DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation: “… providing, by the artificial intelligence system, the input data as an input to a machine learning model trained to output attributes of items and importance data indicating a relative importance of the attributes … receiving, by the artificial intelligence system, a set of attributes and importance data for the set of attributes as an output of the machine learning model; selecting, by the artificial intelligence system and based on the set of attributes and the importance data for the set of attributes, one or more digital components … providing, by the artificial intelligence system, the digital component to the client device for display to the user (emphasis added to accentuate insufficient antecedent basis)”. In the third step of the “for each additional query” loop, the claim introduces “a set of attributes” without clear antecedent basis. The previous step (the output of the machine learning model) introduced “attributes of items” and “importance data.” It did not introduce “a set of attributes.” The attributes require a link to the previously introduced attributes. The claim further introduces “one or more digital components” (plural) and later refers to it as “the digital component” (singular).
For the purposes of examination, the limitation is interpreted as the following:
“… providing, by the artificial intelligence system, the input data as an input to a machine learning model trained to output attributes of items and importance data indicating a relative importance of the attributes … receiving, by the artificial intelligence system, the attributes and the importance data for the attributes as an output of the machine learning model; selecting, by the artificial intelligence system and based on the attributes and the importance data for the attributes, one or more digital components … providing, by the artificial intelligence system, the one or more digital components to the client device for display to the user.”
Claims 11 and 20 recite analogous limitations to claim 1 and are interpreted similarly.
Claims 4, 6-7, 10, 14, 16-17 include the antecedent issues of the independent claims and are interpreted similarly.
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.
Claims 1-3, 5, 8-9, 11-13, 15, 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stillwell et al., hereinafter referred to as Stillwell (US 2022/0217107 A1).
As per claim 1, Stillwell discloses a method (Stillwell: Abstract.), comprising:
receiving, by an artificial intelligence system and from a client device of a user, a first query of a user session (Stillwell: Para. [0018] discloses “a chat driven artificial intelligence system … receive a request … including a current interaction [claimed first query] entered by a user … associated with previous user interactions with the chat portion … in a current session [claimed user session]”. );
receiving, by the artificial intelligence system, one or more additional queries during the user session (Stillwell: Para. [0018] discloses “session includes one or more previous frames associated with previous user interactions [claimed additional queries] with the chat portion … in a current session [claimed user session]”. );
for each additional query:
generating, by the artificial intelligence system, input data based on (i) the additional query and (ii) data related to one or more previous queries received during the user session (Stillwell: Para. [0018] discloses “creates a working frame [claimed input data] including one or more intents and one or more concepts expressed in the previous user interactions [claimed data related to one or more previous queries] and included in the one or more previous frames [claimed received during the user session]”. );
providing, by the artificial intelligence system, the input data as an input to a machine learning model trained to output attributes of items and importance data indicating a relative importance of the attributes based on received inputs (Stillwell: Para. [0102] discloses “conversation management platforms function using machine learning trained [claimed machine learning model] … statement can be classified as having a particular intent and having values for a set of entities [claimed output attributes of items] … intents and entities may then be returned [claimed importance data indicating a relative importance] … by the conversation platform”. );
receiving, by the artificial intelligence system, a set of attributes and importance data for the set of attributes as an output of the machine learning model (Stillwell: Para. [0102] discloses “intents and entities [claimed set of attributes] may then be returned [claimed as an output of the machine learning model] … along with … weights [claimed importance data] …” and Para. [0019] discloses “receive a response … including a determined intent and a determined concept [claimed set of attributes]”. );
selecting, by the artificial intelligence system and based on the set of attributes and the importance data for the set of attributes, one or more digital components (Stillwell: Para. [0016] discloses “context of the web page … used to select content [claimed selecting … one or more digital components] from an advertiser … [based on] the concepts or destinations expressed by the user [claimed based on the set of attributes and the importance data]”. ); and
providing, by the artificial intelligence system, the digital component to the client device for display to the user (Stillwell: Para. [0020] discloses “return the response … to the chat system widget at the user device, to be rendered at the users device [claimed providing … for display to the user] … by presenting … the content [claimed digital component] in the display portion of the single interface”. ).
As per claim 2, Stillwell discloses the method of claim 1, wherein the user session comprises a conversation with an artificial intelligence agent and each query comprises a prompt for the artificial intelligence agent (Stillwell: Para. [0012] discloses an “artificial intelligence system” [claimed artificial intelligence agent] utilizing a “chat driven interface” to interact in a “human understandable and comfortable manner” [claimed conversation], Para. [0018] discloses that the system “retrieves a session associated with the user” [claimed user session], and Para. [0018] further discloses “a request … including a current interaction” [claimed query comprising a prompt] entered by the user.).
As per claim 3, Stillwell discloses the method of claim 1, wherein the input data for at least one additional query comprises data from one or more previous sessions of the user (Stillwell: Para. [0021] discloses “the session includes one or more historical sessions [claimed previous sessions of the user] conducted between the chat system and the user,” and Para. [0019] discloses “Based on the canonical intent … the chat system can determine a data service to call and send a request [claimed input data for at least one additional query] … including the canonical concept determined for the working frame” derived from those sessions.).
As per claim 5, Stillwell discloses the method of claim 1, wherein selecting the one or more digital components comprises:
identifying a set of candidate digital components (Stillwell: Para. [0079] discloses “data services [systems for identifying candidates] may thus take the concepts as inputs and use the concept data to rank a set of travel related items”);
identifying, from among the set of candidate digital components, a subset of the candidate digital components having distribution parameters that match each required attribute (Stillwell: Para. [0079] discloses “data services [systems for identifying candidates] may thus take the concepts [distribution parameters] as inputs and use the concept data to rank a set of travel related items [candidate digital components] and return the content for the top ranked set of travel related items”); and
selecting the one or more digital components from the subset of the candidate digital components (Stillwell: Para. [0079] discloses “data services [systems for identifying candidates] may thus take the concepts [distribution parameters] as inputs and use the concept data to rank a set of travel related items [candidate digital components] and return the content for the top ranked set of travel related items [subset … having attributes matching the input]” and Stillwell: Paras. [0118]-[0119] disclose data services taking the concepts as inputs and using the concept data of the chat system to determine related data [identifying a subset].).
As per claim 8, Stillwell discloses the method of claim 1, wherein the input data comprises, for each of the one or more previous queries, timing data indicating when the previous query was received during the user session (Stillwell: Para. [0018] discloses retrieving a session “which includes one or more previous frames associated with previous user interactions [claimed input data comprising previous queries]” and Stillwell: Para. [0070] discloses “each frame … corresponds to the data associated with responding to a particular request,” while Stillwell: Para. [0261] illustrates an exemplary request comprising a “time” field [claimed timing data indicating when the previous query was received during the user session]; therefore, retrieving the session with previous frames includes the timing data of those previous queries.).
As per claim 9, Stillwell discloses the method of claim 1, wherein the input data comprises contextual information extracted from the one or more previous queries (Stillwell: Para. [0018] discloses “retrieves a session associated with the user, wherein the session includes one or more previous frames associated with previous user interactions [claimed one or more previous queries] … and creates a working frame [claimed input data] including one or more intents and one or more concepts [claimed contextual information] expressed in the previous user interactions”).
As per claims 11 & 20, the claim(s) recites analogous limitations to claim(s) 1 above, and is/are therefore rejected on the same premise.
As per claims 12-13, 15, 18-19, the claim(s) recites analogous limitations to claim(s) 2-3, 5, 8-9 above, and is/are therefore rejected on the same premise.
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, 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.
Claim 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Stillwell in view of Yang et al., hereinafter referred to as Yang (US 2021/0097425 A1).
As per claim 4, Stillwell discloses the method of claim 1 (Stillwell: Abstract.),
However, Stillwell does not explicitly disclose “… wherein the importance data for each set of attributes indicates, for each individual attribute, whether the individual attribute is a required attribute or a user preference.”
Further, Yang is in the same field of endeavor and teaches wherein the importance data for each set of attributes indicates, for each individual attribute, whether the individual attribute is a required attribute or a user preference (Yang: Para. [0015] discloses “The output includes scores representing predictions, estimates, and/or inferences by the machine learning models of propensities, preferences, behavior, categories, and/or other attributes of users, companies, jobs, items, and/or other entities. The output also, or instead, includes a list of the most important features”. Therefore determines whether the individual attribute is a required attribute or a user preference.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Stillwell and Yang before him or her, to modify the AI chat system of Stillwell to include the attribute evaluation feature as described in Yang. The motivation for doing so would have been to improve understanding of the output by users by providing a configuration that aids in narrative insights.
As per claim 14, the claim(s) recites analogous limitations to claim(s) 4 above, and is/are therefore rejected on the same premise.
Claims 6-7, 10, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Stillwell in view of Crabtree et al., hereinafter referred to as Crabtree (US 2025/0156898 A1).
As per claim 6, Stillwell discloses the method of claim 5, (Stillwell: Paras. [0139], [0144] disclose these rules [claimed distribution parameter] may first be applied to determine the set of advertisers [claimed set of candidate digital components] that are allowed to be displayed on that publisher's web site (because either a publisher desires that the content of a particular advertiser not be displayed in association with their web site [claimed indicates that the candidate digital component is not eligible for presentation for component requests that include at least one required attribute in the importance data for the set of attributes] or an advertiser desires that their content not be displayed on a particular publisher's site).).
However, Stillwell does not explicitly disclose “… comprising filtering, from the set of candidate digital components, each candidate digital component having a distribution parameter that indicates that the candidate digital component is not eligible for presentation …”.
Further, Crabtree is in the same field of endeavor and teaches comprising filtering, from the set of candidate digital components, each candidate digital component having a distribution parameter that indicates that the candidate digital component is not eligible for presentation (Crabtree: Para. [0119] discloses “when context computing system 424 identifies a user is on vacation [claimed required attribute in the importance data for the set of attributes], process filtering system 2732 may automatically suppresses [claimed filtering] certain content categories like life insurance or work-related advertising [claimed candidate digital component having a distribution parameter that indicates that the candidate digital component is not eligible for presentation]” and Crabtree: Para. [0377] discloses “implement various Gen AI content filtering [claimed filtering] processes … presence of select material based on user settings and context [claimed required attribute in the importance data] … doesn't trip any “red line” settings [claimed distribution parameter that indicates that the candidate digital component is not eligible for presentation]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Stillwell and Crabtree before him or her, to modify the AI chat system of Stillwell to include the filtering set of candidate digital components presentation feature as described in Crabtree. The motivation for doing so would have been to improve ad relevance and user experience by providing targeted content suppression and customized styling.
As per claim 7, Stillwell-Crabtree disclose the method of claim 1, wherein selecting the one or more digital components comprises:
providing the set of attributes, the importance data for the set of attributes, and distribution parameters to an additional machine learning model trained to output scores for digital components based on input data provided to the additional machine learning model (Crabtree: Para. [0309] discloses “softmax function normalizes the values so that they sum up to 1, representing a probability distribution [claimed distribution parameters] over the vocabulary” provided to machine learning model layers to output scores for digital components.); and
selecting the one or more digital components from a set of candidate digital components based on scores for the candidate digital components output by the additional machine learning model (Stillwell: Para. [0144]-[0145] discloses “The allowable advertisers [claimed set of candidate digital components] can then be scored on relevance by comparing the entities [claimed set of attributes] associated with the publisher's web page 412 with the entities associated with the advertisers or the advertisers' content [claimed digital components] … top ranked advertiser can then be selected [claimed selecting the one or more digital components from a set of candidate digital components based on scores] … these ad selection rules may be implemented as a machine learning algorithm [claimed additional machine learning model]” and Stillwell: Para. [0214] discloses “weighted sum of all concept scores and their corresponding concept weights [claimed importance data for the set of attributes]”, and Crabtree: Para. [0293] which discloses “softmax function normalizes the values so that they sum up to 1, representing a probability distribution [claimed distribution parameters] over the vocabulary” provided to machine learning model layers to output scores for digital components.).
As per claim 10, Stillwell discloses the method of claim 1, further comprising:
providing the set of attributes and an additional set of attributes for the digital component an(Stillwell: Para. [0078] discloses extracting concepts “entities [claimed set of attributes]” and Stillwell: Para. [0139] discloses “entities (e.g., concepts or destinations) associated with an advertiser [claimed additional set of attributes for the digital component]”);
receiving, from the additional machine learning model, data indicating one or more attributes for the digital component that satisfy at least one of the set of attributes (Crabtree: Paras. [0116], [0123], [0128], [0480], [0482] disclose identifying potential advertising content [claimed digital component] that aligns with the query topic [claimed set of attributes] and the user's profile [claimed additional set of attributes] provided to “machine learning models or neural networks [claimed additional machine learning model]” to determine matching ad criteria “to determine the most effective ad [claimed trained to determine whether attributes for a digital component satisfy an input set of attributes]” and receiving selected ad types and positions [claimed receiving, from the additional machine learning model, data indicating one or more attributes for the digital component that satisfy at least one of the set of attributes]”); and
adjusting a visual characteristic of text for each of the one or more attributes in the digital component provided to the client device for display to the user (Crabtree: Paras. [0116], [0483] disclosing “leverage machine learning models or neural networks to adjust the language, dialect, and other visual/auditory attributes … applying appropriate styling and layout … use a standard font … or a slightly different font [claimed adjusting a visual characteristic of text for each of the one or more attributes in the digital component provided to the client device for display to the user]”).
As per claims 16-17, the claim(s) recites analogous limitations to claim(s) 6-7 above, and is/are therefore rejected on the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be viewed in the list of references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEET DHILLON whose telephone number is (571)270-5647. The examiner can normally be reached M-F: 5am-1:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath V. Perungavoor can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEET DHILLON/Primary Examiner
Art Unit: 2488
Date: 09-27-2026
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