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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/28/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 6-8, 10, and 13-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “to classify the subject’s expected amount of change in fatigue state” in line 18, but is indefinite. Is the expected amount of change the same as “a value representing a level of change” or a different change value? Are both values predicted future values? Clarification required. Same issue is seen in Claims 8, 13, 14, and 15.
Claims not listed are rejected by virtue of claim dependency.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 3, 6-8, 10, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Samec et al. (US 20170365101-Previously cited), hereinafter Samec, in view of Craf et al. (US 20200210859), hereinafter Craf, and Bar et al. (US 20170311860), hereinafter Bar.
Regarding claim 7, Samec teaches a device and computer-implemented method (abstract and ¶[0863]), comprising:
obtaining an automated prediction of a change of a state of fatigue of said subject carrying out a visual task (¶[0404,0499,0545], “detecting responses to guided imagery and/or audio presented to the user 60 at a display 62 and/or speakers 66. With reference to FIG. 10, the system 2010 may monitor the user through inward facing cameras 24 for eye tracking, such as to detect eye position, movement, or gaze”) involving any king of visual content, by:
displaying, on a display screen (¶[0545]), at least one question related to a vision of said subject, the answer to the at least one question being at least one subjective measurement relating to said subject measuring, via a camera, at least one pupillary and/or gaze measurement made on said subject at a same time of displaying the at least one question on the display screen, the measurement of the at least one pupillary and/or gaze measurement being at least one (Samec teaches a display system for performing a user’s level of fatigue, i.e. alertness, based on measuring the user’s reaction to a stimulus (abstract and ¶[0544]). The fatigue level is then used in-part to detect injuries, conditions, or disorders affecting the mental state(¶[0544]). The display system is configured to monitor the user’s objective and subjective reactions to stimuli presented (¶[0488]). Objective responses include, “eye movements, neural signals, and autonomic responses,” and subjective responses include, “psychological or emotional reaction to stimuli” (¶[0488]). The method to detect the user’s reaction to the objective and subjective stimuli are indicative of the user's state of fatigue (¶[0547], “the method may detect a user reaction to the stimulus indicative of the user's state of alertness”). The stimuli can be presented via a display and speaker and detected using cameras and microphones ¶[0473,0542,0545-47,0594], “the microphone is configured to allow the user to provide inputs or commands to the system 60 (e.g., the selection of voice menu commands, natural language questions, etc.)” and “the systems and sensors described above may be used according to method 1700 for detection and/or diagnosis of mental states and/or neurological conditions related to a user's level of alertness” (emphasis added)). Moreover, the data is collected simultaneously to improve the analysis performed on the user to reveal relationships between the condition, treatment, and sensor measurements (¶[0411], “thereby allowing different data to be cross-referenced”),
providing a plurality of input data (¶[0546]), relating to said subject, to a fatigue state change predictive model, wherein said plurality of input data comprises at the least one subjective and objective measurement relating to said subject (¶[0865,0869], “One or more computer vision algorithms may be used” and “individual models may be customized for individual data sets . . . . base model may be used as a starting point to generate additional models specific to a data type”);
obtaining, by a processor implementing said model, a value representing change of said state of fatigue of said subject (¶[0404-09,0550], “provide corrective aids, e.g., perception aids, to help the user address shortcomings in their ability to perceive and/or interact with the world” and “perception aid for the user may include interesting content to increase the user's alertness.” That is, repeated analysis is performed to decrease the user’s fatigue, which requires obtaining a new fatigue value that indicates improvement or worsening). Samec fails to teach wherein the value represents a level of change, the level indicating a predetermined category, out a plurality of categories, to classify the subject's expected amount of change in fatigue state in response to carrying out the visual task.
Graf teaches a system and method that can predict the degradation of an asset, e.g. operator of a machine (visual task), based on the data sensed (abstract and ¶[0004]). The degradation can be determined using a predictive model that computes a changing attribute, based on sensor data, in time-series value over time (¶[0021,0041]). The changing attribute includes a change in times series values over time of the specific sensor data (¶[0020-21,0041], “An IIoT may connect assets, such as turbines, jet engines, locomotives, elevators, healthcare devices, mining equipment, oil and gas refineries, and the like, to the Internet or a cloud computing system, or to each other in some meaningful way such as through one or more networks. The cloud can be used to receive, relay, transmit, store, analyze, or otherwise process information for or about assets” (emphasis added)). The degradation value is representative of a score or a level indicating a predetermining category, e.g. letter score, healthy, detrimental, out of many categories, e.g. letter score, healthy, detrimental, to classify the predicted fatigue value in response to operating a type of machinery (¶[0020,0042]). Graf further discloses that predicting degradation, irrespective of whether it is fatigue, can be used to take preventative measures so that the subject can perform in a better physiological state (¶[0003-4]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Samec, such that a level of change, related to a predetermined category in response to a visual task, is obtained, as taught by Graf, to take preventative measures so that the subject can perform in a better physiological state.
Samec-Graf fail to teach adapting a personalized prescription of anti-fatigue lenses for the subject by linking said value to a level of near addition of the anti-fatigue lenses; providing said value, by the processor, to a data outputting unit,
generating and outputting information, by the data outputting unit, of an anti-fatigue optical article comprising said anti-fatigue lenses.
Bar teaches a method for monitoring the fatigue of a subject to provide recommendations for an optical article, e.g. near addition (abstract and ¶[0106, 0112]). Bar emphasizes that visual requirements and behavior of a person may change over time or depending on a situation, as such, it is important to measure the behavior and features related to vision to provide corrective lens information (¶[0004-6,0012-34], “the stimulus is a visual stimulus and/or an auditory stimulus” and “the vision information relates to a recommendation, for example a lens design recommendation and/or an ophthalmic lens recommendation, and/or an alert, for example an alert indicative of the vision state, and/or an activation of at least one functionality on a head mounted device”). The recommendations include generating and outputting information related to near addition based on the predicted level of visual fatigue, which is necessarily dependent on a change in the subjects visual features (¶[0104,0106,0114], “the dioptric function may be adapted by adapting the addition in the near zone to relieve the user from the visual fatigue”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the inventio was effectively filed to have modified the device of Samec-Graf, to adapt a personalized prescription for a subject due to a change in predicted fatigue and output that information, as taught by Bar, because visual requirements of a person change over time and/or based on the situation. The improvement leads to a device that relieve the user from visual fatigue not just in a limited setting, e.g. doctor’s office, but based on the specific situations the person finds themselves in (¶[0004-6] of Graf).
Regarding claims 3 and 10, Samec teaches wherein the said state of fatigue is a state of visual fatigue (see para. [0546], fatigue is calculated based on at least information from a user’s eyes, therefore it is visual fatigue).
Regarding claim 6, Samec teaches wherein it further comprises storing said model in the cloud and carrying out said obtaining in the cloud (¶[0475]).
Regarding claims 8, and 14-15, Samec teaches a device and computer-implemented method (abstract and ¶[0863]), comprising:
displaying, on a display screen (¶[0545]), at least one question related to a vision of said subject, the answer to the at least one question being at least one subjective measurement relating to said subject measuring, via a camera, at least one pupillary and/or gaze measurement made on said subject at a same time of displaying the at least one question on the display screen, the measurement of the at least one pupillary and/or gaze measurement being at least one (Samec teaches a display system for performing a user’s level of fatigue, i.e. alertness, based on measuring the user’s reaction to a stimulus (abstract and ¶[0544]). The fatigue level is then used in-part to detect injuries, conditions, or disorders affecting the mental state(¶[0544]). The display system is configured to monitor the user’s objective and subjective reactions to stimuli presented (¶[0488]). Objective responses include, “eye movements, neural signals, and autonomic responses,” and subjective responses include, “psychological or emotional reaction to stimuli” (¶[0488]). The method to detect the user’s reaction to the objective and subjective stimuli are indicative of the user's state of fatigue (¶[0547], “the method may detect a user reaction to the stimulus indicative of the user's state of alertness”). The stimuli can be presented via a display and speaker and detected using cameras and microphones ¶[0473,0542,0545-47,0594], “the microphone is configured to allow the user to provide inputs or commands to the system 60 (e.g., the selection of voice menu commands, natural language questions, etc.)” and “the systems and sensors described above may be used according to method 1700 for detection and/or diagnosis of mental states and/or neurological conditions related to a user's level of alertness” (emphasis added)). Moreover, the data is collected simultaneously to improve the analysis performed on the user to reveal relationships between the condition, treatment, and sensor measurements (¶[0411], “thereby allowing different data to be cross-referenced”),
providing a plurality of input data (¶[0546]), relating to said subject, to a fatigue state change predictive model, wherein said plurality of input data comprises at the least one subjective and objective measurement relating to said subject (¶[0865,0869], “One or more computer vision algorithms may be used” and “individual models may be customized for individual data sets . . . . base model may be used as a starting point to generate additional models specific to a data type”);
obtaining, by a processor implementing said model, a value representing change of said state of fatigue of said subject (¶[0404-09,0550], “provide corrective aids, e.g., perception aids, to help the user address shortcomings in their ability to perceive and/or interact with the world” and “perception aid for the user may include interesting content to increase the user's alertness.” That is, repeated analysis is performed to decrease the user’s fatigue, which requires obtaining a new fatigue value that indicates improvement or worsening),
and implementing a fatigue state change model based on the measurement input data and at least one other subject-related datum to obtain a value representing a change of said fatigue of said subject and input into the model (¶[0404-09,0550], “provide corrective aids, e.g., perception aids, to help the user address shortcomings in their ability to perceive and/or interact with the world” and “perception aid for the user may include interesting content to increase the user's alertness.” That is, repeated analysis is performed to decrease the user’s fatigue, which requires obtaining a new fatigue value that indicates improvement or worsening).
Samec fails to teach wherein the value represents a level of change, the level indicating a predetermined category, out a plurality of categories, to classify the subject's expected amount of change in fatigue state in response to carrying out the visual task.
Graf teaches a system and method that can predict the degradation of an asset, e.g. operator of a machine (visual task), based on the data sensed (abstract and ¶[0004]). The degradation can be determined using a predictive model that computes a changing attribute, based on sensor data, in time-series value over time (¶[0021,0041]). The changing attribute includes a change in times series values over time of the specific sensor data (¶[0020-21,0041], “An IIoT may connect assets, such as turbines, jet engines, locomotives, elevators, healthcare devices, mining equipment, oil and gas refineries, and the like, to the Internet or a cloud computing system, or to each other in some meaningful way such as through one or more networks. The cloud can be used to receive, relay, transmit, store, analyze, or otherwise process information for or about assets” (emphasis added)). The degradation value is representative of a score or a level indicating a predetermining category, e.g. letter score, healthy, detrimental, out of many categories, e.g. letter score, healthy, detrimental, to classify the predicted fatigue value in response to operating a type of machinery (¶[0020,0042]). Graf further discloses that predicting degradation, irrespective of whether it is fatigue, can be used to take preventative measures so that the subject can perform in a better physiological state (¶[0003-4]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Samec, such that a level of change, related to a predetermined category in response to a visual task, is obtained, as taught by Graf, to take preventative measures so that the subject can perform in a better physiological state.
Samec-Graf fail to teach adapting a personalized prescription of anti-fatigue lenses for the subject by linking said value to a level of near addition of the anti-fatigue lenses; providing said value, by the processor, to a data outputting unit,
generating and outputting information, by the data outputting unit, of an anti-fatigue optical article comprising said anti-fatigue lenses.
Bar teaches a method for monitoring the fatigue of a subject to provide recommendations for an optical article, e.g. near addition, based on a prediction model (abstract and ¶[0106, 0112-114]). Bar emphasizes that visual requirements and behavior of a person may change over time or depending on a situation, as such, it is important to measure the behavior and features related to vision to provide corrective lens information (¶[0004-6,0012-34], “the stimulus is a visual stimulus and/or an auditory stimulus” and “the vision information relates to a recommendation, for example a lens design recommendation and/or an ophthalmic lens recommendation, and/or an alert, for example an alert indicative of the vision state, and/or an activation of at least one functionality on a head mounted device”). The recommendations include generating and outputting information related to near addition based on the predicted level of visual fatigue, which is necessarily dependent on a change in the subjects visual features (¶[0104,0106,0114], “the dioptric function may be adapted by adapting the addition in the near zone to relieve the user from the visual fatigue”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the inventio was effectively filed to have modified the device of Samec-Graf, to adapt a personalized prescription, using a predictive model, for a subject due to a change in predicted fatigue and output that information, as taught by Bar, because visual requirements of a person change over time and/or based on the situation. The improvement leads to a device that relieve the user from visual fatigue not just in a limited setting, e.g. doctor’s office, but based on the specific situations the person finds themselves in (¶[0004-6] of Bar).
Regarding claim 13, Samec teaches a device that comprises at least one device for providing an automated prediction of a change of a state of fatigue of a subject carrying out a visual task involving any kind of visual content (abstract (¶[0404-09,0550,0863], “provide corrective aids, e.g., perception aids, to help the user address shortcomings in their ability to perceive and/or interact with the world” and “perception aid for the user may include interesting content to increase the user's alertness.” That is, repeated analysis is performed to decrease the user’s fatigue, which requires obtaining a new fatigue value that indicates improvement or worsening. As such, a prediction of a change in a worse or improved fatigue state is required.), comprising:
displaying, on a display screen (¶[0545]), at least one question related to a vision of said subject, the answer to the at least one question being at least one subjective measurement relating to said subject measuring, via a camera, at least one pupillary and/or gaze measurement made on said subject at a same time of displaying the at least one question on the display screen, the measurement of the at least one pupillary and/or gaze measurement being at least one (Samec teaches a display system for performing a user’s level of fatigue, i.e. alertness, based on measuring the user’s reaction to a stimulus (abstract and ¶[0544]). The fatigue level is then used in-part to detect injuries, conditions, or disorders affecting the mental state(¶[0544]). The display system is configured to monitor the user’s objective and subjective reactions to stimuli presented (¶[0488]). Objective responses include, “eye movements, neural signals, and autonomic responses,” and subjective responses include, “psychological or emotional reaction to stimuli” (¶[0488]). The method to detect the user’s reaction to the objective and subjective stimuli are indicative of the user's state of fatigue (¶[0547], “the method may detect a user reaction to the stimulus indicative of the user's state of alertness”). The stimuli can be presented via a display and speaker and detected using cameras and microphones, ¶[0473,0542,0545-47,0594], “the microphone is configured to allow the user to provide inputs or commands to the system 60 (e.g., the selection of voice menu commands, natural language questions, etc.)” and “the systems and sensors described above may be used according to method 1700 for detection and/or diagnosis of mental states and/or neurological conditions related to a user's level of alertness” (emphasis added)). Moreover, the data is collected simultaneously to improve the analysis performed on the user to reveal relationships between the condition, treatment, and sensor measurements (¶[0411], “thereby allowing different data to be cross-referenced”),
providing measurement input data (¶[0546]) comprising at least one subjective and objective measurement relating to said subject (¶[0865,0869], “One or more computer vision algorithms may be used” and “individual models may be customized for individual data sets . . . . base model may be used as a starting point to generate additional models specific to a data type”), and implementing a fatigue state change model based on the measurement input data and at least one other subject-related datum to obtain a value representing a change of said fatigue of said subject and input into the model (¶[0404-09,0550], “provide corrective aids, e.g., perception aids, to help the user address shortcomings in their ability to perceive and/or interact with the world” and “perception aid for the user may include interesting content to increase the user's alertness.” That is, repeated analysis is performed to decrease the user’s fatigue, which requires obtaining a new fatigue value that indicates improvement or worsening). Samec fails to teach wherein the value represents a level of change, the level indicating a predetermined category, out a plurality of categories, to classify the subject's expected amount of change in fatigue state in response to carrying out the visual task.
Graf teaches a system and method that can predict the degradation of an asset, e.g. operator of a machine (visual task), based on the data sensed (abstract and ¶[0004]). The degradation can be determined using a predictive model that computes a changing attribute, based on sensor data, in time-series value over time (¶[0021,0041]). The changing attribute includes a change in times series values over time of the specific sensor data (¶[0020-21,0041], “An IIoT may connect assets, such as turbines, jet engines, locomotives, elevators, healthcare devices, mining equipment, oil and gas refineries, and the like, to the Internet or a cloud computing system, or to each other in some meaningful way such as through one or more networks. The cloud can be used to receive, relay, transmit, store, analyze, or otherwise process information for or about assets” (emphasis added)). The degradation value is representative of a score or a level indicating a predetermining category, e.g. letter score, healthy, detrimental, out of many categories, e.g. letter score, healthy, detrimental, to classify the predicted fatigue value in response to operating a type of machinery (¶[0020,0042]). Graf further discloses that predicting degradation, irrespective of whether it is fatigue, can be used to take preventative measures so that the subject can perform in a better physiological state (¶[0003-4]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Samec, such that a level of change, related to a predetermined category in response to a visual task, is obtained, as taught by Graf, to take preventative measures so that the subject can perform in a better physiological state.
Samec-Graf fail to teach adapting a personalized prescription of anti-fatigue lenses for the subject by linking said value to a level of near addition of the anti-fatigue lenses; providing said value, by the processor, to a data outputting unit,
generating and outputting information, by the data outputting unit, of an anti-fatigue optical article comprising said anti-fatigue lenses.
Bar teaches a method for monitoring the fatigue of a subject to provide recommendations for an optical article, e.g. near addition (abstract and ¶[0106, 0112]). Bar emphasizes that visual requirements and behavior of a person may change over time or depending on a situation, as such, it is important to measure the behavior and features related to vision to provide corrective lens information (¶[0004-6,0012-34], “the stimulus is a visual stimulus and/or an auditory stimulus” and “the vision information relates to a recommendation, for example a lens design recommendation and/or an ophthalmic lens recommendation, and/or an alert, for example an alert indicative of the vision state, and/or an activation of at least one functionality on a head mounted device”). The recommendations include generating and outputting information related to near addition based on the predicted level of visual fatigue, which is necessarily dependent on a change in the subjects visual features (¶[0104,0106,0114], “the dioptric function may be adapted by adapting the addition in the near zone to relieve the user from the visual fatigue”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the inventio was effectively filed to have modified the device of Samec-Graf, to adapt a personalized prescription for a subject due to a change in predicted fatigue and output that information, as taught by Bar, because visual requirements of a person change over time and/or based on the situation. The improvement leads to a device that relieve the user from visual fatigue not just in a limited setting, e.g. doctor’s office, but based on the specific situations the person finds themselves in (¶[0004-6] of Graf).
Response to Arguments
Applicant's arguments filed 08/28/2026 have been fully considered but they are not fully persuasive.
Applicant’s arguments related to 35 U.S.C. 101 and 112(a)-(b) have been acknowledged and found persuasive. Thus, related rejections have been withdrawn.
Applicants arguments related to 35 U.S.C. 103 rejections have been acknowledged. However, amendments change the scope of the claims and require an updated rejection in view of Graf and Bar. Accordingly, Applicant’s arguments are moot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fujikado teaches qn eye-fatigue examining device and an eye-fatigue examining method capable of examining eye fatigue of a subject's eye regardless of age of a patient are provided. US 20190099076
Publicover teaches systems and methods are provided to measure reaction times and/or responses for head, eye, eyelid movements, and/or changes in pupil geometry. A low-cost, unobtrusive, portable platform that may repeatedly measure responses and reaction times has a wide range of applications. A small number of examples include monitoring the degree of fatigue of an individual, etc. US 20120293773
Allione teaches determining an updated visual correction need for designing a new vision correcting device of an individual already having a previous vision correcting device previously designed to correct his/her vision, wherein, in a first step, a previous level of correction of the previous vision correcting device is acquired, in a second step, a current vision acuity parameter of the individual wearing the previous vision correcting device is assessed, and, in a third step, the updated visual correction need is determined based on the previous level of correction of the previous vision correcting device acquired in the first step and on the current vision acuity parameter assessed in the second step. US 20190231185
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