DETAILED ACTION
Notice of 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 .
Status of the Application
Claims 1-20 were pending and were rejected in the previous office action.
Claims 1, 4, 6, 8, 11, 13, 15, 18, and 20 were amended.
Claims 1-20 remain pending and are examined in this office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/23/2026 has been entered.
Response to Arguments
35 USC § 101:
Applicant’s arguments regarding the § 101 rejections of claims 1-20 (pgs. 11-13, remarks filed 3/23/2026) have been fully considered and are persuasive.
The examiner previously indicated that “Should applicant limit the claims to i) clarify that the one or more interface content components are actually being modified by a filter/distortion, e.g. “apply one or more filters, distortions, settings…when processing the received rendered interface content to simulate how the rendered interface content may be perceived by a user in a particular user population” (spec. at ¶ 0069) and ii) specify that that the performing step includes performing, using a user population model, a task with the one or more interface content components as modified by the set of test conditions, then it is likely that the examiner would find the claimed invention as a whole to be directed to unconventional solution that provides a technical improvement specific to the testing (and detection of accessibility issues) of computerized user interfaces” (see pgs. 6-7 of Final Rejection mailed 12/23/2025).
Applicant has amended the claims as previously suggested. Therefore, the claims considered a whole, provide a specific technical improvement and solution that addresses issues specific to graphical user interface accessibility by transforming content on graphical user interfaces to simulate the appearance of the user interface as it would appear to a user population of interest during testing (“to apply one or more filters, distortions, settings, and/or when processing the received rendered interface content to simulate how the rendered interface content may be perceived by a user in a particular user population”).
Therefore, the claims at least integrate any recited abstract idea directed to evaluating user interfaces into a practical application. The claims would also, considered as an ordered combination, add significantly more than the abstract idea by reciting an unconventional solution to a specific problem pertaining to user interfaces via the recitation of the specific user interface transformation/improvement (“to apply one or more filters, distortions, settings, and/or when processing the received rendered interface content to simulate how the rendered interface content may be perceived by a user in a particular user population”) discussed above.
The § 101 rejection of claims 1-20 is withdrawn.
35 USC § 103:
Applicant’s arguments regarding the previous § 103 rejections of claims 1-20 (pgs. 14-15, remarks filed 3/23/2026) have been considered but are moot as they do not apply to the current grounds of rejection applied in the § 103 rejections below, in response to applicant’s amendments.
Please see the current § 103 rejections of claims 1-20 which are updated to reflect the claim amendments.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“determining, by analysis circuitry…,” “selecting, by the analysis circuitry…,” “determining, by the analysis circuitry…,” and “determining, by the analysis circuitry…” of claim 1
“determining, by the analysis circuitry…” of claim 2
“determining, by the analysis circuitry…” of claim 3
“identifying, by the analysis circuitry…” and “determining, by the analysis circuitry…” of claim 4
“selecting, by the analysis circuitry…generating, by the analysis circuitry…and generating, by the analysis circuitry…” of claim 5
“identifying, by evaluation circuitry…” of claim 6
“generating, by the evaluation circuitry…and training, by training circuitry…” of claim 7
“analysis circuitry configured to: determine…select…apply…perform…output…determine…and determine…” of claim 8
“the analysis circuitry is further configured to determine…” of claim 9
“the analysis circuitry is further configured to determine…” of claim 10
“the analysis circuitry is further configured to: identify…and determine…” of claim 11
“the analysis circuitry is further configured to: select…generate…and generate…” of claim 12
“evaluation circuitry configured to identify…” of claim 13
“the evaluation circuitry is further configured to: generate…” and “training circuitry configured to train…” of claim 14
“an apparatus to: receive…determine …select…apply…perform…output…determine…determine…and provide …” of claim 15
“the apparatus to determine…” of claim 16
“the apparatus to determine…” of claim 17
“the apparatus to: identify…and determine…” of claim 18
“the apparatus to: select…generate…and generate…” of claim 19
“the apparatus to: identify…provide…and receive…” of claim 20
The analysis circuitry, evaluation circuitry, and training circuitry are described in the following portions of the filed specification: “the analysis circuitry 208, evaluation circuitry 210, training circuitry 212 may each at times leverage use of the processor 202, memory 204, or communications hardware 206, such that duplicate hardware is not required to facilitate operation of these physical elements of the apparatus 200 (although dedicated hardware elements may be used for any of these components in some embodiments, such as those in which enhanced parallelism may be desired). Use of the terms “circuitry” and “engine” with respect to elements of the apparatus therefore shall be interpreted as necessarily including the particular hardware configured to perform the functions associated with the particular element being described. Of course, while the terms “circuitry” and “engine” should be understood broadly to include hardware, in some embodiments, the terms “circuitry” and “engine” may in addition refer to software instructions that configure the hardware components of the apparatus 200 to perform the various functions described herein” (¶ 0034) and “it will be understood that any of analysis circuitry 208, evaluation circuitry 210, and training circuitry 212 may include one or more dedicated processor, specially configured field programmable gate array, or application specific interface circuit to perform its corresponding functions, and may accordingly leverage processor 202 executing software stored in a memory (e.g., memory 204), or communications hardware 206 for enabling any functions not performed by special-purpose hardware. In all embodiments, however, it will be understood that analysis circuitry 208 and training circuitry 212 comprise particular machinery designed for performing the functions described herein in connection with such elements of apparatus 200” (¶ 0035).
The apparatus is described in the following portions of the filed specification: Fig. 2 and ¶ 0025 showing “As illustrated in FIG. 2, the apparatus 200 may include processor 202, memory 204, communications hardware 206, analysis circuitry 208, evaluation circuitry 210, and training circuitry 212.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-5, 8, 10-12, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 12211487 B1 to Sarkar et al. (Sarkar) in view of US 20120324424 A1 to Breeds et al. (Breeds), and further in view of US 20230141070 A1 to Kristoffersen et al. (Kristoffersen).
Claim 1: Sarkar teaches:
A method for evaluating interface content for a target user population (Sarkar: Col. 1: 34-51 showing “A method for creating accessibility of any website or application for people with sight, hearing or speech disabilities…The method can also involve scoring, by the server, the website or the application for its accessibility based on the specific disabilities of the user), the method comprising:
receiving, by communications hardware, the interface content comprising one or more interface content components (Sarkar: Col. 4: 37-47 showing “The website and/or applications 110 can be input to a web parser 120 that can parse HTML elements from the website. Images, audio and/or text in the HTML can be parsed. In some embodiments, the HTML can be parsed using Beautiful Soup. In some embodiments, HTML can be parsed using Fizzler, csquery and/or any tool as is known in the art to parse HTML. The output of the web parser 120 (e.g., images, audio and/or text from the HTML elements) can be input to machine learning (ML) models 130…”; and Col. 5: 3-11 showing receiving output of a website or application to be accessed);
determining, by analysis circuitry, a user population of interest (Sarkar: Col. 1: 43-47 “The method can involve receiving, by a server, input of the website or the application to be accessed and an indicator as to specific disabilities a user of a device sending the input”; also see Col. 5: 1-2 “people with sight, hearing and/or speech disabilities” and 3-16 “receiving, by a server…input of the website or the application (e.g., website and/or application 110 as described above in FIG. 1 ) to be accessed and an indicator as to specific disabilities a user of a device sending the input (Step 210). The website and/or application can be a website and/or application that the user is attempting to use (e.g., YouTube® or X® formerly known as Twitter) through a computing device. The disability of the user can be input by the user and/or automatically detected based on the user's device (e.g., a device with braille capability can be detected, thus the disability of vision impairment can be automatically determined)”);
selecting, by the analysis circuitry, an evaluation model framework based on the user population of interest (Sarkar: Col. 5: 19-27 showing “The method can involve scoring, by the server, the website or the application for its accessibility based on the specific disabilities of the user (Step 230). The scoring can be based on whether the website or the application has an ability to present its output in a format that is usable to the user based on their particular disability. In some embodiments, the scoring can be based on whether the website of the application has an ability present its output in a format that is usable to the user that has any disability”; and Col. 5: 30-33 showing “In some embodiments, the scoring is based on Web Content Accessibility Guidelines (WCAG). In some embodiments, the scoring is based on a WCAG checker tool, as are known in the art. In some embodiments, the scoring is based on ADA compliance”; also see Col. 5: 50-54 showing “The corresponding machine learning algorithm can be a machine learning algorithm that corresponds to the specific disabilities of the user. The machine learning algorithm can be selected from one of a plurality of machine learning algorithms”);
With respect to the following limitations, Sarkar teaches evaluating the accessibility of a user interface/website using a machine learning algorithm with respect to specific disabilities (Sarkar: Col. 5: 19-33, 50-54) but does not explicitly teach the following. However, Breeds teaches:
applying, from the evaluation model framework (Breeds: Fig. 2, ¶ 0040-0044 showing test generating system including test screen component, mask generating components, and/or ¶ 0053 showing testing mechanism 310), a set of test conditions to the one or more interface content components, wherein (a) the set of test conditions modify the one or more interface content components using one or more filters, distortions, or settings (Breeds: ¶ 0040-0044 showing identifying and analyzing objects in the GUI, and applying screens or masks to objects “which may provide visual clues to a user which would not be available to an unsighted user” during testing of the user interface for accessibility; also see ¶ 0053-0054) and (b) the evaluation model framework simulates how the interface content, when rendered, is perceived by a user in the user population of interest to generate the one or more modified interface content components (Breeds: ¶ 0053 “to simulate the content of a GUI 203 of the software 201 as it would be available to a user of a screen reader…” and see ¶ 0054; also ¶ 0097 “A tester is provided 901 with a GUI of software to be tested simulated as provided by a screen reader. The generated mask layer (or layers) is applied 902 to the screen buffer of the GUI to reflect the output of the screen reader. This restricts the GUI to only components that would have been audibly provided”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included testing a software application after applying user interface modifications to simulate how the user interface would be experienced by a user from a target user population of Breeds in the website accessibility scoring system of Sarkar with a reasonable expectation of success of arriving at the claimed invention, with the motivation to address the problems that “One of the difficulties in accessibility testing of a product is reluctance or lack of training on the part of (able) testers to do the testing in a way that truly reflects the target user and their disability…Unfortunately, sighted testers inevitably fall back to using their sight to read the graphical user interface and the mouse to interact with the user interface, so the standard of testing can be diminished” (Breeds: ¶ 0005) and “The current approach to accessibility testing is insufficient and prone to inconsistency in quality, where sighted testers lapse to their familiar graphical user feedback and mouse input device…Such an approach tends to result in (sighted) testers still looking at the user interface for a good portion of their testing, which runs the risk of them interpreting a visual cue that is not available to the unsighted target user” (Breeds: ¶ 0007).
With respect to the limitations:
performing, using a user population model, a task with the one or more modified interface content components;
outputting, in response to performing the task by the user population model, a user population performance metric set;
Sarkar teaches using a machine learning model specific to a particular disability to analyze elements of a website and determine how accessible the interface is to a user with specific disabilities, i.e. generate a performance metric set (Sarkar: Col. 4: 44-53, and Col. 5: 17-30), but does not explicitly teach performing a task with the or more modified interface content components to determine the “metric set” corresponding to the performance of the task.
However, Breeds teaches performing testing of the user interface/software accessibility after applying the modifications to user interface components to provide a restricted GUI (Breeds: ¶ 0029-0030 showing performing testing; see modifications in ¶ 0040-0044, ¶ 0053-0054; and see ¶ 0047, ¶ 0092-0095, ¶ 0097-0100 showing testing user interface navigation tasks), and further teaches determining and highlighting/reporting aspects of the GUI that are not compliant with accessibility to be determined (Breeds: ¶ 0106; also see ¶ 0095, ¶ 0096-0100).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included testing using the restricted GUI/interface components of Breeds in the website accessibility scoring system of Sarkar/Breeds with a reasonable expectation of success of arriving at the claimed invention, for the same reasons described in the limitations above.
Note: The claims do not include any indication of what the task in “performing…a task” includes. Therefore, the disclosure of Breeds where a user “performs testing…” and where the user navigates around the user interface on the restricted GUI reads on the broadest reasonable interpretation of performing a task.
Sarkar, as modified above, further teaches:
determining, by the analysis circuitry, based on the user population performance metric set (Sarkar: Col. 5: 17-34 “based on whether the website or the application has an ability to present its output in a format that is usable to the user based on their particular disability”), and using the evaluation model framework, an accessibility score for the interface content based on the one or more interface content components (Sarkar: Col. 4: 46-53 showing scoring the input HTML/website content using one or more machine learning (ML) models, and Col. 5: 17-34 showing “The method can involve parsing, by the server, html elements from the input (Step 220). The method can involve scoring, by the server, the website or the application for its accessibility based on the specific disabilities of the user (Step 230). The scoring can be based on whether the website or the application has an ability to present its output in a format that is usable to the user based on their particular disability. In some embodiments, the scoring can be based on whether the website of the application has an ability present its output in a format that is usable to the user that has any disability. In some embodiments, the scoring can be low, medium or high. In some embodiments, the scoring can be on a scale from 0 to 100. In some embodiments, the scoring is based on Web Content Accessibility Guidelines (WCAG). In some embodiments, the scoring is based on a WCAG checker tool, as are known in the art. In some embodiments, the scoring is based on ADA compliance”);
determining, by the analysis circuitry, whether the accessibility score satisfies an accessibility score threshold (Sarkar: Col. 5: 35-49 showing the score determined above is compared to a threshold to determine whether the score is below a threshold, which as per above is indicating that the website/application does not output/present information in a way that is accessible to users with disabilities and/or the specific input disabilities); and
providing, by the communications hardware, an interface content evaluation report (Sarkar: Fig. 3B and Col. 7: 32-37 showing “a screen shot of the user interface 301 after the analysis has been performed, showing an example output of the accessibility assessment, according to some embodiments of the invention. The output can provide an assessment of accessibility pass or fail of each element on the website”),
With respect to the limitation:
wherein (a) the interface content evaluation report flags the interface content for the user population of interest in an instance in which the accessibility score fails to satisfy the accessibility score threshold and (b) the interface content evaluation report is indicative of an approval of the interface content for the user population of interest in an instance in which the accessibility score satisfies the accessibility score threshold
Sarkar teaches scoring the accessibility of website interface content for people with disabilities or for people with specific disabilities, i.e. the user population of interest, and comparing the score for the interface content against a threshold (Sarkar: Col. 5: 17-49), wherein the scoring model can be performed by ML models (Sarkar: Col. 4: 46-53) and can be based on WCAG guidelines (Sarkar: Col. 4: 49-41), and Sarkar further teaches performing and outputting the results of an accessibility assessment on the accessibility of the website interface that indicates a pass or fail test result for various aspects of website interface that were analyzed under the accessibility assessment (Sarkar: Fig. 3 and Col. 7: 32-37 as above) – which highly suggests, but does not explicitly teach, that the pass (approval) or fail (flag) results provided on the accessibility assessment report were also based on the accessibility score satisfying or not satisfying the accessibility score threshold.
However, Kristoffersen teaches outputting a report scoring a website interface on accessibility, wherein the output accessibility report that both flags interface content that falls below a threshold score and shows interface content that meets/exceeds a threshold score and depicted in a green box indicating approval of the interface content (Kristoffersen: Fig. 1E and ¶ 0125-0132 showing accessibility report page; with ¶ 0127 showing “A score that is good (e.g., within some predetermined threshold range) can be visually depicted in a green box while a score that is poor can be visually depicted in a red box”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the output showing interface elements that score below (flagged) or above (approved) a predetermined threshold of Kristoffersen in the website accessibility scoring system of Sarkar/Breeds with a reasonable expectation of success of arriving at the claimed invention, with the motivation to address the problems that “if a website is not formatted properly, users who are seeing or hearing impaired may not be able to use website reader applications to review and navigate through the content” (Kristoffersen: ¶ 0003) and “thereby improving user experiences for users who visit the website and maintaining accessibility compliance” (Kristoffersen: ¶ 0004).
Claim Interpretation Note: A target user population or user population of interest, under the broadest reasonable interpretation, could broadly include any/all individuals who have a disability or impairment and does not specify a particular subset of disability or impairment out of the overall population.
Claim 3: Sarkar/Breeds/Kristoffersen teach claim 1. With respect to the following limitations, Sarkar/Breeds do not explicitly teach the following, however, Kristoffersen teaches:
determining, by the analysis circuitry and using the evaluation model framework, one or more sub-accessibility scores (Kristoffersen: Fig. 1E and ¶ 0125-0130 showing overall accessibility score, a WCAG level conformance score, and one or sub-accessibility scores that were determined; see ¶ 0206-0209, ¶ 0260 showing the determination of current score values),
wherein (a) a sub-accessibility score corresponds to an interface content component of the one or more interface content components and (b) the accessibility score is based on the one or more sub-accessibility scores (Kristoffersen: Fig. 1E and ¶ 0125-0130 showing the components scores making up the overall accessibility scores and/or scores for WCAG level conformance)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of components scores making up the overall accessibility score of Kristoffersen in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen with a reasonable expectation of success of arriving at the claimed invention, for the same reasons described in the rejection of claim 1 above.
Claim 4: Sarkar/Breeds/Kristoffersen teach claim 1. With respect to the following limitations, Sarkar/Breeds do not explicitly teach the following limitations, however, Kristoffersen teaches:
identifying, by the analysis circuitry and using the evaluation model framework, an evaluation test for an interface content component of the one or more interface content components based on an interface content component type (Kristoffersen: ¶ 0233 “ the computer system can receive user selection of a potential issue in a list of issues for a website” and ¶ 0234 “Next, the computer system can present an accessibility conformity testing (ACT) module at the user's client computing device for review of the selected potential issue,” wherein as per ¶ 0235 “the computer system can retrieve a set of ACT questions that correspond to a type of the selected potential issue in block 712”),
wherein the evaluation test comprises one or more tasks to be performed (Kristoffersen: ¶ 0234-0237 showing presenting the test including questions to be answered, wherein the test is based upon whether user input has previously been provided pertaining to the potential issue or a similar potential issue; also see ¶ 0007, ¶ 0031-0032, ¶ 0142, ¶ 0218) and one or more test conditions (Kristoffersen: ¶ 0095-0096, Fig. 1A showing plurality of WCAG conformance levels available to choose from for which the system tests the website interface elements against); and
determining, by the analysis circuitry and using the evaluation model framework, a sub-accessibility score for the interface content component based on one or more user population performance metrics (Kristoffersen: ¶ 0125-0129 showing sub-component scores associated with actual issues that are grouped under WCAG level A conformance, which can be identified as actual issues via the test questions above; and ¶ 0098, ¶ 0111 showing each issue is associated with a WCAG conformance level),
wherein the one or more user population performance metrics are determined based on an inferred accessibility of the interface content component for the user population of interest under the one or more test conditions (Kristoffersen: ¶ 0007, ¶ 0031-0032, ¶ 0142, ¶ 0218 ¶ 0234-0237 showing based on the accessibility conformance test, a potential issue is converted to an actual issue under the accessibility conformance standards; see Fig. 1E showing actual issues indicated by exclamation point; wherein each issue is classified based on an WCAG level conformance level standard as per ¶ 0098, ¶ 0111; also see Kristoffersen: ¶ 0096, ¶ 0099, ¶ 0118 showing the accessibility is being score using WCAG conformance for users with disabilities, i.e. user population of interest; see https://www.w3.org/WAI/standards-guidelines/wcag/ “to make web content more accessible to people with disabilities”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the processes for identifying issues and measuring conformance in comparison to WCAG standards of Kristoffersen in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen with a reasonable expectation of success of arriving at the claimed invention, for the same reasons identified in the rejection of claim 1 above.
Claim 5: Sarkar/Breeds/Kristoffersen teach claim 4. With respect to the following limitations, Sarkar/Breeds do not explicitly teach the following limitations, however, Kristoffersen teaches:
selecting, by the analysis circuitry and using the evaluation model framework, a test condition from the one or more test conditions (Kristoffersen: Figs. 1A-1B and ¶ 0093-0096 showing selection based on user input such that the system selects a web content accessibility guidance conformance level to evaluate the website on; see ¶ 0096 “By selecting one of the options 104 and/or at least one of the options 106 in the pop out window 102, the customer is indicating what accessibility issues in their website they are aiming to address (e.g., resolve, fix) or considering to address”);
generating, by the analysis circuitry and using the evaluation model framework, a baseline performance metric set under the selected test condition (Kristoffersen: ¶ 0096-0097 showing based on the selection above, a target quality score is determined that represents a score that would be achieved if the customer fixes all of the tagged issues corresponding to conformance with the selected WCAG conformance level; wherein as per ¶ 0098 the selected conformance level, i.e. test condition, corresponds to criteria based on the chosen conformance level, e.g. “ The “Level A” conformance level corresponds to issues in the website that have been tagged with “A” type issues. The “Level AA” conformance level corresponds to issues in the website that have been tagged with “A” and/or “AA” type issues. The “Level AAA” conformance level corresponds to issues in the website that have been tagged with “A,” “AA,” and/or “AAA” type issues”); and
generating, by the analysis circuitry and using the evaluation model framework, a user population performance metric set under the selected test condition (Kristoffersen: ¶ 0096, ¶ 0098, ¶ 0104 and Fig. 1D-1E showing specific set of issues presented to the user with suggestions to fix the issues to increase the conformance/quality score; also see Fig. 2A and ¶ 0144-0148 showing all potential issues displayed, i.e. a user population metric set, which may be filtered by the selected level of conformance),
wherein determining the sub-accessibility score for the interface content component is based on a comparison of the baseline performance metric set to the user population performance metric set (Kristoffersen: Fig. 1E and ¶ 0125-129 showing WCAG level A conformance issues that are detracting from the conformance score, each with an associated sub-component score that is based on their compliance with the WCAG level “A” conformance; ¶ 0098 specifying that the issues that have tagged as level “A” WCAG conformance level issues are scored based on the selected WCAG conformance level)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the processes for identifying issues to fix with respect to a selected level of conformance of Kristoffersen in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen with a reasonable expectation of success of arriving at the claimed invention, for the same reasons identified in the rejection of claim 1 above.
Note: The claims broadly recite and do not specify in any detail what a user population performance metric set or a baseline performance metric set are. Therefore, under the broadest reasonable interpretation, they may read on a set of accessibility conformance issues specific to the website (user population performance metric set) and a set of issues/criteria associated with the selected WCAG conformance level (baseline performance metric set).
Claim 8: See the rejection of claim 1 above teaching analogous limitations. Sarkar further teaches: An apparatus for evaluating interface content for a target user population, the apparatus comprising: communications hardware …and analysis circuitry (Sarkar: Col. 7: 62 – Col. 8: 61 and Fig. 4 showing device 400 including controller executing executable code in memory and input/output devices; also see Col. 11: 23-53).
Claim 10: See the rejection of claim 3 above.
Claim 11: See the rejection of claim 4 above.
Claim 12: See the rejection of claim 5 above.
Claim 15: See the rejection of claim 1 above teaching analogous limitations. Sarkar further teaches: A computer program product for evaluating interface content for a target user population, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to… (Sarkar: Col. 11: 54 – Col. 12: 11 showing “Some embodiments of the present invention may be embodied in the form of a system, a method or a computer program product. Similarly, some embodiments may be embodied as hardware, software or a combination of both. Some embodiments may be embodied as a computer program product saved on one or more non-transitory computer readable medium (or media) in the form of computer readable program code embodied thereon. Such non-transitory computer readable medium may include instructions that when executed cause a processor to execute method steps in accordance with embodiments…”).
Claim 17: See the rejection of claim 3 above.
Claim 18: See the rejection of claim 4 above.
Claim 19: See the rejection of claim 5 above.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 12211487 B1 to Sarkar et al. (Sarkar) in view of US 20120324424 A1 to Breeds et al. (Breeds), further in view of US 20230141070 A1 to Kristoffersen et al. (Kristoffersen), and further in view of US 20220229756 A1 to Sharma.
Claim 2: Sarkar/Breeds/Kristoffersen teach claim 1. With respect to the following limitations, Sarkar/Breeds/Kristoffersen do not explicitly teach the following. However, Sharma teaches:
determining, by the analysis circuitry, a device type of interest (Sharma: at least ¶ 0004-0005, ¶ 0008, ¶ 0023-0024, ¶ 0030-0040 showing determining first application data from mobile user devices using a mobile version of an application, and second application data from desktop user devices using a desktop version of an application), wherein determining the accessibility score for the interface content is further based on the device type of interest (Sharma: at least ¶ 0023-0024, ¶ 0028, ¶ 0030-0040 showing determining an experience score for the user interface that is specific to the mobile user experience, or the desktop experience)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the analysis and scoring of mobile and desktop user interface experiences separately of Sharma in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen with a reasonable expectation of success of arriving at the claimed invention, with the motivation to address the problem that “Current methods for determining the quality of a user experience with software applications exclude important factors that affect a user's experience….Some metrics fail to consider a desktop and a mobile version of an application separately. All of these methods fail to consider that a low user experience on any metric can cause an overall negative user experience” (Sharma: ¶ 0002), and “As a result, a need exists for determining a comprehensive user experience score for application users” (Sharma: ¶ 0004).
Claim 9: See the rejection of claim 2 above.
Claim 16: See the rejection of claim 2 above.
Claims 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 12211487 B1 to Sarkar et al. (Sarkar) in view of US 20120324424 A1 to Breeds et al. (Breeds), further in view of US 20230141070 A1 to Kristoffersen et al. (Kristoffersen), and further in view of US 20240394078 A1 to Bonnet et al. (Bonnet).
Claim 6: Sarkar/Breeds/Kristoffersen teach claim 1. With respect to the following limitations, Sarkar/Breeds/Kristoffersen do not explicitly teach the following limitations. However, Bonnet teaches:
identifying, by evaluation circuitry, a training interface content set comprising a plurality of training interface content (Bonnet: ¶ 0041 showing “the cloud server 118 hosts a webpage 122 having navigable content. The webpage 122 comprises content accessible to the user device via the network connection. The webpage 122 includes any type of web content. The webpage 122 can alternatively be referred to as a website, a portal page or content platform,” wherein the interactions with the content are training interface content, as seen in ¶ 0069 showing identifying specific training data including interactions with specific webpage and webpage content),
wherein (a) each training interface content comprises one or more training interface content components and (b) each training interface content comprises at least one unique training interface content component (Bonnet: ¶ 0069 showing data identifies the interactions with the webpage content, i.e. at least one unique content component; for further reference, see ¶ 0062 “The VA manager 140 observes the interaction of a user 208 with the webpage 122 in real-time as the user 208 navigates the webpage 122 via a UI of the user device 116. The VA manager 140 generates the user interaction data describing the user interaction with the webpage 122” which as per ¶ 0069 user interaction data is used as training data; also see ¶ 0020, ¶ 0046, ¶ 0064 showing the content items through which interactions occur can include icons, tabs, links, buttons, etc.);
providing, by the communications hardware, training interface content to an additional user (Bonnet: ¶ 0050 “The VA manager 140 generates user interaction data 128 describing user interaction with the webpage 122 in real-time as the user navigates the webpage 122 via the user interface (UI) 120 associated with the user device 116…”), wherein the additional user is associated with the user population; and receiving, by the communications hardware, a user response to the provided training interface content (Bonnet: ¶ 0069 “The per-user data 310 is user-specific data associated with visually impaired users and non-visually impaired users. The per-user data 310 optionally includes historical user interaction data associated with users navigating the content of the webpage, and/or per-user customized website settings. The per-user data 310 may be stored in a profile 312. This data trains the ML model 202 for the specific webpage and webpage content for improved accuracy”; also see ¶ 0068 “learning from feedback 304. The feedback 304 is provided by users and/or from other trained ML models in a feedback loop. The trained ML model 202 updates the criteria 132, including one or more rule(s) 306, used to detect visual impairments in users based on the feedback 304”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the presentation and monitoring of user interactions with specific elements of content of the webpage, which is then used as training data to train machine learning models to detect visual impairment, of Bonnet in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen with a reasonable expectation of success of arriving at the claimed invention, with the motivation that “Automatic prediction of the presence or level of visual impairment of the user is performed by the ML model using available user-specific interaction data. In some examples, this enables more accurate and efficient determination of visual impairment without employing typical visual assessment tests to further reduce system processor load and network resource usage” (Bonnet: ¶ 0024) and that “This data trains the ML model 202 for the specific webpage and webpage content for improved accuracy in generating a VA prediction 314. Detecting visual impairments and/or modifying settings of the webpage improve navigability to accommodate different types of visual impairment” (Bonnet: ¶ 0069).
Claim 7: Sarkar/Breeds/Kristoffersen/Bonnet teach claim 6. With respect to the following limitations, Sarkar/Breeds/Kristoffersen do not explicitly teach the following limitations. However, Bonnet teaches:
generating, by the evaluation circuitry, a user performance training set comprising (i) the training interface content, (ii) the user population, and (iii) the user response (Bonnet: ¶ 0069 “The training data 308 in this example includes annotated data identifying detected user interactions associated with one or more visual impairments. The training data in other examples includes web traffic historical data and per-user data 310. The per-user data 310 is user-specific data associated with visually impaired users and non-visually impaired users. The per-user data 310 optionally includes historical user interaction data associated with users navigating the content of the webpage, and/or per-user customized website settings” and ¶ 0068 “The feedback 304 is provided by users and/or from other trained ML models in a feedback loop”); and
training, by training circuitry, one or more models included in the evaluation model framework based on the user performance training set (Bonnet: ¶ 0069 “This data trains the ML model 202 for the specific webpage and webpage content for improved accuracy in generating a VA prediction 314”; see Fig. 2 showing the ML model is implemented on server 204)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the generation of training data used for training the machine learning models based on user interactions with content of the webpage, which is then used as training data to train machine learning models to detect visual impairment, of Bonnet in the website accessibility scoring system of Sarkar/Breeds/Kristoffersen/Bonnet with a reasonable expectation of success of arriving at the claimed invention, for the same reasons described in the rejection of claim 6 above.
Claim 13: See the rejection of claim 6 above.
Claim 14: See the rejection of claim 7 above.
Claim 20: See the rejection of claim 6 above.
Conclusion
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/HUNTER MOLNAR/Examiner, Art Unit 3628
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