DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .2. This action is responsive to the following communication: Amendment filed 07/22/26. This action is made final.
3. Claims 1-11, 15-17 and 21-26 are pending in the case. Claims 1, 9 and 16 are independent claims
Claim Rejections - 35 USC § 102
4. Claims 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
5. 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)(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.
6. Claim 1, 4, 6, 7 and 21 are rejected under 35 U.S.C. 102(a)(1) as being rejected by anticipated by Han (US 20150026166).
Regarding claim 1, Han discloses a method comprising:
identifying, by a processing device, a first instance of digital content displayed in a user interface (see FIG. 3 wherein an input query is made as seen in FIG. 5a);
extracting, by the processing device, a tag from metadata associated with the first instance of digital content, the tag including one or more keywords that identify characteristics of the first instance of digital content (see FIG. 3 wherein a tag is acquired from contents);
selecting, by the processing device based on the tag, an option from a plurality of options to generate a second instance of digital content based on the one or more keywords, each of the plurality of options configured, respectively, to use generative artificial intelligence as implemented by at least one machine-learning model (see FIG. 3 wherein context information is gathered and context information similarity is calculated);
presenting, by the processing device, a menu for display in the user interface having a recommendation of the option that is user selectable to initiate generation of the second instance of digital content (see FIG. 3 wherein contents are ranked and displayed); and
presenting, by the processing device, the second instance of digital content for display as generated using the generative artificial intelligence that corresponds to the option, the second instance of digital content generated based on the one or more keywords (see FIG. 3 wherein contents are ranked and displayed accordingly).
Regarding claim 4, Han discloses wherein the selecting is based on characteristics of the first instance of digital content indicated by the tag (see at least FIG. 3 wherein tag information of contents is gathered).
Regarding claim 6, Han discloses wherein the first instance of digital content and the second instance of digital content are digital images (FIGS. 5A-5C display photos, see also paragraph 0085).
Regarding claim 7, Han discloses wherein the presenting of the menu is performed non-modally in the user interface (FIG. 5A, the photo contents having the highest ranking among the retrieved contents are displayed largest on the screen and the contents having the second highest ranking are displayed small on a lower end of the screen, see also paragraph 0086).
Regarding claim 21, Han disclose wherein the selecting is performed by a generative AI assistant module based on a type of the digital content identified by the tag (see FIG. 3 wherein context information is gathered and context information similarity is calculated).
Claim Rejections - 35 USC § 103
7. 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.
8. Claim 2, 3, 5 are rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Jiao (US 20240046037).
Regarding claim 2, Han does not disclose further comprising receiving an additional keyword from an input source and wherein the second instance of digital content is generated by the generative artificial intelligence using the at least one machine-learning model based on the one or more keywords and an additional keyword.
However, Jiao discloses wherein the generative keyword retriever 124 may perform generative retrieval of keywords based on the received query by expanding the query and retrieving data from the data server 160. The trained model 126 (e.g., a trained language generation model) may perform the expansion of the received query into a set of queries by predicting words of the queries based on the receive query. The answer provider 128 may provide answers (e.g., results) to the query to the data retriever 112 on the interactive browser 104 in the client device 102. The data viewer 114 may receive the results to the query for viewing by the user using the interactive browser 104 on the client device 102 (paragraph 0028).
The combination of Han and Jiao would have resulted in the contextual menu generation of Han to utilize Jiao’s teachings of generative keywords. One would have been motivated to have combined the teachings because a user of Han is already interested in creating similarity based keywords and utilizing generative intelligence would have made the similarity analysis more efficient. As such, the combination of references would have been obvious to one of ordinary skill and the resulting invention would have been predictable.
Regarding claim 3, Han does not disclose wherein the additional keyword is determined based on analytics data describing interactions of entities included in an entity segment with instances of digital content
However, Jiao discloses wherein the generative keyword retriever 124 may perform generative retrieval of keywords based on the received query by expanding the query and retrieving data from the data server 160. The trained model 126 (e.g., a trained language generation model) may perform the expansion of the received query into a set of queries by predicting words of the queries based on the receive query. The answer provider 128 may provide answers (e.g., results) to the query to the data retriever 112 on the interactive browser 104 in the client device 102. The data viewer 114 may receive the results to the query for viewing by the user using the interactive browser 104 on the client device 102 (paragraph 0028).
The combination of Han and Jiao would have resulted in the contextual menu generation of Han to utilize Jiao’s teachings of generative keywords. One would have been motivated to have combined the teachings because a user of Han is already interested in creating similarity based keywords and utilizing generative intelligence would have made the similarity analysis more efficient. As such, the combination of references would have been obvious to one of ordinary skill and the resulting invention would have been predictable.
Regarding claim 5, Han does not disclose wherein the second instance of digital content is generated by the generative artificial intelligence by the at least one machine-learning model independent of access to the first instance of digital content, itself.
However, Jiao discloses wherein the generative keyword retriever 124 may perform generative retrieval of keywords based on the received query by expanding the query and retrieving data from the data server 160. The trained model 126 (e.g., a trained language generation model) may perform the expansion of the received query into a set of queries by predicting words of the queries based on the receive query. The answer provider 128 may provide answers (e.g., results) to the query to the data retriever 112 on the interactive browser 104 in the client device 102. The data viewer 114 may receive the results to the query for viewing by the user using the interactive browser 104 on the client device 102 (paragraph 0028).
The combination of Han and Jiao would have resulted in the contextual menu generation of Han to utilize Jiao’s teachings of generative keywords. One would have been motivated to have combined the teachings because a user of Han is already interested in creating similarity based keywords and utilizing generative intelligence would have made the similarity analysis more efficient. As such, the combination of references would have been obvious to one of ordinary skill and the resulting invention would have been predictable.
9. Claim 8 is are rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Pavlin (US 20210075887).
Regarding claim 8, Han does not disclose wherein the menu is configured as a sidebar in the user interface that is configured to display the recommendation and then the second instance of digital content.
However, Pavlin discloses wherein n the example of FIG. 4, this new microservice may be a recommendation microservice that recommends products to users in a sidebar of the user interface (paragraph 0032).
The combination of Han and Pavlin would have resulted in the contextual menu generation of Han to utilize Pavlin’s teachings of presenting it on sidebars. One would have been motivated to have combined the teachings because a user of Han is already interested in creating similarity based keywords and presenting it on sidebars would have been an obvious design variation. As such, the combination of references would have been obvious to one of ordinary skill and the resulting invention would have been predictable.
Response to Amendment
10. Applicant’s arguments with respect to claims X have been considered but are moot in view of the new grounds of rejection.
Regarding applicant’s three separate arguments the first is simply that mathematical equations are not generative AI. Second, the claims themselves do not claim that which implies that the generation of digital content is by generative AI. Third, the applicant appears to be again arguinmg that Han’s mathematical calculations do not constitute generative artificial intelligence.
In response, the Examiner disagrees that math is not generative artificial intelligence. In contrast, math is the fundamental nature of generative artificial intelligence. In fact, distilled it is basically nothing but math. The Applicant claims phrases that are essentially generic descriptions of math as “generative AI” as if this “generative AI” does more than what math already can do. Without any further clarification, the term “generative AI” is not persuasive to teach over well accepted mathematical equations unless generative AI is doing something on a fundamentally different level and as if that’s the case then that teachings is not sufficiently claimed in the subject matter.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID E CHOI whose telephone number is (571)270-3780. The examiner can normally be reached on M-F: 7-2, 7-10 (PST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on 571-431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID E CHOI/Primary Examiner, Art Unit 2148
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