DETAILED ACTION
Status of the Claims
Claims 1-20 are pending.
Notice of AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-10, and 12-20 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Kabra et al., US 2025/0061472 A1.
Regarding claim 1, Kabra discloses a system for electronic message adaptation, the system comprising:
One or more memories; and one or more processors, communicatively coupled to the one or more memories. Kabra fig. 2.
[The one or more processors] configured to: obtain interaction information associated with a first electronic message, the interaction information being associated with interaction of a user with the first electronic message. Kabra teaches collecting eye gaze data and/or click stream data for content. Kabra ¶ 61. The content may comprise “any text, images, audio, graphical elements, or the like.” Id. ¶ 55.
[The one or more processors configured to:] determine, based on the interaction information, content performance information associated with a plurality of content blocks of the first electronic message. Kabra teaches using the collected eye gaze data and/or click stream data to generate heat map data to identify content blocks (i.e., “segments of content”) to which a user’s attention is primarily directed. Kabra ¶ 62.
[The one or more processors configured to:] generate a plurality of content block interaction metrics based on the content performance information, each content block interaction metric in the plurality of content block interaction metrics being associated with a respective content block of the plurality of content blocks. Kabra teaches using the heat map data to score the content blocks based on the amount of user attention. Kabra ¶ 62.
[The one or more processors configured to:] generate content priority information based on the plurality of content block interaction metrics. Kabra teaches using the scores of the content blocks to train a machine learning model. Kabra ¶¶ 64-65. The machine learning model may be used to predict attention scores for content blocks of new content. Id. ¶ 66. The attention scores may be used to determine a final score for each content block of new content. Id. ¶ 67. The scores for each of the content blocks of the new content may be used to rank the content blocks relative to one another. Id.
[The one or more processors configured to:] generate content adaptation information associated with the user based on the content priority information. Kabra teaches using the rankings to identify content blocks to render and content blocks to compact. Kabra ¶ 68.
[The one or more processors configured to:] cause a second electronic message for the user to be generated based on the content adaptation information. Kabra teaches displaying modified new content according to the identified content blocks. Kabra ¶ 68.
Regarding claim 3, which depends on claim 1, Kabra discloses wherein the one or more processors, to generate the content priority information, are configured to: identify a content block for which a content block interaction metric satisfies a threshold; and generate the content priority information to include an indication that content related to the identified content block is high priority content. Kabra teaches identifying a threshold number of high priority (i.e., “top ranked”) content blocks. Kabra ¶ 67.
Regarding claim 4, which depends on claim 1, Kabra discloses wherein the one or more processors, to generate the content priority information, are configured to: identify a content block for which a content block interaction metric fails to satisfy a threshold; and generate the content priority information to include an indication that content related to the identified content block is low priority content. Kabra teaches identifying low ranked content blocks. Kabra ¶ 67.
Regarding claim 5, which depends on claim 1, Kabra discloses wherein the content adaptation information includes an indication that content associated with a particular content block of the plurality of content blocks is to not to be included in the second electronic message. Kabra teaches identifying content blocks for compaction. Kabra ¶ 67.
Regarding claim 6, which depends on claim 1, Kabra discloses wherein the content adaptation information includes an indication that additional content related to content associated with a particular content block of the plurality of content blocks is to be included in the second electronic message. Kabra teaches identifying multiple content blocks to be included in the displayed content. Kabra ¶¶ 67-68.
Regarding claim 7, which depends on claim 1, Kabra discloses wherein the content adaptation information includes an indication that content associated with two or more content blocks of the plurality of content blocks is to be reordered in the second electronic message. Kabra teaches using the rankings to identify the top ranked content blocks to render. Kabra ¶ 68. This constitutes a reordering of content blocks.
Regarding claim 8, which depends on claim 1, Kabra discloses: obtain second interaction information associated with the second electronic message; determine, based on the second interaction information, second content performance information associated with a plurality of content blocks of the second electronic message; generate a second plurality of content block interaction metrics based on the second content performance information; generate second content priority information based on the second plurality of content block interaction metrics; and update the content adaptation information associated with the user based on the second content priority information. Kabra teaches retraining the machine learning model based on data gathered for a particular user. Kabra ¶ 22.
Regarding claim 9, Kabra discloses a method for electronic message adaptation, comprising:
Obtaining, by a system, interaction information associated with an electronic message, the interaction information including information associated with interaction of a user with the electronic message. Kabra teaches collecting eye gaze data and/or click stream data for content. Kabra ¶ 61. The content may comprise “any text, images, audio, graphical elements, or the like.” Id. ¶ 55.
Determining, by the system, content performance information based on the interaction information, the content performance information including information associated with performance of a plurality of content blocks of the electronic message with respect to user interaction. Kabra teaches using the collected eye gaze data and/or click stream data to generate heat map data to identify content blocks (i.e., “segments of content”) to which a user’s attention is primarily directed. Kabra ¶ 62.
Generating, by the system, a plurality of content block interaction metrics based on the content performance information, each content block interaction metric in the plurality of content block interaction metrics being associated with a respective content block of the plurality of content blocks. Kabra teaches using the heat map data to score the content blocks based on the amount of user attention. Kabra ¶ 62.
Generating, by the system, content priority information based on the plurality of content block interaction metrics. Kabra teaches using the scores of the content blocks to train a machine learning model. Kabra ¶¶ 64-65. The machine learning model may be used to predict attention scores for content blocks of new content. Id. ¶ 66. The attention scores may be used to determine a final score for each content block of new content. Id. ¶ 67. The scores for each of the content blocks of the new content may be used to rank the content blocks relative to one another. Id.
Generating, by the system, content adaptation information associated with the user based on the content priority information. Kabra teaches using the rankings to identify content blocks to render and content blocks to compact. Kabra ¶ 68.
Regarding claim 10, which depends on claim 9, Kabra discloses causing another electronic message to be generated based on the content adaptation information. Kabra teaches displaying modified new content according to the identified content blocks. Kabra ¶ 68.
Regarding claim 12, which depends on claim 9, Kabra discloses wherein generating the content priority information comprises: determining whether a content block interaction metric associated with a content block satisfies a threshold; and generating the content priority information to include an indication of a priority of content related to the content block based on whether the content block interaction metric satisfies the threshold. Kabra teaches identifying a threshold number of high priority (i.e., “top ranked”) content blocks. Kabra ¶ 67.
Regarding claim 13, which depends on claim 9, Kabra discloses wherein the content adaptation information includes an indication of whether content associated with a particular content block of the plurality of content blocks is to be included in another electronic message to be provided to the user. Kabra teaches identifying a content block to be included in the displayed content. Kabra ¶¶ 67-68.
Regarding claim 14, which depends on claim 9, Kabra discloses wherein the content adaptation information includes information associated with reordering content associated with two or more content blocks of the plurality of content blocks in another electronic message to be provided to the user. Kabra teaches using the rankings to identify the top ranked content blocks to render. Kabra ¶ 68. This constitutes a reordering of content blocks.
Regarding claim 15, which depends on claim 9, Kabra discloses: obtaining second interaction information associated with a second electronic message provided to the user; determining, based on the second interaction information, second content performance information associated with a plurality of content blocks of the second electronic message; generating a second plurality of content block interaction metrics based on the second content performance information; generating second content priority information based on the second plurality of content block interaction metrics; and updating the content adaptation information associated with the user based on the second content priority information. Kabra teaches retraining the machine learning model based on data gathered for a particular user. Kabra ¶ 22.
Regarding claim 16, Kabra discloses a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a system, cause the system to:
Obtain interaction information associated with a first electronic message provided to a user. Kabra teaches collecting eye gaze data and/or click stream data for content. Kabra ¶ 61. The content may comprise “any text, images, audio, graphical elements, or the like.” Id. ¶ 55.
Determine, based on the interaction information, content performance information associated with a plurality of content blocks of the first electronic message. Kabra teaches using the collected eye gaze data and/or click stream data to generate heat map data to identify content blocks (i.e., “segments of content”) to which a user’s attention is primarily directed. Kabra ¶ 62.
Generate content priority information based on a plurality of content block interaction metrics generated based on the content performance information. Kabra teaches using the heat map data to score the content blocks based on the amount of user attention. Kabra ¶ 62.
Generate or update content adaptation information associated with the user based on the content priority information. Kabra teaches using the rankings to identify content blocks to render and content blocks to compact. Kabra ¶ 68.
Provide the content adaptation information in association with modifying the first electronic message or generating a second electronic message associated with the user. Kabra teaches displaying modified new content according to the identified content blocks. Kabra ¶ 68.
Regarding claim 17, which depends on claim 16, Kabra discloses wherein the one or more instructions, that cause the system to generate the content priority information, cause the system to generate the content priority information based on a determination of whether a content block interaction metric associated with a content block from the plurality of content blocks satisfies a threshold. Kabra teaches identifying a threshold number of high priority (i.e., “top ranked”) content blocks. Kabra ¶ 67.
Regarding claim 18, which depends on claim 16, Kabra discloses wherein the content adaptation information includes an indication of whether content associated with a particular content block of the plurality of content blocks is to be removed from the first electronic message or omitted from the second electronic message. Kabra teaches identifying content blocks for compaction. Kabra ¶ 67.
Regarding claim 19, which depends on claim 16, Kabra discloses, wherein the content adaptation information includes an indication that content associated with two or more content blocks of the plurality of content blocks is to be reordered in the first electronic message or in the second electronic message. Kabra teaches using the rankings to identify the top ranked content blocks to render. Kabra ¶ 68. This constitutes a reordering of content blocks.
Regarding claim 20, which depends on claim 16, Kabra discloses: obtain second interaction information associated with the second electronic message; determine, based on the second interaction information, second content performance information associated with a plurality of content blocks of the second electronic message; generate second content priority information based on a second plurality of content block interaction metrics generated based on the second content performance information; and update the content adaptation information associated with the user based on the second content priority information; and provide the updated content adaptation information in association with modifying the first electronic message, modifying the second electronic message, or generating a third electronic message associated with the user. Kabra teaches retraining the machine learning model based on data gathered for a particular user. Kabra ¶ 22.
Claim Rejections - 35 USC § 103
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 of this title, 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 2 and 11 are rejected under 35 U.S.C. § 103 as being unpatentable over Kabra et al., US 2025/0061472 A1, in view of Zhang et al., US 2021/0004437 A1.
Regarding claim 2, which depends on claim 1, Kabra does not explicitly disclose, but Zhang discloses wherein the content adaptation information is provided to cause the first electronic message to be modified based on the content adaptation information. Zhang discloses using a message effectiveness prediction for a first message to remove elements from the first message to form a new second message. Zhang ¶ 103, fig. 9.
It would have been obvious before the effective filing date of the claimed invention to a person with ordinary skill in the art to modify Kabra’s process of generating content adaption information with Zhang’s process of using content adaption information to modify a message. Such a modification would aid in the publication of more effective advertising messages. See Zhang ¶ 4.
Regarding claim 11, which depends on claim 9, Kabra does not explicitly disclose, but Zhang discloses causing the electronic message to be modified based on the content adaptation information. Zhang discloses using a message effectiveness prediction for a first message to remove elements from the first message to form a new second message. Zhang ¶ 103, fig. 9.
It would have been obvious before the effective filing date of the claimed invention to a person with ordinary skill in the art to modify Kabra’s process of generating content adaption information with Zhang’s process of using content adaption information to modify a message. Such a modification would aid in the publication of more effective advertising messages. See Zhang ¶ 4.
Conclusion
Although particular portions of the prior art may have been cited in support of the rejections, the specified citations are merely representative of the teachings. Other passages and figures in the cited prior art may apply. Accordingly, Applicant should consider the entirety of the cited prior art for potentially teaching all or part of the claims.
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Li et al., US 2024/0202803 A1, discloses modifying message content based on interaction with the message.
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Asher D. Kells
Primary Examiner
Art Unit 2171
/Asher D Kells/Primary Examiner, Art Unit 2171