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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 30 June 2026 has been entered.
Response to Amendment
The amendment filed 30 June 2026 has been entered. Claims 1-20 are acknowledged as pending with claims 1, 8, and 15 being currently amended. Applicant’s amendments to the claims have overcome all of the rejections under 35 U.S.C. 103 previously set forth in the Office Action mailed 30 March 2026.
Response to Arguments
Applicant’s arguments with respect to the rejections under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Botrashvili (US 2022/0117548) in view of Kapoula (WO 2022/074130), further in view of Yano (US 11,141,059).
Regarding claim 1, Botrashvili teaches a method (Abstract), comprising:
tracking, with a camera (para. 0046), eye movements of a human (para. 0033) as the human reads text presented by a display (paras. 0062 and 0030);
collecting eye movement data (para. 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; para. 0062);
analyzing eye movement data (para. 0030 wherein the AI module compares the transmitted data to pre-stored big data; para. 0062);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (para. 0030, wherein the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; para. 0036 and 0065);
when an impairment is indicated, adjusting a parameter (para. 0045) of the display (shown in Figs. 2-3);
collecting additional eye movement data after the adjusting (para. 0028, “and subsequently monitoring individual's eye reaction (such as the eyes’ movement) to such modification(s)”; para. 0030);
comparing the additional eye movement data with eye movements of the user without an impairment (para. 0028, “if the individual's attentiveness level is within a predefined threshold range, no adjustments are needed; and if the individual's attentiveness level is outside said predefined threshold range, specific conditions are adjusted until the individual's attentiveness level is determined to be within said predefined threshold range”; paras. 0030, wherein the AI module compares transmitted data to pre-stored big data (pre-stored big data is being construed as normal or healthy eye movement/data) to determine the individual’s attentiveness level; para. 0036, wherein the data is received from the camera that captures an individual’s eye movements; Claims 7-9); and
repeating the adjusting and comparing until the additional eye movement data is determined to be similar to the eye movements of the user without an impairment (paras. 0028, 0031 and 0058-0063, wherein the steps of monitoring the eyes and modifying the visual presentation are repeated constantly until the system identifies the individuals attentiveness/focus level is back in the desired range; Claim 7).
While Botrashvili teaches eye movement data, Botrashvili fails to specifically teach Irlen Syndrome and analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome, wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines, rereading, or lateral eye movement greater than corresponding eye movements of the user without Irlen Syndrome; based on the pattern stability, determining whether or not Irlen Syndrome is indicated by the eye movement data
Kapoula teaches an analogous system and method for obtaining, analyzing, and comparing eye data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without an impairment (page 27, lines 4-22; page 51, lines 19-27; Fig. 5A; page 52, Table 3, wherein the ‘Healthy individual’ and ‘dyslexic individual’ reading parameters where compared), wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines (page 53, lines 5-6, “reading mistakes per word where higher (0.0819 in dyslexic on average, versus 0.0319 in healthy individuals)”), rereading, or lateral eye movement greater than corresponding eye movements of the user without an impairment (Figs. 5A-5B; page 27, lines 4-27; page 53, lines 15-24);
based on the pattern stability, determining whether or not an impairment is indicated by the eye movement data (page 53, lines 1-9; page 53, Conclusions, “The above data shows that dyslexic individuals have troubles when reading, as well as saccade abnormalities”).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Botrashvili with the analysis of pattern stability based on eye movement data of Kapoula. Utilizing well-established biologic descriptors of eye movement from the field of neurology may help clinicians identify adolescents with visual impairments based on their eye movement patterns during reading exercises (Kapoula, page 99, lines 19-33 and page 100, lines 1-15).
While Botrashvili, in view of Kapoula, teaches a pattern stability, and individually, Botrashvili further teaches visual stress (para. 0065) and Kapoula further teaches ocular motor abnormalities and learning disorders (Abstract), the combination of Botrashvili and Kapoula fails to specifically teach Irlen Syndrome.
Yano teaches an analogous method for identifying and compensating for Irlen Syndrome (Col. 7, lines 47-67; Col. 6, lines 31-39).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the method of Botrashvili in view of Kapoula with the detection and remedy for Irlen syndrome of Yano. By directly identifying Irlen Syndrome, the appropriate color corrections could be made to improve the user’s vision and conceivably, the user’s literacy (Yano, Abstract; Col. 3, lines 54-67 and Col. 4, lines 1-17; Col. 5, lines 55-57).
Regarding claim 2, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paras. 0030, 0065, and 0067; Figs. 2-3) when Irlen Syndrome is indicated (Yano, Col. 13, lines 15-24 and 49-54 where if the subject 300 has Irlen Syndrome, the measurement value of Bm is smaller; subsequentially, the blue color image signal is adjusted).
Regarding claim 3, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the camera and the display are elements of a computing device (Botrashvili, Figure 1C, camera and display are shown).
Regarding claim 4, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the parameter of the display is a background color (Botrashvili, paras. 0029 and 0065) against which the text is displayed (Botrashvili, para. 0067 and Figure 2).
Regarding claim 5, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the determining is performed without use of any input consciously provided by the human (Botrashvili, para. 0030, where the AI module compares transmitted data and determines whether modifications in the display screen are needed).
Regarding claim 6, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the determining is performed using only input that is reflexively provided by the human (Botrashvili, para. 0046, wherein eye movements may be used by the software forming part of the processing system; para. 0028).
Regarding claim 7, Botrashvili in view of Kapoula, further in view of Yano teaches the method according to claim 1 as stated above wherein the determining that Irlen Syndrome (Yano, Col. 6, lines 31-38 and Figure 4) is presented, or not, is performed automatically (Botrashvili, para. 0028; see claims 1, 5, and 7).
Regarding claim 8, Botrashvili teaches a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising (paras. 0030 and 0062-0064):
tracking, with a camera (para. 0046), eye movements of a human (para. 0033) as the human reads text presented by a display (paras. 0062 and 0030);
collecting eye movement data (para. 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; para. 0062);
analyzing eye movement data (para. 0030 wherein the AI module compares the transmitted data to pre-stored big data; para. 0062);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (para. 0030, wherein the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; para. 0036 and 0065);
when an impairment is indicated, adjusting a parameter (para. 0045) of the display (shown in Figs. 2-3);
collecting additional eye movement data after the adjusting (para. 0028, “and subsequently monitoring individual's eye reaction (such as the eyes’ movement) to such modification(s)”; para. 0030);
comparing the additional eye movement data with eye movements of the user without an impairment (para. 0028, “if the individual's attentiveness level is within a predefined threshold range, no adjustments are needed; and if the individual's attentiveness level is outside said predefined threshold range, specific conditions are adjusted until the individual's attentiveness level is determined to be within said predefined threshold range”; paras. 0030, wherein the AI module compares transmitted data to pre-stored big data (pre-stored big data is being construed as normal or healthy eye movement/data) to determine the individual’s attentiveness level; para. 0036, wherein the data is received from the camera that captures an individual’s eye movements; Claims 7-9); and
repeating the adjusting and comparing until the additional eye movement data is determined to be similar to the eye movements of the user without an impairment (paras. 0028, 0031 and 0058-0063, wherein the steps of monitoring the eyes and modifying the visual presentation are repeated constantly until the system identifies the individuals attentiveness/focus level is back in the desired range; Claim 7).
While Botrashvili teaches eye movement data, Botrashvili fails to specifically teach Irlen Syndrome and analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome, wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines, rereading, or lateral eye movement greater than corresponding eye movements of the user without Irlen Syndrome; based on the pattern stability, determining whether or not Irlen Syndrome is indicated by the eye movement data
Kapoula teaches an analogous system and method for obtaining, analyzing, and comparing eye data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without an impairment (page 27, lines 4-22; page 51, lines 19-27; Fig. 5A; page 52, Table 3, wherein the ‘Healthy individual’ and ‘dyslexic individual’ reading parameters where compared), wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines (page 53, lines 5-6, “reading mistakes per word where higher (0.0819 in dyslexic on average, versus 0.0319 in healthy individuals)”), rereading, or lateral eye movement greater than corresponding eye movements of the user without an impairment (Figs. 5A-5B; page 27, lines 4-27; page 53, lines 15-24);
based on the pattern stability, determining whether or not an impairment is indicated by the eye movement data (page 53, lines 1-9; page 53, Conclusions, “The above data shows that dyslexic individuals have troubles when reading, as well as saccade abnormalities”).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Botrashvili with the analysis of pattern stability based on eye movement data of Kapoula. Utilizing well-established biologic descriptors of eye movement from the field of neurology may help clinicians identify adolescents with visual impairments based on their eye movement patterns during reading exercises (Kapoula, page 99, lines 19-33 and page 100, lines 1-15).
While Botrashvili, in view of Kapoula, teaches a pattern stability, and individually, Botrashvili further teaches visual stress (para. 0065) and Kapoula further teaches ocular motor abnormalities and learning disorders (Abstract), the combination of Botrashvili and Kapoula fails to specifically teach Irlen Syndrome.
Yano teaches an analogous non-transitory storage medium for identifying and compensating for Irlen Syndrome (Col. 7, lines 47-67; Col. 6, lines 31-39).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the non-transitory storage medium of Botrashvili in view of Kapoula with the detection and remedy for Irlen syndrome of Yano. By directly identifying Irlen Syndrome, the appropriate color corrections could be made to improve the user’s vision and conceivably, the user’s literacy (Yano, Abstract; Col. 3, lines 54-67 and Col. 4, lines 1-17; Col. 5, lines 55-57).
Regarding claim 9, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paras. 0030, 0065, and 0067; Figs. 2-3) when Irlen Syndrome is indicated (Yano, Col. 13, lines 15-24 and 49-54 where if the subject 300 has Irlen Syndrome, the measurement value of Bm is smaller; subsequentially, the blue color image signal is adjusted).
Regarding claim 10, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the camera and the display are elements of a computing device (Botrashvili, Figure 1C, camera and display are shown).
Regarding claim 11, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the parameter of the display is a background color (Botrashvili, paras. 0029 and 0065) against which the text is displayed (Botrashvili, para. 0067 and Figure 2).
Regarding claim 12, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the determining is performed without use of any input consciously provided by the human (Botrashvili, para. 0030, where the AI module compares transmitted data and determines whether modifications in the display screen are needed).
Regarding claim 13, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the determining is performed using only input that is reflexively provided by the human (Botrashvili, para. 0046, wherein eye movements may be used by the software forming part of the processing system; para. 0028).
Regarding claim 14, Botrashvili in view of Kapoula, further in view of Yano teaches the non-transitory storage medium according to claim 8 as stated above wherein the determining that Irlen Syndrome (Yano, Col. 6, lines 31-38 and Figure 4) is presented, or not, is performed automatically (Botrashvili, para. 0028; see claims 1, 5, and 7).
Regarding claim 15, Botrashvili teaches a system (para. 0068), comprising:
one or more hardware processors (para. 0030); and
a non-transitory storage medium having stored therein instructions that are executable by the one or more hardware processors to perform operations comprising (para. 0030, “memory”):
tracking, with a camera (para. 0046), eye movements of a human (para. 0033) as the human reads text presented by a display (paras. 0062 and 0030);
collecting eye movement data (para. 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; para. 0062);
analyzing eye movement data (para. 0030 wherein the AI module compares the transmitted data to pre-stored big data; para. 0062);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (para. 0030, wherein the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; para. 0036 and 0065);
when an impairment is indicated, adjusting a parameter (para. 0045) of the display (shown in Figs. 2-3);
collecting additional eye movement data after the adjusting (para. 0028, “and subsequently monitoring individual's eye reaction (such as the eyes’ movement) to such modification(s)”; para. 0030);
comparing the additional eye movement data with eye movements of the user without an impairment (para. 0028, “if the individual's attentiveness level is within a predefined threshold range, no adjustments are needed; and if the individual's attentiveness level is outside said predefined threshold range, specific conditions are adjusted until the individual's attentiveness level is determined to be within said predefined threshold range”; paras. 0030, wherein the AI module compares transmitted data to pre-stored big data (pre-stored big data is being construed as normal or healthy eye movement/data) to determine the individual’s attentiveness level; para. 0036, wherein the data is received from the camera that captures an individual’s eye movements; Claims 7-9); and
repeating the adjusting and comparing until the additional eye movement data is determined to be similar to the eye movements of the user without an impairment (paras. 0028, 0031 and 0058-0063, wherein the steps of monitoring the eyes and modifying the visual presentation are repeated constantly until the system identifies the individuals attentiveness/focus level is back in the desired range; Claim 7).
While Botrashvili teaches eye movement data, Botrashvili fails to specifically teach Irlen Syndrome and analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome, wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines, rereading, or lateral eye movement greater than corresponding eye movements of the user without Irlen Syndrome; based on the pattern stability, determining whether or not Irlen Syndrome is indicated by the eye movement data
Kapoula teaches an analogous system and method for obtaining, analyzing, and comparing eye data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without an impairment (page 27, lines 4-22; page 51, lines 19-27; Fig. 5A; page 52, Table 3, wherein the ‘Healthy individual’ and ‘dyslexic individual’ reading parameters where compared), wherein the pattern stability comprises determining whether the eye movement data indicates one or more of skipping words, skipping lines (page 53, lines 5-6, “reading mistakes per word where higher (0.0819 in dyslexic on average, versus 0.0319 in healthy individuals)”), rereading, or lateral eye movement greater than corresponding eye movements of the user without an impairment (Figs. 5A-5B; page 27, lines 4-27; page 53, lines 15-24);
based on the pattern stability, determining whether or not an impairment is indicated by the eye movement data (page 53, lines 1-9; page 53, Conclusions, “The above data shows that dyslexic individuals have troubles when reading, as well as saccade abnormalities”).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Botrashvili with the analysis of pattern stability based on eye movement data of Kapoula. Utilizing well-established biologic descriptors of eye movement from the field of neurology may help clinicians identify adolescents with visual impairments based on their eye movement patterns during reading exercises (Kapoula, page 99, lines 19-33 and page 100, lines 1-15).
While Botrashvili, in view of Kapoula, teaches a pattern stability, and individually, Botrashvili further teaches visual stress (para. 0065) and Kapoula further teaches ocular motor abnormalities and learning disorders (Abstract), the combination of Botrashvili and Kapoula fails to specifically teach Irlen Syndrome.
Yano teaches an analogous system for identifying and compensating for Irlen Syndrome (Col. 7, lines 47-67; Col. 6, lines 31-39).
Therefore, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the system of Botrashvili in view of Kapoula with the detection and remedy for Irlen syndrome of Yano. By directly identifying Irlen Syndrome, the appropriate color corrections could be made to improve the user’s vision and conceivably, the user’s literacy (Yano, Abstract; Col. 3, lines 54-67 and Col. 4, lines 1-17; Col. 5, lines 55-57).
Regarding claim 16, Botrashvili in view of Kapoula, further in view of Yano teaches the system according to claim 15 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paras. 0030, 0065, and 0067; Figs. 2-3) when Irlen Syndrome is indicated (Yano, Col. 13, lines 15-24 and 49-54 where if the subject 300 has Irlen Syndrome, the measurement value of Bm is smaller; subsequentially, the blue color image signal is adjusted).
Regarding claim 17, Botrashvili in view of Kapoula, further in view of Yano teaches the system according to claim 15 as stated above wherein the system comprises a computing device that includes the camera and the display (Botrashvili, Figure 1C, camera and display are shown).
Regarding claim 18, Botrashvili in view of Kapoula, further in view of Yano teaches the system according to claim 15 as stated above wherein the parameter of the display is a background color (Botrashvili, paras. 0029 and 0065) against which the text is displayed (Botrashvili, para. 0067 and Figure 2).
Regarding claim 19, Botrashvili in view of Kapoula, further in view of Yano teaches the system according to claim 15 as stated above wherein the determining is performed using only input that is reflexively provided by the human (Botrashvili, para. 0046, wherein eye movements may be used by the software forming part of the processing system; para. 0028).
Regarding claim 20, Botrashvili in view of Kapoula, further in view of Yano teaches the system according to claim 15 as stated above wherein the determining that Irlen Syndrome (Yano, Col. 6, lines 31-38 and Figure 4) is presented, or not, is performed automatically (Botrashvili, para. 0028; see claims 1, 5, and 7).
Conclusion
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/B.R.L./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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