in part recites “obtain a result associated with the at least one clinical condition based on the eye image when the obtained quality level information is at or above a predetermined quality level, obtain grade information including a first grade or a second grade based on the result, wherein the first grade indicates a higher risk for the at least one clinical condition than the second grade, provide at least one of first guide information for treating the clinical condition or second guide information for a future care plan for the clinical condition as guide information based on the grade information”. The “obtain” step of the claim encompass making a determination about the observation of the image of the eye for example by an ophthalmologist. Moreover, the results based on grading and quality may be interpreted as the ophthalmologist making a diagnosis and requesting the patient to follow up based on the diagnosis. Such mental observations or evaluations fall within the “mental processed” grouping of abstract ideas.
Step 2A Prong 2 Analysis: The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application. In particular, the claim in part recites the additional elements –
obtain quality level information of the eye image,
generate diagnosis assistant information based on the result
“storage unit” and “processor”
“machine learning model”
display the result, the quality level information and the guide information using graphical user interface.
As recited, the “obtaining” step is mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity.
As recited, the “generate” step is mere data output recited at a high level of generality, and thus are insignificant extra-solution activity.
The “storage unit” and “processor” are recited as being operated connected together. The “storage unit” and “processor” are recited at a high level of generality and amounts to no more than more memory connected with a processor used to apply the exception and are parts of a generic computer.
The “machine learning model” is used to generally apply the abstract idea without limiting how the trained deep learning model functions. The machine learning model is described at a high level such that it amounts to using a computer with a generic deep learning model to apply the abstract idea. These limitations only recite the outcomes of “obtaining results” and “create diagnosis information” and without any details about how the outcomes are accomplished.
Step 2B Analysis: The claim does not include additional elements that are
sufficient to amount to significantly more than the judicial exception. For the reasons
given in Step 2A Prong 2. Thus, the claim is not patent eligible.
Accordingly, the dependent claims 2-14 do not provide elements that overcome the deficiencies of the independent claim 1.
Claim 2 recites in part “wherein the result includes first result and second result, wherein the first result is used to assist in diagnosing first clinical condition, and the second result is used to assist in diagnosing second clinical condition, and wherein the first clinical condition and the second clinical condition are different“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 3 recites in part “wherein the first clinical condition is associated with an eye disease, wherein the second clinical condition is associated with a systemic disease, and wherein the systemic disease comprises at least one of hypertension, Alzheimer's disease, cytomegalovirus disease, stroke, arteriosclerosis or cardiovascular disease” do not overcome the rejection of the parent claim 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 4 recites in part “wherein the first clinical condition is associated with first systemic disease, wherein the second clinical condition is associated with second systemic disease, and wherein the first systemic disease or the second systemic disease comprises at least one of hypertension, Alzheimer's disease, cytomegalovirus disease, stroke, arteriosclerosis or cardiovascular disease“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 5 recites in part “wherein the machine learning model includes first machine learning model and second machine learning model, and wherein the at least one processor is configured to obtain the first result based on the eye image using the first machine learning model and the second result based on the eye image using the second machine learning mode “ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 6 recites in part “wherein the eye image includes at least one vessel of the eye“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claims 7 recites in part “wherein the eye image includes a retinal image or a fundus image“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 9 recites in part “wherein the at least one processor is configured to provide first guide information when the obtained grade information is the first grade “do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 10 recites in part “wherein the at least one processor is configured to provide second guide information for future care plan for the at least one clinical condition when the obtained grade information is the second grade“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 11 recites in part “wherein the at least one processor is configured to obtain quality grade information of the eye image, wherein the quality grade information includes first quality grade or second quality grade lower than the first quality grade, and wherein the at least one processor is configured to provide the diagnosis assistant information when the obtained quality grade information is the first quality grade “ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
` Claim 12 recites in part “wherein the diagnosis assistant information is not obtained when the obtained quality grade information is the second quality level “do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 13 recites in part “wherein the at least one processor is configured to require a new eye image when the obtained quality grade information is the second quality grade“ do not overcome the rejection of the parent claims 1 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 15 is similar in scope to claim 1 and is likewise rejected.
Claim 16 recites in part “wherein the result includes first result and second result, wherein the first result is used to assist in diagnosing first clinical condition, and the second result is used to assist in diagnosing second clinical condition, and wherein the first clinical condition and the second clinical condition are different clinical conditions each other “ do not overcome the rejection of the parent claims 15 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 18 recites in part “wherein the method further comprising obtaining quality grade information of the eye image, wherein the quality grade information includes first quality grade or second quality grade lower than the first quality grade, and wherein the diagnosis assistant information is provided when the obtained quality grade information is the first quality grade “ do not overcome the rejection of the parent claims 15 as stated above because the additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 19 is similar in scope to claim 1 and is likewise rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of 1-12 and 15 of U.S. Patent No. 11,276,497 (herein referred to as Patent’497). Although the claims at issue are not identical, they are not patentably distinct from each other because Patent’497 discloses an eye image obtaining unit configured to acquire a target eye image which is a basis for acquiring diagnosis assistance information on a subject not required by the instant claims.
Claims 1-7, 9-13, 15-16 and 18-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of 1-21 of U.S. Patent No. US11790645 (herein referred to as Patent’645). Although the claims at issue are not identical, they are not patentably distinct from each other because Patent’645 discloses a wherein the diagnosis assistant information comprises first guide information for treating at least one of the first clinical condition or the second clinical condition when the obtained grade information is the first grade, not required by the instant claims.
Instant claims
US11790645B2
US Patent No.: 11,276,497
1. A diagnosis assistant device for assisting diagnosis of at least one clinical condition based on an eye image, comprising: a storage unit; and at least one processor operably connected to the storage unit and configured to: obtain a result associated with the at least one clinical condition based on the eye image using a machine learning model, and generate diagnosis assistant information based on the result.
2. The diagnosis assistant device of claim 1, wherein the result includes first result and second result, wherein the first result is used to assist in diagnosing first clinical condition, and the second result is used to assist in diagnosing second clinical condition, and wherein the first clinical condition and the second clinical condition are different clinical conditions each other.
3. The diagnosis assistant device of claim 2, wherein the first clinical condition is associated with an eye disease, wherein the second clinical condition is associated with a systemic disease, and wherein the systemic disease comprises at least one of hypertension, Alzheimer's disease, cytomegalovirus disease, stroke, arteriosclerosis or cardiovascular disease.
4. The diagnosis assistant device of claim 2, wherein the first clinical condition is associated with first systemic disease, wherein the second clinical condition is associated with second systemic disease, and wherein the first systemic disease or the second systemic disease comprises at least one of hypertension, Alzheimer's disease, cytomegalovirus disease, stroke, arteriosclerosis or cardiovascular disease.
5. The diagnosis assistant device of claim 2, wherein the machine learning model includes first machine learning model and second machine learning model, and wherein the at least one processor is configured to obtain the first result based on the eye image using the first machine learning model and the second result based on the eye image using the second machine learning model.
6. The diagnosis assistant device of claim 1, wherein the eye image includes at least one vessel of the eye.
7. The diagnosis assistant device of claim 1, wherein the eye image includes a retinal image or a fundus image.
wherein the at least one processor is configured to obtain grade information including first grade or second grade based on the result, wherein the first grade indicates a higher risk for the at least one clinical condition than the second grade, and wherein the at least one processor is configured to provide guide information based on the obtained grade information.
9. The diagnosis assistant device of claim 1, wherein the at least one processor is configured to provide first guide information for treating the at least one clinical condition when the obtained grade information is the first grade.
10. The diagnosis assistant device of claim, wherein the at least one processor is configured to provide second guide information for future care plan for the at least one clinical condition when the obtained grade information is the second grade.
11. The diagnosis assistant device of claim 1, wherein the at least one processor is configured to obtain quality grade information of the eye image, wherein the quality grade information includes first quality grade or second quality grade lower than the first quality grade, and wherein the at least one processor is configured to provide the diagnosis assistant information when the obtained quality grade information is the first quality grade.
12. The diagnosis assistant device of claim 11, wherein the diagnosis assistant information is not provided when the obtained quality grade information is the second quality grade.
1. A diagnosis assistant device for assisting diagnosis of a plurality of clinical conditions based on an eye image, comprising:
A diagnosis assistant system for assisting diagnosis of a plurality of diseases based on an eye image, comprising:
a storage unit; and
at least one processor operably connected to the storage unit and configured to:
obtain a first result associated with a first clinical condition based on a first target eye image using a first machine learning model, and a second result associated with a second clinical condition based on a second target eye image using a second machine learning model, wherein the first target eye image and the second target eye image are processed images of the target eye image, and
generate diagnosis assistant information based on at least one of the first result or the second result,
wherein at least part of the first machine learning model is different from the second machine learning model,
wherein the first result is used to assist in diagnosing the first clinical condition, and the second result is used to assist in diagnosing the second clinical condition, and
wherein the first clinical condition and the second clinical condition are different clinical conditions each other,
wherein the at least one processor is configured to obtain grade information including first grade or second grade based on the first result and the second result,
wherein the first grade indicates a higher risk for at least one of the first clinical condition or the second clinical condition than the second grade,
wherein the diagnosis assistant information comprises first guide information for treating at least one of the first clinical condition or the second clinical condition when the obtained grade information is the first grade, and
wherein the at least one processor is configured to provide the first guide information when the obtained grade information is the first grade.
an eye image obtaining unit configured to acquire a target eye image which is a basis for acquiring diagnosis assistance information on a subject;
a first processing unit configured to, for the target eye image, obtain a first result related to a first disease of the subject using a first machine learning model;
a second processing unit configured to, for the target eye image, obtain a second result related to a second disease of the subject using a second machine learning model, wherein at least part of the first machine learning model is different from the second machine learning model;
2. The diagnosis assistant system of claim 1, wherein the eye image includes at least one vessel of the eye of the subject.
3. The diagnosis assistant system of claim 1, wherein the eye image includes a retinal image or a fundus image.
4. The diagnosis assistant system of claim 1, wherein:
the first machine learning model comprises a first neural network model, and
the second machine learning model comprises a second neural network model.
5. The diagnosis assistant system of claim 1, wherein:
the first machine learning model is trained to classify an input eye image into one of normal label and an abnormal label regarding the first disease, and
the first processing unit obtains the first result by classifying the target eye image into one of the normal label or the abnormal label.
6. The diagnosis assistant system of claim 1, wherein the first processing unit obtains a first map related to the first result via the first machine learning model and the diagnostic information output unit outputs an image of the first map.
7. The diagnosis assistant system of claim 1, further comprising:
a third processing unit configured to obtain a quality information of the target eye image, and
wherein the diagnostic information output unit outputs the quality information of the target fundus image obtained by the third processing unit.
13. The diagnosis assistant device of claim 11, wherein the at least one processor is configured to require a new eye image when the obtained quality grade information is the second quality grade.
.
Allowable Subject Matter
Claims 1-7, 9-13, 15-16 and 18-19 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and Double Patenting rejections set forth in this Office action.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673 January 28, 2026