DETAILED ACTION
This Office Action is in response to the amendment filed 02/23/2026. Claims 1-20 are acknowledged as pending with claims 1-2, 8-9, and 15-16 being currently amended.
The claim objections and the rejections under 35 U.S.C. 103 are withdrawn as having been overcome by the amendment. New rejections necessitated by the amendment are presented below.
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 Seassau (WO 2014/083281), citing to attached translation, further in view of Yano (US 11,141,059).
Regarding claim 1, Botrashvili teaches a method (abstract) comprising:
tracking, with a camera (paragraph 0046, lines 8-9), eye movement (paragraph 0033, lines 7-9) of a human as the human reads text (paragraph 0062, lines 6-8) presented by a display (paragraph 0030, lines 8-10);
collecting eye movement data (paragraph 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; paragraph 0062, lines 1-2);
analyzing eye movement data (paragraph 0030 where the AI module compares the transmitted data to pre-stored big data; paragraph 0062, lines 3-8);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (paragraph 0030 where the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; paragraph 0036, lines 6-9; paragraph 0065, lines 3-11); and
when an impairment is indicated, adjusting a parameter (paragraph 0045, lines 7-14) of the display (shown in Figs. 2-3).
While Botrashvili teaches analyzing eye movement data, Botrashvili fails to specifically disclose analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen syndrome: based on pattern stability, determining whether or not Irlen Syndrome is indicated.
Seassau teaches an analogous system and method for obtaining, analyzing, and comparing eye metric data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome (Fig. 5 depicts a recording of eye movements of a healthy (an individual without Irlen Syndrome) subject, and Fig. 6 illustrates a recording of eye movements of an individual with an impairment; page 7, para. 10, “a recording, in time, of the displacement of each eye in amplitude along the horizontal axis (degrees relative to each other)”; page 8, para. 10, “once the line returns and their return saccades identified, the processing module 17 determines and calculates in particular one or more return saccade parameters”);
based on the pattern stability (Figs. 5-6; page 9, paras. 6-7, “These monocular parameters based on single-eye recording 16 include: an average number of return saccades on a plurality of line breaks during the reading task…”), determining whether or not an impairment is indicated by the eye movement data (page 3, para. 13, “this method of data collection can be used in a method for detecting an oculomotor abnormality in a subject in which…the calculated [return saccade] parameter is compared with minus a reference threshold value…to determine an oculomotor abnormality”).
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 Seassau. Analyzing parameters related to return saccades provides an alternative method for diagnosing visual impairments. By comparing one or more new measurable factors, such as a horizontal total amplitude of constituent return saccades, an average number of return jerks on a plurality of line breaks, and an average of ratio of multiple returns to the line, clinicians may be able to diagnosis impairments with added certainty (Seassau, Abstract; page 3, para. 10, “Parameters relating to return saccades make it possible to reveal cases of oculomotor disorder, where the conventional parameters are insufficient”; the entirety of page 4).
Botrashvili further teaches visual stress (Paragraph 0065, lines 3-6) and Seassau further teaches oculomotor disorders (page 4, para. 2, “this system, dedicated to the collection of data, can be part of a system for detecting an oculomotor abnormality in a subject”). However, Botrashvili and Seassau do not specifically disclose Irlen Syndrome.
Yano teaches an analogous system and method for identifying and compensating for Irlen Syndrome wherein Irlen Syndrome is either indicated or not (Col. 7, lines 47-67); and when Irlen Syndrome is indicated, adjusting a parameter of the display (as shown in step 300 of Figure 3; Col. 6, lines 6-15 and lines 30-39).
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 and Seassau with the detection and remedy for Irlen syndrome of Yano. Doing so would distinguish the invention as a tool that could be used for people suffering from either visual stress, an oculomotor disorder, or Irlen Syndrome. 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. 5, lines 55-57; Col. 3, lines 54-67 and Col. 4, lines 1-17).
Regarding claim 2, Botrashvili, in view of Yano, teaches the method according to claim 1 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paragraph 0030, paragraph 0067, lines 17-22; paragraph 0065, lines 1-6; figures 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 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 Yano teaches the method according to claim 1 as stated above wherein the parameter of the display is a background color (Botrashvili, paragraph 0029, lines 9-13) against which the text is displayed (Botrashvili, paragraph 0067 and Figure 2).
Regarding claim 5, Botrashvili 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, paragraph 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 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, paragraph 0046, lines 11-14 where eye movements may be used by the software forming part of the processing system; paragraph 0028, lines 4-8).
Regarding claim 7, Botrashvili in view of Yano teaches the method according to claim 1 as stated above wherein the determining that an impairment is presented, or not, is performed automatically (Botrashvili, paragraph 0028, lines 1-14; additionally, reference claims 1, 5, and 7) specifically when Irlen Syndrome is presented, or not (Yano, Col. 6, lines 31-38 and Figure 4).
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 (Paragraph 0030, line 7):
tracking, with a camera (paragraph 0046, lines 8-9), eye movement (paragraph 0033, lines 7-9) of a human as the human reads text (paragraph 0062, lines 6-8) presented by a display (paragraph 0030, lines 8-10);
collecting eye movement data (paragraph 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; paragraph 0062, lines 1-2);
analyzing eye movement data (paragraph 0030 where the AI module compares the transmitted data to pre-stored big data; paragraph 0062, lines 3-8);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (paragraph 0030 where the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; paragraph 0036, lines 6-9; paragraph 0065, lines 3-11); and
when an impairment (Paragraph 0065, lines 3-6 where visual stress is mentioned) is indicated, adjusting a parameter (paragraph 0045, lines 7-14) of the display (shown in Figs. 2-3).
While Botrashvili teaches analyzing eye movement data, Botrashvili fails to specifically disclose analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen syndrome: based on pattern stability, determining whether or not Irlen Syndrome is indicated.
Seassau teaches an analogous system and method for obtaining, analyzing, and comparing eye metric data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome (Fig. 5 depicts a recording of eye movements of a healthy (an individual without Irlen Syndrome) subject, and Fig. 6 illustrates a recording of eye movements of an individual with an impairment; page 7, para. 10, “a recording, in time, of the displacement of each eye in amplitude along the horizontal axis (degrees relative to each other)”; page 8, para. 10, “once the line returns and their return saccades identified, the processing module 17 determines and calculates in particular one or more return saccade parameters”);
based on the pattern stability (Figs. 5-6; page 9, paras. 6-7, “These monocular parameters based on single-eye recording 16 include: an average number of return saccades on a plurality of line breaks during the reading task…”), determining whether or not an impairment is indicated by the eye movement data (page 3, para. 13, “this method of data collection can be used in a method for detecting an oculomotor abnormality in a subject in which…the calculated [return saccade] parameter is compared with minus a reference threshold value…to determine an oculomotor abnormality”).
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 Seassau. Analyzing parameters related to return saccades provides an alternative method for diagnosing visual impairments. By comparing one or more new measurable factors, such as a horizontal total amplitude of constituent return saccades, an average number of return jerks on a plurality of line breaks, and an average of ratio of multiple returns to the line, clinicians may be able to diagnosis impairments with added certainty (Seassau, Abstract; page 3, para. 10, “Parameters relating to return saccades make it possible to reveal cases of oculomotor disorder, where the conventional parameters are insufficient”; the entirety of page 4).
Botrashvili further teaches visual stress (Paragraph 0065, lines 3-6) and Seassau further teaches oculomotor disorders (page 4, para. 2, “this system, dedicated to the collection of data, can be part of a system for detecting an oculomotor abnormality in a subject”). However, Botrashvili and Seassau do not specifically disclose Irlen Syndrome.
Yano teaches an analogous system and method for identifying and compensating for Irlen Syndrome wherein Irlen Syndrome is either indicated or not (Col. 7, lines 47-67); and when Irlen Syndrome is indicated, adjusting a parameter of the display (as shown in step 300 of Figure 3; Col. 6, lines 6-15 and lines 30-39).
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 and Seassau with the detection and remedy for Irlen syndrome of Yano. Doing so would distinguish the invention as a tool that could be used for people suffering from either visual stress, an oculomotor disorder, or Irlen Syndrome. 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. 5, lines 55-57; Col. 3, lines 54-67 and Col. 4, lines 1-17).
Regarding claim 9, Botrashvili in view of Yano teaches the method according to claim 8 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paragraph 0030, paragraph 0067, lines 17-22; paragraph 0065, lines 1-6; figures 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 Yano teaches the method 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 Yano teaches the method according to claim 8 as stated above wherein the parameter of the display is a background color (Botrashvili, paragraph 0029, lines 9-13) against which the text is displayed (Botrashvili, paragraph 0067 and Figure 2).
Regarding claim 12, Botrashvili in view of Yano teaches the method according to claim 8 as stated above wherein the determining is performed without use of any input consciously provided by the human (Botrashvili, paragraph 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 Yano teaches the method according to claim 8 as stated above wherein the determining is performed using only input that is reflexively provided by the human (Botrashvili, paragraph 0046, lines 11-14 where eye movements may be used by the software forming part of the processing system; paragraph 0028, lines 4-8).
Regarding claim 14, Botrashvili in view of Yano teaches the method according to claim 8 as stated above wherein the determining that an impairment is presented, or not, is performed automatically (Botrashvili, paragraph 0028, lines 1-14; additionally, reference claims 1, 5, and 7) specifically when Irlen Syndrome is presented, or not (Yano, Col. 6, lines 31-38 and Figure 4).
Regarding claim 15, Botrashvili teaches a system (paragraph 0068), comprising:
one or more hardware processors (paragraph 0011, line 7); and
a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising (paragraph 0030, line 7):
tracking, with a camera (paragraph 0046, lines 8-9), eye movement (paragraph 0033, lines 7-9) of a human as the human reads text (paragraph 0062, lines 6-8) presented by a display (paragraph 0030, lines 8-10);
collecting eye movement data (paragraph 0030 where the sensor/camera monitors the individual’s sense(s) and transmits the data to the AI module; paragraph 0062, lines 1-2);
analyzing eye movement data (paragraph 0030 where the AI module compares the transmitted data to pre-stored big data; paragraph 0062, lines 3-8);
based on the analyzing, determining whether or not an impairment is indicated by the eye movement data (paragraph 0030 where the AI module determines whether modifications in the display screen are needed in order to improve the individual’s outcome; paragraph 0036, lines 6-9; paragraph 0065, lines 3-11); and
when an impairment (Paragraph 0065, lines 3-6 where visual stress is mentioned) is indicated, adjusting a parameter (paragraph 0045, lines 7-14) of the display (shown in Figs. 2-3).
While Botrashvili teaches analyzing eye movement data, Botrashvili fails to specifically disclose analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen syndrome: based on pattern stability, determining whether or not Irlen Syndrome is indicated.
Seassau teaches an analogous system and method for obtaining, analyzing, and comparing eye metric data comprising:
analyzing a pattern stability based on the eye movement data and eye movements of a user without Irlen Syndrome (Fig. 5 depicts a recording of eye movements of a healthy (an individual without Irlen Syndrome) subject, and Fig. 6 illustrates a recording of eye movements of an individual with an impairment; page 7, para. 10, “a recording, in time, of the displacement of each eye in amplitude along the horizontal axis (degrees relative to each other)”; page 8, para. 10, “once the line returns and their return saccades identified, the processing module 17 determines and calculates in particular one or more return saccade parameters”);
based on the pattern stability (Figs. 5-6; page 9, paras. 6-7, “These monocular parameters based on single-eye recording 16 include: an average number of return saccades on a plurality of line breaks during the reading task…”), determining whether or not an impairment is indicated by the eye movement data (page 3, para. 13, “this method of data collection can be used in a method for detecting an oculomotor abnormality in a subject in which…the calculated [return saccade] parameter is compared with minus a reference threshold value…to determine an oculomotor abnormality”).
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 Seassau. Analyzing parameters related to return saccades provides an alternative method for diagnosing visual impairments. By comparing one or more new measurable factors, such as a horizontal total amplitude of constituent return saccades, an average number of return jerks on a plurality of line breaks, and an average of ratio of multiple returns to the line, clinicians may be able to diagnosis impairments with added certainty (Seassau, Abstract; page 3, para. 10, “Parameters relating to return saccades make it possible to reveal cases of oculomotor disorder, where the conventional parameters are insufficient”; the entirety of page 4).
Botrashvili further teaches visual stress (Paragraph 0065, lines 3-6) and Seassau further teaches oculomotor disorders (page 4, para. 2, “this system, dedicated to the collection of data, can be part of a system for detecting an oculomotor abnormality in a subject”). However, Botrashvili and Seassau do not specifically disclose Irlen Syndrome.
Yano teaches an analogous system and method for identifying and compensating for Irlen Syndrome wherein Irlen Syndrome is either indicated or not (Col. 7, lines 47-67); and when Irlen Syndrome is indicated, adjusting a parameter of the display (as shown in step 300 of Figure 3; Col. 6, lines 6-15 and lines 30-39).
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 and Seassau with the detection and remedy for Irlen syndrome of Yano. Doing so would distinguish the invention as a tool that could be used for people suffering from either visual stress, an oculomotor disorder, or Irlen Syndrome. 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. 5, lines 55-57; Col. 3, lines 54-67 and Col. 4, lines 1-17).
Regarding claim 16, Botrashvili in view of Yano teaches the method according to claim 15 as stated above wherein the parameter of the display is adjusted automatically (Botrashvili, paragraph 0030, paragraph 0067, lines 17-22; paragraph 0065, lines 1-6; figures 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 Yano teaches the method according to claim 15 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 18, Botrashvili in view of Yano teaches the method according to claim 15 as stated above wherein the parameter of the display is a background color (Botrashvili, paragraph 0029, lines 9-13) against which the text is displayed (Botrashvili, paragraph 0067 and Figure 2).
Regarding claim 19, Botrashvili in view of Yano teaches the method according to claim 15 as stated above wherein the determining is performed using only input that is reflexively provided by the human (Botrashvili, paragraph 0046, lines 11-14 where eye movements may be used by the software forming part of the processing system; paragraph 0028, lines 4-8).
Regarding claim 20, Botrashvili in view of Yano teaches the method according to claim 15 as stated above wherein the determining that an impairment is presented, or not, is performed automatically (Botrashvili, paragraph 0028, lines 1-14; additionally, reference claims 1, 5, and 7) specifically when Irlen Syndrome is presented, or not (Yano, Col. 6, lines 31-38 and Figure 4).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/B.R.L./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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