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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) correlating data relating to movement of a patients eye to a neurological condition and determining based on the correlation, a level of progression of the neurological condition. Such limitations constitute evaluations and judgements that fall within the mental process grouping of abstract ideas. Claims 9 and 18 additionally recited planning a treatment based on the determined neurological information, which likewise constitutes an evaluation or judgement. This judicial exception is not integrated into a practical application because the additional element do not meaningfully limit the abstract idea. Displaying a test target, capturing eye movement with an image capture device, and performing the recited fixation, smooth-pursuit, and saccade measurements merely obtain data used in the neurological evaluation. The processor, machine learning model, and computer program product merely implement the evaluation using computer technology, and producing objective information or a treatment parameter merely outputs the result of the evaluation. The claims do not recite an improvement to the functioning of the computer, machine learning model, or eye tracking technology, and the treatment limitations do not affirmatively effect a particular treatment. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. The display, image capture device, and recited eye movement tests merely perform data gathering functions; the processor, machine learning model, and computer readable program code are used as tools to perform the claimed evaluation; and the output limitations merely provide the result of the evaluation. These elements are not recited in an unconventional order arrangement or in a manner that improves the operation of the underlying technology. Rather, they apply the abstract evaluation using ordinary computer and eye tracking functionality. The applicants own disclosure says the computer readable code may be used by a computer system in accordance with conventional processes.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 10-16 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over De Villers-Sidani et al. (US 2022/0369923 A1) herein after De Villers
Regarding claim 1, De Villers discloses a method of managing a neurological condition ([0005] discloses: method for detecting a neurological disease), the method comprising:
displaying a test target ([0009] discloses: displaying a sequence of targets on the screen);
detecting, by an image capture device, movement of a patient eye with respect to the test target ([0008] discloses: detecting movement of at least one eye of the users face; [0010] discloses: camera for generating video of the users face, for detecting a neurological disease);
receiving data relating to the movement of the patient eye as an input to a machine learning model ([0467] discloses: input vectors, to a machine learning system, to detect presence of neurological condition, via eye movement);
correlating the data relating to the movement of the patient eye with respect to the test target to a neurological condition ([0010] discloses: set of tasks including a fixation task, a pro-saccade task etc., to detect progression of neurological disease); and
producing objective output information by the machine learning model based on the correlation of the data to the neurological condition indicating a level of progression of the neurological condition (Figures 37A and 37B depict: method for detecting neurological disease including using ML model to predict a neurological disease; thus producing objective output information by the ML model, based on data correlation to determine a neurological condition).
Regarding claim 2, De Villers discloses the method of claim 1, wherein the detecting movement is performed during one or more eye movement tests ([0010]-[0015] discloses: displaying stimulus videos corresponding to multiple eye movement tasks while simultaneously recording the patients face/eye, including fixation, pro-saccade, anti-saccade, smooth pursuit etc.).
Regarding claim 3, De Villers discloses the method of claim 2, wherein the one or more eye movement tests comprises one or more of a fixation test or a smooth pursuit test ([0011] discloses: including fixation, pro-saccade, anti-saccade, smooth pursuit etc.).
Regarding claim 4, De Villers discloses the method of claim 3, wherein the fixation test further comprises displaying the test target at one or more locations and detecting a latency, duration, peak amplitude, or peak velocity of the movement of the patient eye ([0270-0289] discloses: nine point fixation task, at multiple locations and detecting saccade intrusions during the fixation periods; [0009] discloses: detecting a saccade latency; thus the fixation test, uses saccade intrusions to detect a saccade latency).
Regarding claim 5, De Villers discloses the method of claim 4, further comprising detecting a catch-up saccade of the movement of the patient eye ([0506] discloses: when a smooth pursuit is initiated, a saccadic movement occurs to allow the gaze to catch up to the target, after change in velocity, the eye performs that saccade before resuming pursuit).
Regarding claim 6, De Villers discloses the method of claim 2, wherein the smooth pursuit test comprises moving the test target along a trajectory on the display and detecting the movement of the patient eye following the trajectory (claim 1 discloses: displaying a target in a sequence on the screen following a predetermined continuous path prompting the user to follow the movement of the target, generating gaze predictions from each task to detect a neurological disease using a pre-trained machine learning model).
Regarding claim 7, De Villers discloses the method of claim 1, further comprising training a machine learning model using the data relating to the movement of the patient eye during the eye movement test ([0227] discloses: once data is captured, a machine learning algorithm is chosen with which calibration models are trained; [0010] discloses: using eye gaze patterns to detect neurological diseases; thus training a machine learning model using the data relating to the movement of the patient eye during the eye movement test).
Regarding claim 10, De Villers discloses a system, comprising:
an image capture device configured to detect movement of a patient eye with respect to a displayed test target ([0014] discloses: device including at least a screen, camera and targets to detect gaze pattern abnormality); and
a processor in communication with the image capture device ([0016] discloses: processing unit for system), the processor configured to receive, from the image capture device, data relating to the movement of the patient eye as an input to a machine learning model ([0016] discloses: processing unit, using data and a MLM to detect a neurological disease, See Figure 37A), correlate the data relating to the movement of the patient eye with respect to the test target to a neurological condition (Figures 37A and 37B depict: using eye gaze features associated with eye movement tasks and supplies those features to a pretrained MLM to detect a neurological disease), and produce objective output information by the machine learning model based on the correlation of the data to the neurological condition indicating a level of progression of the neurological condition (Figures 37A-37C depict: using output information by MLM based on correlated data to indicate neurological disease and progression; [0010] discloses: set of tasks including a fixation task, a pro-saccade task etc., to detect progression of neurological disease).
Regarding claim 11, De Villers discloses the system of claim 10, wherein the image capture device detects movement during one or more eye movement tests ([0010]-[0015] discloses: displaying stimulus videos corresponding to multiple eye movement tasks while simultaneously recording the patients face/eye, including fixation, pro-saccade, anti-saccade, smooth pursuit etc.).
Regarding claim 12, De Villers discloses the system of claim 11, wherein the one or more eye movement tests comprises one or more of a fixation test or a smooth pursuit test ([0011] discloses: including fixation, pro-saccade, anti-saccade, smooth pursuit etc.).
Regarding claim 13, De Villers discloses the system of claim 12, wherein the fixation test further comprises displaying the test target on the display at one or more locations on the display and detecting a latency, duration, peak amplitude, or peak velocity of the movement of the patient eye ([0270-0289] discloses: nine point fixation task, at multiple locations and detecting saccade intrusions during the fixation periods; [0009] discloses: detecting a saccade latency; thus the fixation test, uses saccade intrusions to detect a saccade latency).
Regarding claim 14, De Villers discloses the system of claim 13, wherein the image capture device detects a catch-up saccade of the movement of the patient eye ([0506] discloses: when a smooth pursuit is initiated, a saccadic movement occurs to allow the gaze to catch up to the target, after change in velocity, the eye performs that saccade before resuming pursuit).
Regarding claim 15, De Villers discloses the system of claim 12, wherein the smooth pursuit test comprises moving the test target along a trajectory on the display and detecting the movement of the patient eye following the trajectory (claim 1 discloses: displaying a target in a sequence on the screen following a predetermined continuous path prompting the user to follow the movement of the target, generating gaze predictions from each task to detect a neurological disease using a pre-trained machine learning model).
Regarding claim 16, De Villers discloses the system of claim 10, wherein the processor is further configured to train a machine learning model using the data relating to the movement of the patient eye during the eye movement test ([0227] discloses: once data is captured, a machine learning algorithm is chosen with which calibration models are trained; [0010] discloses: using eye gaze patterns to detect neurological diseases; thus training a machine learning model using the data relating to the movement of the patient eye during the eye movement test).
Regarding claim 19, De Villers discloses a computer program product for use on a computer system, the computer program product comprising a tangible, non-transient computer usable medium having computer readable program code thereon ([0016] discloses: non-transitory computer readable medium with computer executable instructions stored thereon, thus computer readable program code), the computer readable program code comprising:
program code for displaying a test target ([0009] discloses: sequence of targets on the screen including pro-saccade and other eye movement tasks);
program code for receiving data relating to a movement of a patient eye with respect to the test target as an input to a machine learning model (Figure 37A depicts: using gave and extracts features and supplies to a pretrained MLM);
program code for correlating the data relating to the movement of the patient eye with respect to the test target to a neurological condition ([0010] discloses: set of tasks including a fixation task, a pro-saccade task etc., to detect progression of neurological disease); and
program code for producing objective output information by the machine learning model based on the correlation of the data to the neurological condition indicating a level of progression of the neurological condition (Figures 37A-37C depict: a pretrained MLM predicts/determines a neurological disease and its progression; thus considered producing objective output information).
Claims 8-9, 17-18 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over De Villers-Sidani et al. (US 2022/0369923 A1) in view of Kupermann et al. (US 2022/0180993).
Regarding claim 8, De Villers discloses the method of claim 7.
De Villers fails to disclose a method further comprising outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. De Villers and Kupermann are related because both disclose methods for managing neurological diseases.
Kupermann teaches a method further comprising outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition ([0017] teaches: using eye movement to generate personalized dose-response profile, based on neurological diseases and conditions; [0055] teaches: data including progression of disease).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified De Villers in view of Kupermann and provide a method further comprising outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. Doing so would allow for neurological information determined from the patients eye movement data to be used to select or adjust patient specific treatment thereby improving management of the neurological condition.
Regarding claim 9, De Villers discloses the method of claim 1.
De Villers fails to disclose a method further comprising planning a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition. De Villers and Kupermann are related because both disclose methods for managing neurological diseases.
Kupermann teaches a method further comprising planning a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition ([0017] teaches: using eye movement to generate personalized dose-response profile, based on neurological diseases and conditions; [0055] teaches: data including progression of disease).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified De Villers in view of Kupermann and provide a method further comprising planning a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition. Doing so would allow for neurological information determined from the patients eye movement data to be used to select or adjust patient specific treatment thereby improving management of the neurological condition.
Regarding claim 17, De Villers discloses the system of claim 16.
De Villers fails to disclose a system wherein the processor is further configured to output a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. De Villers and Kupermann are related because both disclose methods for managing neurological diseases.
Kupermann teaches a system wherein the processor is further configured to output a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition ([0017] teaches: using eye movement to generate personalized dose-response profile, based on neurological diseases and conditions; [0055] teaches: data including progression of disease).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified De Villers in view of Kupermann and provide a system wherein the processor is further configured to output a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. Doing so would allow for neurological information determined from the patients eye movement data to be used to select or adjust patient specific treatment thereby improving management of the neurological condition.
Regarding claim 18, De Villers discloses the system of claim 10.
De Villers fails to disclose a system wherein the processor is further configured to output a plan for a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition. De Villers and Kupermann are related because both disclose methods for managing neurological diseases.
Kupermann teaches a system wherein the processor is further configured to output a plan for a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition ([0017] teaches: using eye movement to generate personalized dose-response profile, based on neurological diseases and conditions; [0055] teaches: data including progression of disease).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified De Villers in view of Kupermann and provide a system wherein the processor is further configured to output a plan for a treatment for the neurological disorder based on the correlation of the data to the neurological condition indicating the level of progression of the neurological condition. Doing so would allow for neurological information determined from the patients eye movement data to be used to select or adjust patient specific treatment thereby improving management of the neurological condition.
Regarding claim 20, De Villers discloses the computer program product of claim 19, comprising program code ([0016] discloses: non-transitory computer readable medium with computer executable instructions stored thereon, thus computer readable program code).
De Villers fails to disclose a computer product for outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. Villers and Kupermann are related because both disclose methods for managing neurological diseases.
Kupermann teaches a computer product for outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition ([0017] teaches: using eye movement to generate personalized dose-response profile, based on neurological diseases and conditions; [0055] teaches: data including progression of disease).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified De Villers in view of Kupermann and provide a computer product for outputting a patient treatment parameter based upon the correlation of the data relating to the movement of the patient eye to a neurological condition. Doing so would allow for neurological information determined from the patients eye movement data to be used to select or adjust patient specific treatment thereby improving management of the neurological condition.
Compact Prosecution
To overcome the prior art, Applicant may consider amending the independent claims, if supported by the specification to further recite the disclosed Volterra-Laguerre modeling of smooth-pursuit eye movement, including use of complex value horizontal and vertical target/gaze components and complex valued coefficients representing cross-coupling between horizontal and vertical eye movements.
Such limitations do not appear to be taught or suggested by the presently applied prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Sahin (US 2015/0223731 A1) and Mansouri et al. (US 2022/0361749 A1) both disclose relevant optical systems.
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John Sipes
Examiner
Art Unit 2872
/J.C.S./Examiner, Art Unit 2872
/BUMSUK WON/Supervisory Patent Examiner, Art Unit 2872
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