Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The following is a FINAL Office action in reply to the Amendments and Arguments received on October 28, 2025.
Status of Claims
Claims 1, 4, 11, 14 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 11-19 are drawn to methods while claim(s) 1-10 and 20 are drawn to an apparatus. As such, claims 1-20 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 11 (representative of independent claim(s) 1 and 20) recites the following steps:
accessing content associated with a first entity;
identifying a second entity associated with the first entity, the second entity to be presented with information based on the content;
determining a current intent for the second entity for engaging with an online system; and
training a plurality of generative models to generate, for a same content, different content summaries, each model of the plurality of models generating content summaries personalized for a different known entity intent;
passing the content into a selected model of the plurality of models, the selected model trained specifically to generate summaries for users with the determined current intent, the GAI model generating, for the second entity, a summary of the content based on the determined intent and the content;
These steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) creating personalized summaries for viewers based at least partially on viewer intent (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass mathematical relationships. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 11 recites an abstract idea (Step 2A - Prong One: YES).
Independent claims 1 and 20 are determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
An online system
a generative artificial intelligence (GAI) model
user interface
A system comprising: at least one processor; and at least one non-transitory computer-readable medium having instructions stored thereon, which, when executed by the at least one processor, cause the system to perform operations comprising:
A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Alternatively, “Step 2A - Prong 2”, the recited additional element(s) of "changing a user interface (UI) element displayed to the second entity to include the summary generated by the selected GAI model" serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
As discussed above in “Step 2A - Prong 2”, the recited additional element(s) of "changing a user interface (UI) element displayed to the second entity to include the summary generated by the selected GAI model" alternatively serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more5' (see MPEP 2106.05(g, h)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 2, 7, 9, 12, 17 and 19 fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 3-6, 8, 10, 13-16, and 18 include additional limitations that are part of the abstract idea except for:
a generative artificial intelligence (GAI) model
a machine learning model
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
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)(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.
(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 1-20 are rejected under 35 U.S.C. 102(a)(1)/ (a)(2) as being anticipated by Peng et al. (2019/0325084)
Claims 1, 11 and 20
Peng discloses generating user profile summaries based on user intent:
A system comprising: at least one processor; and at least one non-transitory computer-readable medium having instructions stored thereon, which, when executed by the at least one processor, cause the system to perform operations comprising (Peng [0108][0115]):
A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising (Peng [0115]):
accessing content associated with a first entity (Peng [0074]); See at least “where the assistant system 140 may receive, from a client system 130 associated with a first user, a user request for a summarization of a particular type of content [content associated with a first entity] objects for the first user.”
identifying a second entity associated with the first entity, the second entity to be presented with information based on the content (Peng [0007]); See at least “identify user interests based on the user profile and contextual information of the user, determine one or more modalities for the summarization based on the contextual information of the user request and the user's client system, generate a summary for each of the contents in a personalized and context-aware manner, generate a digest of all the summaries based on the identified interests in a personalized and context-aware manner, and send the digest via the determined modalities to the user.
determining a current intent for the second entity for engaging with an online system (Peng [0040]) See at least “the NLU module 220 may identify a domain, an intent, and one or more slots from the user input [second entity] in a personalized and context-aware manner.
training a plurality of generative artificial intelligence (GAI) models to generate, for a same content, different content summaries, each GAI model of the plurality of GAI models generating content summaries personalized for a different known entity intent: (Peng [0068]); See “The third evaluation strategy may comprise generating different summaries over the same content object using different summarization templates.”
routing the content into a selected GAI model of the plurality of GAI models, the selected GAI model trained specifically to generate summaries for users with the determined current intent, the GAI model generating, for the second entity, a summary of the content based on the determined intent and the content (Peng [0050][0068][0074]); See at least [0050] “In particular embodiments, the dialog engine 235 may identify the dialog intent, state, and history associated with the user. Based on the dialog intent, the dialog engine 235 may select some candidate entities among the recommended candidate entities to send to the client system 130.”
changing a user interface (I) element displayed to the second entity to include the summary generated by the selected GAI model (Peng [0073]). See “FIGS. 4A-4R illustrate an example summarization generated by the assistant system 140. FIG. 4A illustrates an example digest generated by the assistant system 140. As displayed in FIG. 4A, the digest may comprise a section of news stories 410. Within the section of news stories 410, the assistant system 140 may list one or more summaries associated with one or more news stories.”
Claims 2 and 12
Peng discloses:
wherein a prompt for generating the summary includes the content and the determined intent (Peng [0050]). See at least “In particular embodiments, the dialog engine 235 may identify the dialog intent, state, and history associated with the user. Based on the dialog intent, the dialog engine 235 may select some candidate entities among the recommended candidate entities to send to the client system 130.”
Claims 3 and 13
Peng discloses:
wherein the generating of the summary further comprises passing the determined intent into the GAI model as a contextual input to the GAI model (Peng [0054]). See at least “As an example and not by way of limitation, the intent classifier may be based on a machine-learning model that may take the domain classification/selection result as input and calculate a probability of the input being associated with a particular predefined intent. At step 224b, the NLU module may process the domain classification/selection result using a meta-intent classifier. The meta-intent classifier may determine categories that describe the user's intent. As an example and not by way of limitation, the meta-intent classifier may be based on a machine-learning model that may take the domain classification/selection result as input and calculate a probability of the input being associated with a particular predefined meta-intent.”
Claims 4 and 14
Peng discloses:
wherein the GAI model uniquely corresponds to the determined intent and has been trained specifically to generate summaries for viewers with the determined intent (Peng [0049]). See at least “The generation may be alternatively based on a machine-learning model that is trained based on user profile, entity attributes, and relevance between users and entities.” See also [0050] “the dialog engine 235 may identify the dialog intent, state, and history associated with the user. Based on the dialog intent, the dialog engine 235 may select some candidate entities among the recommended candidate entities to send to the client system 130.”
Claims 5 and 15
Peng discloses:
wherein the determining a user intent includes using a machine learning model trained to classify user intent for a user based on information about the user (Peng [0054]). See at least “The intent classifier may determine the user's intent associated with the user request. As an example and not by way of limitation, the intent classifier may be based on a machine-learning model that may take the domain classification/selection result as input and calculate a probability of the input being associated with a particular predefined intent.”
Claims 6 and 16
Peng discloses:
wherein the machine learning model takes as input one or more features corresponding to a user (Peng [0049][0067]). See at least [0049] “The generation may be alternatively based on a machine-learning model that is trained based on user profile, entity attributes, and relevance between users and entities.” See also [0067] “the summarization templates may be suitable for generating summaries of content objects associated with different attributes (e.g., interest, friends, location, etc.).”
Claims 7 and 17
Peng discloses:
wherein the summary is in a different format than the content (Peng [0069][0074]). See at least [0074] “At step 550, the assistant system 140 may send, to the client system 130 in response to the user request, instructions for presenting the summaries of the plurality of content objects to the first user, wherein the summaries are presented via one or more of the determined modalities (see [0028] the modalities may include audio, text, image, video, etc.).
Claims 8 and 18
Peng discloses:
wherein the one or more features include an embedding generated by a GAI model based on a profile for the user (Peng [0083]). See also [0084] “In particular embodiments, an object may be mapped to a vector based on one or more properties, attributes, or features of the object, relationships of the object with other objects, or any other suitable information associated with the object.”
Claims 9 and 19
Peng discloses:
wherein the one or more features include user activity information (Peng 0055]). See at least “the NLU module 220 may improve the domain classification/selection of the content objects by extracting semantic information from the semantic information aggregator 230. In particular embodiments, the semantic information aggregator 230 may aggregate semantic information in the following way…. As an example and not by way of limitation, the data may include news feed posts/comments, interactions with news feed posts/comments, Instagram posts/comments, search history, etc. that are collected from a prior 90-day window. The processing result may be stored in the user context engine 225 as part of the user profile.”
Claim 10
Peng discloses:
wherein the operations further comprise: passing the summary to a downstream recommender machine learning model to cause determination of a recommendation to the second entity based in part on the summary (Peng [0050]). Where the reference teaches “the proactive task may comprise recommending the candidate entities to a user.”
Response to Arguments
Applicant's arguments with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: Because the claims do not fall within any of the enumerated groupings of abstract ideas, it is reasonable to find that the claims do not recite an abstract idea. Applicant respectfully notes that, even if theoretically possible, it is not practical for a person to mentally apply one of a plurality of internal mental models ( each specifically trained to generate summaries for a specific current user intent) to accessed content nor to then dynamically generate summaries for that content according to a current intent of a given user.
Examiner respectfully disagrees. Furthermore, the Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer,” and that “courts have found requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind” (see p. 8 of the October 2019 Update: Subject Matter Eligibility). The Examiner also notes that “both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes (see p. 8 of the October 2019 Update: Subject Matter Eligibility).
The Specification fails to clearly evidence how the use of a machine learning model being trained with content to issue personalize summaries is an actual technological improvement over, or differs from, the expected general concept of applying the ML model. It is unclear how the ML models are being integrated in any specialized manner that serves any specialized technical purpose/solution.
The claims are not rooted in machine learning technology, and the claims do not solve a technical problem that only arises in AI or machine learning. MPEP § 2106.05(a). The amended limitations being referred to simply apply data analysis to train the generic ML models and do nothing more than use computational instructions to be implemented in a computer processing environment, simply to "apply it" without any improvement to the computer functionality or technology itself.
Applicant Argues: Here, the claims at issue do not merely add insignificant extra-solution activity to the Examiner's alleged abstract concept. Respectfully, training a plurality of GAI models to separately and uniquely generate summaries for a given piece of content according to a current intention state of a user is not insignificant extra-solution activity.
Respectfully, Examiner’s analysis focused on the additional elements being merely “apply it” and with the amended claim language, merely generally linking the use of the judicial exception to a particular technological environment or field of use.
As explained in the rejection, the only feature here that may be considered to be an “additional element” is the requirement for the broadly claimed training a plurality of generative artificial intelligence (GAI) models” to be done using an “online system” (e.g., a general purpose computer). Such a requirement amounts to a general requirement to perform the training a plurality of generative artificial intelligence (GAI) models” function (e.g., identifying a simple correlation in data using some basic mathematical approach) to be performed using a general purpose computer. Additional elements are parsed out of the claim language and considered during step 2A prong two and step 2B of the analysis. Parsing out the additional elements during step 2A prong one is proper, and does not mean the Examiner ignored any limitations. “training... to generate, for a same content, different content summaries,” is part of the identified abstract idea, which is shown to be a mental process activity.
As for “changing the user interface element,” Examiner has determined that to be a field of us and because the claim is actually directed to changing information on a display to updated results, an improvement has not been found.
Applicant Argues: Here, the Examiner did not perform the necessary evaluation. Applicant submits that the claims recite an element or combination of elements that that are other than what is well understood,
routine, conventional activity in the field.
Examiner respectfully disagrees. Referring to the Recentive Analytics v. Fox Corp decision, the U.S. Court of Appeals for the Federal Circuit affirmed the district court' s dismissal of a patent infringement lawsuit brought by Recentive Analytics against Fox Corporation, where it was determined that the machine learning models employed were conventional. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Similarly, confining the trained machine learning model to a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. It is important to note that most machine learning models are inherently trained on large, often complex datasets to generate predictions or classifications [summaries]. It is not apparent that such a non-generic improvement is reflective in the instant claims as the claims do not provide any detail that addresses any improvement to the broadly claimed training step. As such the rejection is maintained.
Applicant's arguments with respect to the rejection under 35 USC 103 have been fully considered but they are not persuasive.
Applicant Argues: Peng does not describe, in any manner whatsoever, that the summaries for various content are dynamically built according to each user's current intention when viewing that respective content.
Examiner respectfully disagrees. See Peng [0040] “The semantic information aggregator 230 may provide ontology data associated with a plurality of predefined domains, intents, and slots to the NLU module 220….and [0052] “the assistant system 140 may comprise a summarizer 290. The summarizer 290 may provide customized news feed summaries to a user... Based on the information from the proactive inference layer 280, the entity resolution module 240, and the user context engine 225, the summarizer 290 may generate personalized and context-aware summaries for the user.”
Applicant Argues: Peng, there is no indication or even vague suggestion that the news stories 410 ( or any other modality for that matter) will have a different summary construction for the same content when the viewing user is in a job seeking phase vs. a recruiting phase ( or according to any other viewer intent).
Examiner respectfully disagrees. See Peng [0070] “Although this disclosure describes generating particular
summaries based on particular machine-learning models in a particular manner, this disclosure contemplates generating any suitable summaries based on any suitable machine learning models in any suitable manner.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626