DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/16/2026 has been entered.
Response to Arguments
3. Applicant’s arguments (see Remarks dated 03/16/2026) with respect to claims 1-2, 5-9, 12-16, and 19-21 have been fully considered, but they are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 USC 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-2, 5-9, 12-16, and 19-26 are rejected under 35 USC 102(a)(1) as being anticipated by Sinclair (GB 2577274 A).
Regarding claim 1, Sinclair discloses a method comprising:
analyzing, by a processor (claim 14, microprocessor), readable text in a first digital content displayed in a user interface for color deficiencies (page 1 lines 5-6, “automatically finding optimal…colors…for captioning an image or video content, so that the text appearance is readable”) based on a user profile (page 29 line 24, “color preferences can be tracked and served to user devices”);
determining, by the processor, based on the analyzing, a first color deficiency is present based on the user profile associated with a user viewing the readable text in the first digital content (page 27 lines 26-28, “color tests to determine if the user has some form of color blindness such as red-green can be displayed. For example…a colored number”);
identifying, by the processor, a first color substitution for the first color deficiency based on the user profile (page 12 lines 18-19, “the best of the cluster colors is used to replace it for that text location”), wherein the user profile includes a color palette with the first color substitution (page 27 lines 21-22, “a palette of colors can be shown and a user can be asked to select their favorite color”) from a plurality of variations of a plurality of recognizable colors by the user (page 12 lines 17, “for each color, it is compared to its cluster colors”);
displaying, by the processor, the first color substitution for the first color deficiency as an overlay on the first digital content (Abstract, “Overlaying text content”);
receiving a feedback selection from the user that includes a manual color substitution for the first color deficiency as the overlay on the first digital content (page 19 lines 29-30, “a user can manually select a color from the underlying image for rendering of the overlaid text”);
determining, based on iterative machine learning through the feedback selection from the user, which color substitution are preferred by the user (page 11 lines 16-17, “machine learning component may be implemented remotely, such as by preference server 100A”) for each device from a plurality of devices, wherein the plurality of devices includes a client device with the manual color substitution for the first color deficiency (page 27 lines 3-5, “information regarding color preference can be leveraged across a plurality of applications and devices”) as the overlay on the first digital content (page 28 lines 8-9, “image-text overlay applications described herein”); and
storing as preferences for each device from the plurality of devices for future color substitutions on digital content displayed on each device from the plurality of devices (page 29 lines 7-10, “color preferences are stored…for future use so when…information is requested, the results can be quickly looked up and returned…to the client application”).
Regarding claim 2, Sinclair discloses identifying, by the processor, the user viewing the first digital content based on the user profile associated with the client device with the user interface (page 27 lines 33-34, “records associated with the user for whom the collected color preference information applies…can be their Facebook ID, email address, phone number or other common ID”).
Regarding claim 5, Sinclair discloses determining, by the processor, one or more color deficiencies for the user based on a color vision deficiency test performed by the user, wherein the one or more color deficiencies includes the first color deficiency (page 27 line 26, “color tests to determine if the user has some form of color blindness”);
determining, by the processor, the plurality of recognizable colors by the user based on the color vision deficiency test (page 6, lines 24, “final global list of ‘good’ filtered unique colors); and
storing, by the processor, results from the color vision deficiency test as the user profile, wherein the results include the one or more color deficiencies for the user (claim 9, “colors determined to be undesirable based on prior interactions of a user with an application”) and the plurality of recognizable colors by the user (page 6, lines 24-25, “list of ‘good’ filtered unique colors…is saved for later use”).
Regarding claim 6, Sinclair discloses generating, by the processor, the color palette with the plurality of variations of the plurality of recognizable colors by the user (Fig. 7, 720; page 16 lines 31-32, “720 illustrates other preferred colors for the current text”), wherein each recognizable color from the plurality of recognizable colors includes a subset of variations from the plurality of variations (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected…color”).
Regarding claim 7, Sinclair discloses determining, by the processor, to customize the color palette (page 27 lines 21-22, “a palette of colors…to select”);
displaying, by the processor, a first subset of variations from the plurality of variations of a first recognizable color from the plurality of recognizable colors (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected blue color”);
receiving, by the processor, a user color substitution for the color palette that includes adjusting a shade of at least one variation from the first subset of variations from the plurality of variations of the first recognizable color from the plurality of recognizable colors (page 8 line 33, “Each of these effects may use a grayscale shade from white to black”), wherein the user color substitution is the first color substitution (Fig. 7);
storing, by the processor, the user color substitution (page 28 lines 10-11, “selected text color…is transmitted to server 100A”); and
updating, by the processor, the user profile based on the color palette (Fig. 24, S2435).
Regarding claim 8, Sinclair discloses a computer program product comprising:
one or more computer readable storage media (Fig. 1B, Data Storage 170); and
program instructions stored on the one or more computer readable storage media to perform operations (Fig. 1B, Application 170A) comprising:
analyzing readable text in a first digital content displayed in a user interface for color deficiencies (page 1 lines 5-6, “automatically finding optimal…colors…for captioning an image or video content, so that the text appearance is readable”) based on a user profile (page 29 line 24, “color preferences can be tracked and served to user devices”);
determining a first color deficiency is present based on the user profile associated with a user viewing the readable text in the first digital content (page 27 lines 26-28, “color tests to determine if the user has some form of color blindness such as red-green can be displayed. For example…a colored number”);
identifying a first color substitution for the first color deficiency based on the user profile (page 12 lines 18-19, “the best of the cluster colors is used to replace it for that text location”), wherein the user profile includes a color palette with the first color substitution (page 27 lines 21-22, “a palette of colors can be shown and a user can be asked to select their favorite color”) from a plurality of variations of a plurality of recognizable colors by the user (page 12 lines 17, “for each color, it is compared to its cluster colors”);
displaying the first color substitution for the first color deficiency as an overlay on the first digital content (Abstract, “Overlaying text content”);
receiving a feedback selection from the user that includes a manual color substitution for the first color deficiency as the overlay on the first digital content (page 19 lines 29-30, “a user can manually select a color from the underlying image for rendering of the overlaid text”);
determining, based on iterative machine learning through the feedback selection from the user, which color substitution are preferred by the user (page 11 lines 16-17, “machine learning component may be implemented remotely, such as by preference server 100A”) for each device from a plurality of devices, wherein the plurality of devices includes a client device with the manual color substitution for the first color deficiency(page 27 lines 3-5, “information regarding color preference can be leveraged across a plurality of applications and devices”) as the overlay on the first digital content (page 28 lines 8-9, “image-text overlay applications described herein”); and
storing as preferences for each device from the plurality of devices for future color substitutions on digital content displayed on each device from the plurality of devices (page 29 lines 7-10, “color preferences are stored…for future use so when…information is requested, the results can be quickly looked up and returned…to the client application”).
Regarding claim 9, Sinclair discloses identifying the user viewing the first digital content based on the user profile associated with the client device with the user interface (page 27 lines 33-34, “records associated with the user for whom the collected color preference information applies…can be their Facebook ID, email address, phone number or other common ID”).
Regarding claim 12, Sinclair discloses determining one or more color deficiencies for the user based on a color vision deficiency test performed by the user, wherein the one or more color deficiencies includes the first color deficiency (page 27 line 26, “color tests to determine if the user has some form of color blindness”);
determining the plurality of recognizable colors by the user based on the color vision deficiency test (page 6, lines 24, “final global list of ‘good’ filtered unique colors); and
storing results from the color vision deficiency test as the user profile, wherein the results include the one or more color deficiencies for the user (claim 9, “colors determined to be undesirable based on prior interactions of a user with an application”) and the plurality of recognizable colors by the user (page 6, lines 24-25, “list of ‘good’ filtered unique colors…is saved for later use”).
Regarding claim 13, Sinclair discloses generating the color palette with the plurality of variations of the plurality of recognizable colors by the user (Fig. 7, 720; page 16 lines 31-32, “720 illustrates other preferred colors for the current text”), wherein each recognizable color from the plurality of recognizable colors includes a subset of variations from the plurality of variations (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected…color”).
Regarding claim 14, Sinclair discloses determining to customize the color palette (page 27 lines 21-22, “a palette of colors…to select”);
displaying a first subset of variations from the plurality of variations of a first recognizable color from the plurality of recognizable colors (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected blue color”);
receiving a user color substitution for the color palette that includes adjusting a shade of at least one variation from the first subset of variations from the plurality of variations of the first recognizable color from the plurality of recognizable colors (page 8 line 33, “Each of these effects may use a grayscale shade from white to black”), wherein the user color substitution is the first color substitution (Fig. 7);
storing the user color substitution (page 28 lines 10-11, “selected text color…is transmitted to server 100A”); and
updating the user profile based on the color palette (Fig. 24, S2435).
Regarding claim 15, Sinclair discloses a computer system comprising:
a processor set (claim 14, microprocessor);
one or more computer readable storage media (Fig. 1B, Data Storage 170); and
program instructions stored on the one or more computer readable storage media to cause the processor set to perform operations (Fig. 1B, Application 170A) comprising:
analyzing readable text in a first digital content displayed in a user interface for color deficiencies (page 1 lines 5-6, “automatically finding optimal…colors…for captioning an image or video content, so that the text appearance is readable”) based on a user profile (page 29 line 24, “color preferences can be tracked and served to user devices”);
determining a first color deficiency is present based on the user profile associated with a user viewing the readable text in the first digital content (page 27 lines 26-28, “color tests to determine if the user has some form of color blindness such as red-green can be displayed. For example…a colored number”);
identifying a first color substitution for the first color deficiency based on the user profile (page 12 lines 18-19, “the best of the cluster colors is used to replace it for that text location”), wherein the user profile includes a color palette with the first color substitution (page 27 lines 21-22, “a palette of colors can be shown and a user can be asked to select their favorite color”) from a plurality of variations of a plurality of recognizable colors by the user (page 12 lines 17, “for each color, it is compared to its cluster colors”);
displaying the first color substitution for the first color deficiency as an overlay on the first digital content (Abstract, “Overlaying text content”);
receiving a feedback selection from the user that includes a manual color substitution for the first color deficiency as the overlay on the first digital content (page 19 lines 29-30, “a user can manually select a color from the underlying image for rendering of the overlaid text”);
determining, based on iterative machine learning through the feedback selection from the user, which color substitution are preferred by the user (page 11 lines 16-17, “machine learning component may be implemented remotely, such as by preference server 100A”) for each device from a plurality of devices, wherein the plurality of devices includes a client device with the manual color substitution for the first color deficiency (page 27 lines 3-5, “information regarding color preference can be leveraged across a plurality of applications and devices”) as the overlay on the first digital content (page 28 lines 8-9, “image-text overlay applications described herein”); and
storing as preferences for each device from the plurality of devices for future color substitutions on digital content displayed on each device from the plurality of devices (page 29 lines 7-10, “color preferences are stored…for future use so when…information is requested, the results can be quickly looked up and returned…to the client application”).
Regarding claim 16, Sinclair discloses identifying the user viewing the first digital content based on the user profile associated with the client device with the user interface (page 27 lines 33-34, “records associated with the user for whom the collected color preference information applies…can be their Facebook ID, email address, phone number or other common ID”).
Regarding claim 19, Sinclair discloses determining one or more color deficiencies for the user based on a color vision deficiency test performed by the user, wherein the one or more color deficiencies includes the first color deficiency (page 27 line 26, “color tests to determine if the user has some form of color blindness”);
determining the plurality of recognizable colors by the user based on the color vision deficiency test (page 6, lines 24, “final global list of ‘good’ filtered unique colors); and
storing results from the color vision deficiency test as the user profile, wherein the results include the one or more color deficiencies for the user (claim 9, “colors determined to be undesirable based on prior interactions of a user with an application”) and the plurality of recognizable colors by the user (page 6, lines 24-25, “list of ‘good’ filtered unique colors…is saved for later use”).
Regarding claim 20, Sinclair discloses generating the color palette with the plurality of variations of the plurality of recognizable colors by the user (Fig. 7, 720; page 16 lines 31-32, “720 illustrates other preferred colors for the current text”), wherein each recognizable color from the plurality of recognizable colors includes a subset of variations from the plurality of variations (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected…color”).
Regarding claim 21, Sinclair discloses determining to customize the color palette (page 27 lines 21-22, “a palette of colors…to select”);
displaying a first subset of variations from the plurality of variations of a first recognizable color from the plurality of recognizable colors (Fig. 7, 710; page 16 lines 29-30, “710 presents other variations of the selected blue color”);
receiving a user color substitution for the color palette that includes adjusting a shade of at least one variation from the first subset of variations from the plurality of variations of the first recognizable color from the plurality of recognizable colors (page 8 line 33, “Each of these effects may use a grayscale shade from white to black”), wherein the user color substitution is the first color substitution (Fig. 7);
storing the user color substitution (page 28 lines 10-11, “selected text color…is transmitted to server 100A”); and
updating the user profile based on the color palette (Fig. 24, S2435).
Regarding claim 22, Sinclair discloses identifying, utilizing the feedback selection from the user, a pattern for color substitutions by the user for the first color deficiency (page 28 lines 22-24, “data received from user devices…is stored…for future use, including analysis and processing by Color Preference Server application logic 102”), wherein the pattern for the color substitutions by the user includes a first future color substitution for color deficiencies similar to the first color deficiency and similar to the first digital content (Fig. 7); and
performing, based on the pattern for the color substitutions by the user, the first future color substitution for a second digital content similar to the first digital content, the second digital content having a second color deficiency similar to the first color deficiency (page 12 lines 17-19, “for each color, it is compared to its cluster colors and…then the best of the cluster colors is used to replace it for that text location”).
Regarding claim 23, Sinclair discloses determining, utilizing optical character recognition (Abstract, “text colour is compute[d] automatically for a given location…based on…perceived brightness”; optical character recognition is performed when a user optically recognizes the brightness of a character), where to display the first color substitution for the first color deficiency, wherein the overlay covers each letter, character, or number with the first color substitution (Abstract, “Overlaying text content…on a digital image”).
Regarding claim 24, Sinclair discloses identifying, utilizing the feedback selection from the user, a pattern for color substitutions by the user for the first color deficiency (page 28 lines 22-24, “data received from user devices…is stored…for future use, including analysis and processing by Color Preference Server application logic 102”), wherein the pattern for the color substitutions by the user includes a first future color substitution for color deficiencies similar to the first color deficiency and similar to the first digital content (Fig. 7); and
performing, based on the pattern for the color substitutions by the user, the first future color substitution for a second digital content similar to the first digital content, the second digital content having a second color deficiency similar to the first color deficiency (page 12 lines 17-19, “for each color, it is compared to its cluster colors and…then the best of the cluster colors is used to replace it for that text location”).
Regarding claim 25, Sinclair discloses determining, utilizing optical character recognition (Abstract, “text colour is compute[d] automatically for a given location…based on…perceived brightness”; optical character recognition is performed when a user optically recognizes the brightness of a character), where to display the first color substitution for the first color deficiency, wherein the overlay covers each letter, character, or number with the first color substitution (Abstract, “Overlaying text content…on a digital image”).
Regarding claim 26, Sinclair discloses identifying, utilizing the feedback selection from the user, a pattern for color substitutions by the user for the first color deficiency (page 28 lines 22-24, “data received from user devices…is stored…for future use, including analysis and processing by Color Preference Server application logic 102”), wherein the pattern for the color substitutions by the user includes a first future color substitution for color deficiencies similar to the first color deficiency and similar to the first digital content (Fig. 7); and
performing, based on the pattern for the color substitutions by the user, the first future color substitution for a second digital content similar to the first digital content, the second digital content having a second color deficiency similar to the first color deficiency (page 12 lines 17-19, “for each color, it is compared to its cluster colors and…then the best of the cluster colors is used to replace it for that text location”).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Reference Fink et al. (US 20200305707 A1) discloses an apparatus, software, and methods for assessing ocular/ophthalmic conditions (including color vision), based on user feedback. This corresponds to applicant’s independent claims 1, 8, and 15.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniel Jeffery Jordan whose telephone number is 571-270-7641. The examiner can normally be reached 9:30a-6:00p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephone Allen can be reached at 571-272-2434. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D. J. J./Examiner, Art Unit 2872
/STEPHONE B ALLEN/Supervisory Patent Examiner, Art Unit 2872
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