DETAILED ACTION
The action is responsive to the Application filed on 05/22/2024. Claims 1-20 are pending in the case. Claims 1, 11 and 20 are independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 9 and 19 are objected to because of the following informalities: the claims recite "a speaking disorder, a mental disorder, a physical disability." which is grammatically incorrect. For the purposes of examination, Examiner assume the claims to recite “a speaking disorder, a mental disorder or a physical disability”. Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kandel (US 20240241734 A1) in view of Bossart (US 20230290262 A1).
As to claim 1, Kandel discloses a method for updating a graphical user interface (GUI) of an educational platform, the method comprising:
identifying a first accessibility indicator in a user profile of a user ("In FIG. 3, user profile 300 includes a variety of selectable settings the user 102 can use to define accessibility requirements to select accessibility plugins 216a, 216b or 216n (FIG. 1). In this example, the user profile 300 includes settings for name 302, passwords 304 and accessibility options 306," Kandel paragraph 0034; "Accessibility options 306 enables people with different disabilities to set their accessibility requirements. Specifically, users with visual impairment can set options under visual 308, users with hearing impairment can set requirements under hearing 310 and users with mobility impairment can set requirements under mobility 312. Under visual 308, users can select low vision, color blindness and/or complete blindness," Kandel paragraph 0035);
generating, for display on a computing device, the GUI in a first layout suitable for students with the first accessibility indicator ("In one implementation, determining accessibility requirements of user 102 may involve accessing the user profile 300 (FIG. 3), for example. Here, the system parses the user profile 300 to extract the pertinent accessibility requirements to determine the appropriate accessibility plugin 216a, 216b, 216n," Kandel paragraph 0045; "For example, accessibility plugin 216a may be a high contrast plugin, and its accessibility requirement may relate to low vision. As another example, accessibility plugin 216b may be a screen reader plugin and its accessibility requirement may relate to complete blindness impairment," Kandel paragraph 0047; "At block 406, method 400 involves selecting, without user 102's intervention, one of the available accessibility plugins 216a, 216b, 216n to satisfy the accessibility requirements of the user 102 to access the website," Kandel paragraph 0048);
monitoring an interaction of the user with the GUI ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites and user activities in order to detect accessibility requirements in addition to ones listed in the user profile); and
in response to detecting a second accessibility indicator based on the monitored interaction ("In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites in order to detect accessibility requirements in addition to ones listed in the user profile), updating, for display on the computing device, the GUI to a second layout that is a variation of the first layout, wherein the second layout is suitable for students with both the first accessibility indicator and the second accessibility indicator ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015; "In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast. In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030, using the first indicator and second detected indicator to select plugins that will change the GUI to be more accessible for the first and second indicators).
However Kandel does not appear to explicitly disclose:
identifying a first accessibility indicator in a user profile of a user accessing the educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user; and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI.
Bossart teaches:
an educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user (“At operation 208, one or more lessons, and in some embodiments a plurality of lesson modules (i.e., a course) can be retrieved for each failed skill of the aptitude test. In some embodiments, the course can then be provided and generated for access to the student on the GUI,” Bossart paragraph 0038); and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI ("In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037, monitoring interaction with modules such as time to answer a question or number of failed questions to determine a learning disability).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kandel to monitor interactions with activity modules of an educational platform as taught by Bossart. One would have been motivated to make such a combination so that the finished product could support more kinds of websites and GUIs and so that more kinds of information could be considered when determining accessibility indicators.
As to claim 2, Kandel as modified by Bossart further discloses the method of claim 1, further comprising:
monitoring another interaction of the user with the GUI when accessing another activity module of the plurality of activity modules on the GUI ("In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites in order to detect further accessibility requirements (i.e., a third, fourth or nth accessibility requirement) in addition to ones listed in the user profile or previously detected); and
in response to detecting a third accessibility indicator based on the another interaction, updating, for display on the computing device, the GUI to a third layout that is a variation of the second layout, wherein the third layout is suitable for students with the first accessibility indicator, the second accessibility indicator, and the third accessibility indicator ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015; "In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast. In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030, using the indicators to select plugins that will change the GUI to be more accessible for the indicators).
As to claim 3, Kandel as modified by Bossart further discloses the method of claim 1, further comprising:
monitoring another interaction of the user with the GUI when accessing another activity module of the plurality of activity modules on the GUI ("In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, further monitoring interactions to determine if a selection was accurate (i.e., if a previously selected accessibility indicator is needed) and remove it if not); and
in response to determining that the another interaction does not comprise the second accessibility indicator, reverting the GUI to the first layout ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015; "In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast. In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030, updating the GUI based on which accessibility indicators are active).
As to claim 4, Kandel as modified by Bossart further discloses the method of claim 1, wherein monitoring the interaction comprises determining one or more of:
(1) an amount of time taken to fully complete the activity module, (2) an amount of time taken to complete a portion of the activity module ("In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037),
(3) a number of times the activity module is closed or restarted,
(4) a number of times a portion of the activity module is restarted,
(5) a number of correct responses received from the user in the activity module, (6) a number of incorrect responses received from the user in the activity module ("In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037), and
(7) a number of manual setting adjustments made during the activity module.
As to claim 5, Kandel as modified by Bossart further discloses the method of claim 1, wherein detecting the second accessibility indicator based on the monitored interaction comprises executing a machine learning algorithm trained to detect accessibility indicators based on activity scores and clickstream data and from one or more of the plurality of activity modules ("In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "In one implementation, determining accessibility requirements of user 102 may involve accessing the user profile 300 (FIG. 3), for example. Here, the system parses the user profile 300 to extract the pertinent accessibility requirements to determine the appropriate accessibility plugin 216a, 216b, 216n. In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037).
As to claim 6, Kandel as modified by Bossart further discloses the method of claim 1, wherein updating the GUI to the second layout comprises executing a machine learning algorithm that utilizes reinforcement learning to optimize the GUI for the user such that an amount of time to complete the activity module is minimized and an amount of correct responses received from the user in the activity module is maximized ("The machine learning model 111 is operative to suggest the appropriate accessibility plugin for downloaded based on training data 211 that is fed to the machine learning model 111 over time," Kandel paragraph 0025; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055; "In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037, using how quick or slow the user answered and the time to completion to change the GUI to min/max these variables).
As to claim 7, Kandel as modified by Bossart further discloses the method of claim 1, wherein the first accessibility indicator is a disability at a first severity level and the second accessibility indicator is the disability at a second severity level ("Under visual 308, users can select low vision, color blindness and/or complete blindness," Kandel paragaph 0035; "Under hearing 310, users can select the appropriate option button 316 for deafness, hard of hearing and hyperacusis," Kandel paragraph 0036; "Under mobility 312, users can select the corresponding option button 318 for cerebral palsy, paralysis, carpal tunnel and dyspraxia," Kandel paragraph 0036, for visual impairments low vision or color blindness has a lower severity than complete blindness, for audio impairments, hard of hearing has a lower severity than deafness and for motion impairments, carpal tunnel has a lower severity than cerebral palsy, paralysis or dyspraxia).
As to claim 8, Kandel as modified by Bossart further discloses the method of claim 1, wherein the GUI comprises a plurality of virtual objects, and wherein updating the GUI to the second layout comprises incrementally altering one or more of:
(1) a type of virtual object depicted on the GUI,
(2) a size of a virtual object depicted on the GUI ("The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015),
(3) a virtual distance between two or more virtual objects,
(4) a color of the virtual object ("In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast," Kandel paragraph 0030), and
(5) a sound associated with selecting the virtual object (In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030).
As to claim 9, Kandel as modified by Bossart further discloses the method of claim 1, wherein the first accessibility indicator comprises one of a behavioral disability, vision impairment, a hearing impairment, a cognitive impairment, a mental disorder, motor impairment (Kandel Figure 3 "Low Vision", "Color Blindness", "Complete Blindness", "Deafness", "Hard of Hearing", "Hyperacusis", "Cerebral Palsy", "Paralysis", "Carpal Tunnel" and "Dyspraxia" vision, hearing and motor impairments; "In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037, detecting cognitive, mental and behavior disabilities).
As to claim 10, Kandel as modified by Bossart further discloses the method of claim 1, wherein the user profile is indicative of one or more of: an age, a gender, a school grade level, scores on any of the plurality of activity modules, and any known accessibility issues ("In FIG. 3, user profile 300 includes a variety of selectable settings the user 102 can use to define accessibility requirements to select accessibility plugins 216a, 216b or 216n (FIG. 1). In this example, the user profile 300 includes settings for name 302, passwords 304 and accessibility options 306," Kandel paragraph 0034; "Accessibility options 306 enables people with different disabilities to set their accessibility requirements. Specifically, users with visual impairment can set options under visual 308, users with hearing impairment can set requirements under hearing 310 and users with mobility impairment can set requirements under mobility 312. Under visual 308, users can select low vision, color blindness and/or complete blindness. In fact, as can be seen here, the option button 314 is turned on, indicating that user 102 has a low vision impairment (for example)," Kandel paragraph 0035).
As to claim 11, Kandel discloses a system for updating a graphical user interface (GUI) of an educational platform, comprising:
at least one memory (“The non-transitory computer-readable storage medium can be encoded to store executable instructions that are stored in memory and wherein the memory is coupled to processor and cause the processor to perform operations according to examples of the disclosure.,” Kandel paragraph 0062);
at least one hardware processor coupled with the at least one memory and configured, individually or in combination (“The non-transitory computer-readable storage medium can be encoded to store executable instructions that are stored in memory and wherein the memory is coupled to processor and cause the processor to perform operations according to examples of the disclosure.,” Kandel paragraph 0062), to:
identify a first accessibility indicator in a user profile of a user ("In FIG. 3, user profile 300 includes a variety of selectable settings the user 102 can use to define accessibility requirements to select accessibility plugins 216a, 216b or 216n (FIG. 1). In this example, the user profile 300 includes settings for name 302, passwords 304 and accessibility options 306," Kandel paragraph 0034; "Accessibility options 306 enables people with different disabilities to set their accessibility requirements. Specifically, users with visual impairment can set options under visual 308, users with hearing impairment can set requirements under hearing 310 and users with mobility impairment can set requirements under mobility 312. Under visual 308, users can select low vision, color blindness and/or complete blindness," Kandel paragraph 0035);
generate, for display on a computing device, the GUI in a first layout suitable for students with the first accessibility indicator ("In one implementation, determining accessibility requirements of user 102 may involve accessing the user profile 300 (FIG. 3), for example. Here, the system parses the user profile 300 to extract the pertinent accessibility requirements to determine the appropriate accessibility plugin 216a, 216b, 216n," Kandel paragraph 0045; "For example, accessibility plugin 216a may be a high contrast plugin, and its accessibility requirement may relate to low vision. As another example, accessibility plugin 216b may be a screen reader plugin and its accessibility requirement may relate to complete blindness impairment," Kandel paragraph 0047; "At block 406, method 400 involves selecting, without user 102's intervention, one of the available accessibility plugins 216a, 216b, 216n to satisfy the accessibility requirements of the user 102 to access the website," Kandel paragraph 0048);
monitor an interaction of the user with the GUI ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites and user activities in order to detect accessibility requirements in addition to ones listed in the user profile); and
in response to detecting a second accessibility indicator based on the monitored interaction ("In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites in order to detect accessibility requirements in addition to ones listed in the user profile), update, for display on the computing device, the GUI to a second layout that is a variation of the first layout, wherein the second layout is suitable for students with both the first accessibility indicator and the second accessibility indicator ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015; "In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast. In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030, using the first indicator and second detected indicator to select plugins that will change the GUI to be more accessible for the first and second indicators).
However Kandel does not appear to explicitly disclose:
identifying a first accessibility indicator in a user profile of a user accessing the educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user; and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI.
Bossart teaches:
an educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user (“At operation 208, one or more lessons, and in some embodiments a plurality of lesson modules (i.e., a course) can be retrieved for each failed skill of the aptitude test. In some embodiments, the course can then be provided and generated for access to the student on the GUI,” Bossart paragraph 0038); and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI ("In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037, monitoring interaction with modules such as time to answer a question or number of failed questions to determine a learning disability).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kandel to monitor interactions with activity modules of an educational platform as taught by Bossart. One would have been motivated to make such a combination so that the finished product could support more kinds of websites and GUIs and so that more kinds of information could be considered when determining accessibility indicators.
As to claim 12, it is substantially similar to claim 2 and is therefore rejected using the same rationale as above.
As to claim 13, it is substantially similar to claim 3 and is therefore rejected using the same rationale as above.
As to claim 14, it is substantially similar to claim 4 and is therefore rejected using the same rationale as above.
As to claim 15, it is substantially similar to claim 5 and is therefore rejected using the same rationale as above.
As to claim 16, it is substantially similar to claim 6 and is therefore rejected using the same rationale as above.
As to claim 17, it is substantially similar to claim 7 and is therefore rejected using the same rationale as above.
As to claim 18, it is substantially similar to claim 8 and is therefore rejected using the same rationale as above.
As to claim 19, it is substantially similar to claim 9 and is therefore rejected using the same rationale as above.
As to claim 20, Kandel discloses a non-transitory computer readable medium storing thereon computer executable instructions for updating a graphical user interface (GUI) of an educational platform, including instructions (“The non-transitory computer-readable storage medium can be encoded to store executable instructions that are stored in memory and wherein the memory is coupled to processor and cause the processor to perform operations according to examples of the disclosure.,” Kandel paragraph 0062) for:
identifying a first accessibility indicator in a user profile of a user ("In FIG. 3, user profile 300 includes a variety of selectable settings the user 102 can use to define accessibility requirements to select accessibility plugins 216a, 216b or 216n (FIG. 1). In this example, the user profile 300 includes settings for name 302, passwords 304 and accessibility options 306," Kandel paragraph 0034; "Accessibility options 306 enables people with different disabilities to set their accessibility requirements. Specifically, users with visual impairment can set options under visual 308, users with hearing impairment can set requirements under hearing 310 and users with mobility impairment can set requirements under mobility 312. Under visual 308, users can select low vision, color blindness and/or complete blindness," Kandel paragraph 0035);
generating, for display on a computing device, the GUI in a first layout suitable for students with the first accessibility indicator ("In one implementation, determining accessibility requirements of user 102 may involve accessing the user profile 300 (FIG. 3), for example. Here, the system parses the user profile 300 to extract the pertinent accessibility requirements to determine the appropriate accessibility plugin 216a, 216b, 216n," Kandel paragraph 0045; "For example, accessibility plugin 216a may be a high contrast plugin, and its accessibility requirement may relate to low vision. As another example, accessibility plugin 216b may be a screen reader plugin and its accessibility requirement may relate to complete blindness impairment," Kandel paragraph 0047; "At block 406, method 400 involves selecting, without user 102's intervention, one of the available accessibility plugins 216a, 216b, 216n to satisfy the accessibility requirements of the user 102 to access the website," Kandel paragraph 0048);
monitoring an interaction of the user with the GUI ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites and user activities in order to detect accessibility requirements in addition to ones listed in the user profile); and
in response to detecting a second accessibility indicator based on the monitored interaction ("In another implementation, the system may rely on machine learning model 111 to determine the accessibility requirements as discussed with reference to FIG. 5," Kandel paragraph 0045; "In an implementation, machine learning model 111 may be based on a classification method. The classification method may correspond to one or more of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), for example. In one example, an RF classification method can be used. The RF classification method is a collection of decision trees such as decision tree 520 that can predict or select a plugin based on user profile input data. That is, each individual decision tree includes branches that classify user profile input data according to their characteristics (e.g., type of impairment, username, history of use of plugins, websites accessed, etc.)," Kandel paragraph 0051; "At each intersection such as 521, a test such as for example 'is the selection accurate' is made. This is of course a symbolic representation and processing of expected selections can be significantly more complex. In an example, processing builds a tree, whereby each branch of the tree represents expected recommendations, whereby each branch is associated with an expected recommendation. Classification may increase in precision as additional user profile input data is processed. This progressive process is referred to as the learning phase, whereby classification becomes increasingly more accurate at future selections," Kandel paragraph 0055, monitoring interactions with websites in order to detect accessibility requirements in addition to ones listed in the user profile), updating, for display on the computing device, the GUI to a second layout that is a variation of the first layout, wherein the second layout is suitable for students with both the first accessibility indicator and the second accessibility indicator ("The method includes determining accessibility requirements of the user to access a website. In some examples, determining accessibility requirements may be based on a user profile. In other implementations, determining accessibility requirements may be based on a machine learning model (and a user profile)," Kandel paragraph 0013; "At block 408, without user intervention, the selected accessibility plugin is downloaded and installed to facilitate use of the website by the user. Although not shown, method 400 may involve accessing user activities and historical usage of same or similar plugins to select the accessibility plugin 216a, 216b or 216n that meets the user 102's accessibility requirements," Kandel paragraph 0049; "The experience of using a web browser to access an app or website that is not WCAG-compliant is also enhanced because plug-in adjustments such as changing color contrast of hex values, enlarging font size, or enabling basic keyboard shortcuts, etc., can be made," Kandel paragraph 0015; "In one example, accessibility plugin 216a may be a contrast accessibility plugin. If user 102 has a low vision impairment, accessibility plugin 216a is the appropriate plugin to work with browser 232 to provide high or increased color contrast. In another example, if user 102 has complete blindness impairment, accessibility plugin 216b which is a screen reading accessibility plugin may be selected to provide screen reading capabilities," Kandel paragraph 0030, using the first indicator and second detected indicator to select plugins that will change the GUI to be more accessible for the first and second indicators).
However Kandel does not appear to explicitly disclose:
identifying a first accessibility indicator in a user profile of a user accessing the educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user; and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI.
Bossart teaches:
an educational platform, wherein the educational platform comprises a plurality of activity modules that test an educational knowledge of the user (“At operation 208, one or more lessons, and in some embodiments a plurality of lesson modules (i.e., a course) can be retrieved for each failed skill of the aptitude test. In some embodiments, the course can then be provided and generated for access to the student on the GUI,” Bossart paragraph 0038); and
monitoring an interaction of the user with the GUI when accessing an activity module of the plurality of activity modules on the GUI ("In some embodiments, machine learning (i.e., artificial intelligence) is employed to take into other factors in order to provide improved lessons for the student. In some embodiments, machine learning can be used to consider other factors outside of the answered questions to determine if lesson focus on particular skills is warranted, for example, if a student had past difficulty (e.g., a record of past failed questions in a particular skill or set of related skills) in learning a related skill, the amount of time taken to pass related skills that the student answered correctly for (e.g., if a student answered a question correctly, but took three times as long to answer the question, suggesting more lessons may be required, or if a student answered one or more questions particularly quickly in comparison to other students/past test records, suggesting the student should be placed ahead), the number of inputs/changes to a passed or failed questions (e.g., taking into consideration that a student initially entered the correct answer but ultimately changed the input into an incorrect answer, perhaps suggesting confidence and/or sloppiness issues), the time of day the student took the test, the last time the student took an aptitude test and/or completed lessons, whether other applications were being operated at the same time that can suggest dishonesty (e.g., calculator software, Internet access to teaching sites during the test) or attention issues (e.g., Internet access to social media sites), whether recorded facial reactions, hand movements, and/or eye movements (recorded by a camera of student network access device 102) of the student suggest a particular issue that is impairing the student (external distractions, sleep deprivation, learning disability, dishonesty, disinterest, stress, boredom)," Bossart paragraph 0037, monitoring interaction with modules such as time to answer a question or number of failed questions to determine a learning disability).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium of Kandel to monitor interactions with activity modules of an educational platform as taught by Bossart. One would have been motivated to make such a combination so that the finished product could support more kinds of websites and GUIs and so that more kinds of information could be considered when determining accessibility indicators.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20190121633 A1 to Chen et al. discloses crowd-sourcing ;
US 20190340212 A1 to Isager discloses dynamic content modifications where content on a webpage is modified to adapt to a detected disability;
US 20210043109 A1 to Mese et al. discloses alteration of accessibility settings of a device based on characteristics of users where a user’s input in a GUI is used by artificial intelligence to determine if the user has a disability and adapting the GUI for the disability;
US 20210055856 A1 to Capruso et al. discloses detecting accessibility patterns to modify the user interface of an application where artificial intelligence is used to detect a user’s accessibility indicators and modifying a GUI for the accessibility indicators; and
US 20240186009 A1 to Kim et al. discloses systems and methods of providing deep learning based neurocognitive impairment evaluation using extended reality.
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/DANIEL SAMWEL/Primary Examiner, Art Unit 2171