Prosecution Insights
Last updated: April 19, 2026
Application No. 19/087,640

OBJECTIVE EVALUATION OF NEUROLOGICAL MOVEMENT DISORDERS FROM MEDICAL IMAGING

Non-Final OA §112
Filed
Mar 24, 2025
Examiner
SHENG, CHAO
Art Unit
3797
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
Massachusetts Eye And Ear Infirmary
OA Round
1 (Non-Final)
62%
Grant Probability
Moderate
1-2
OA Rounds
3y 4m
To Grant
91%
With Interview

Examiner Intelligence

Grants 62% of resolved cases
62%
Career Allow Rate
170 granted / 276 resolved
-8.4% vs TC avg
Strong +29% interview lift
Without
With
+29.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 4m
Avg Prosecution
32 currently pending
Career history
308
Total Applications
across all art units

Statute-Specific Performance

§101
2.7%
-37.3% vs TC avg
§103
46.8%
+6.8% vs TC avg
§102
16.5%
-23.5% vs TC avg
§112
31.4%
-8.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 276 resolved cases

Office Action

§112
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 . Claim Objections Claim 3, 4 and 6 – 8 are objected to because of the following informalities: Claim 3 line 1 – 2, limitation "wherein capturing a three-dimensional medical image of the brain of the patient comprises" should read "wherein said capturing a three-dimensional medical image of the brain of the patient comprises". Claim 4 line 1 – 2, limitation "wherein providing the three-dimensional medical image of the brain of the patient to the artificial neural network comprises" should read "wherein said providing the three-dimensional medical image of the brain of the patient to the artificial neural network comprises". Claim 6 line 1 – 2, limitation "wherein providing the treatment to the patient based on the clinical parameter comprises" should read "wherein said providing the treatment to the patient based on the clinical parameter comprises". Claim 7 line 2 – 3, limitation "a presence of the neurological movement disorder" should read "the presence of the neurological movement disorder". Claim 7 line 3 – 4, limitation "a specific treatment for the neurological movement disorder" should read "the specific treatment for the neurological movement disorder". Claim 8 line 1 – 2, limitation "wherein the three-dimensional medical image of the brain of the patient comprises capturing a first three-dimensional medical image" should read "wherein the three-dimensional medical image of the brain of the patient comprises a first three-dimensional medical image". Claim 8 line 7, limitation "a brain of the patient" should read "the brain of the patient". Claim 8 line 12 – 14, limitation "the one of a presence of the neurological movement disorder and a response of the patient to a specific treatment for the neurological movement disorder" should read "the one of the presence of the neurological movement disorder and the response of the patient to the specific treatment for the neurological movement disorder". 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. Claim 2 and 8 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 2 recites limitation "an artificial neural network" in line 1 – 2, it is unclear the above artificial neural network is a newly introduced different neural network, or the same “artificial neural network” introduced in claim 1 line 4 – 5. Thus, the above limitation renders claim indefinite. For the purpose of examination, the above limitation is interpreted as any reasonable neural network. Claim 8 recites limitation “a treatment” in line 6 and limitation “the treatment” in line 16, it is unclear the above “treatment” is a newly introduced different treatment or the same “specific treatment” introduced in claim 1 line 9. Thus, the above limitations render claim indefinite. For the purpose of examination, the above limitations are interpreted as any reasonable treatment. Allowable Subject Matter Claim 2 and 8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: Jain et al. (Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images, published online on 01/04/2019) (hereinafter “Jain”) and Shah et al. (Detection of Parkinson Disease in Brain MRI using Convolutional Neural Network; published in conference dated on 09/07/2018) (hereinafter “Shah”) are cited as most relevant prior arts to the claimed invention. Regarding independent claim 1, Jain, Shah and other search results collectively neither teach nor fairly well suggest a method for evaluating a patient for a neurological movement disorder comprising: “providing the three-dimensional medical image of the brain of the patient to an artificial neural network having at least one convolutional layer to provide a set of output values; and providing the set of output values to a machine learning model to provide a clinical parameter representing one of a presence of the neurological movement disorder in the patient and a response of the patient to a specific treatment for the neurological movement disorder”, in combination with other limitations as recited in claim 1. The claimed invention requires stacking of one convolutional neural network and another machine learning model. The 3D brain image is input into CNN first, then the output is feed into another learning model to generate clinical indication. The claimed method is specified for determining movement disorder. Although Jain teaches using CNN first then a classifier with transfer learning, such method is used to classify Alzheimer’s disease, which is not considered as a movement disorder like Parkinson’s disease. Shah teaches using CNN to classify Parkinson’s disease but the method only uses one CNN, and there is no teaching of two model stacking together. Thus, neither cited prior arts nor other search results in combination teach the invention as claimed. Claim 2 – 15 are either directly or indirectly dependent on claim 1 and therefore inherently include the allowable feature as discussed above. Regarding independent claim 16, Jain, Shah and other search results collectively neither teach nor fairly well suggest a method for diagnosing dystonia comprising: “providing the raw MR image of the brain of the patient to a convolutional neural network to provide a set of output values; and providing the set of output values to a machine learning model to provide a clinical parameter representing the presence of dystonia in the patient”, in combination with other limitations as recited in claim 16. The claimed invention requires stacking of one convolutional neural network and another machine learning model. The 3D brain image is input into CNN first, then the output is feed into another learning model to generate clinical indication. The claimed method is specified for determining dystonia. Although Jain teaches using CNN first then a classifier with transfer learning, such method is used to classify Alzheimer’s disease, which is not considered as a movement disorder like Parkinson’s disease. Shah teaches using CNN to classify Parkinson’s disease but the method only uses one CNN, and there is no teaching of two model stacking together. In addition, dystonia is a specific movement disorder, which is different from Parkinson’s disease. Thus, neither cited prior arts nor other search results in combination teach the invention as claimed. Claim 17 – 20 are either directly or indirectly dependent on claim 16 and therefore inherently include the allowable feature as discussed above. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vaillancourt (US 2020/0046281 A1; filed on 08/30/2018) teaches a method for diagnosis of Parkinson’s disease using AI model and dMRI scan. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAO SHENG whose telephone number is (571)272-8059. The examiner can normally be reached Monday to Friday, 8:30 am to 5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M. Kozak can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHAO SHENG/ Primary Examiner, Art Unit 3797
Read full office action

Prosecution Timeline

Mar 24, 2025
Application Filed
Feb 06, 2026
Non-Final Rejection — §112 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
62%
Grant Probability
91%
With Interview (+29.2%)
3y 4m
Median Time to Grant
Low
PTA Risk
Based on 276 resolved cases by this examiner. Grant probability derived from career allow rate.

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