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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 30, 2026 has been entered.
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 1-4, 6, 8-11, 13, 15-17, 19, and 21-26 are rejected under 35 U.S.C. 101 because they are directed to a judicial exception without significantly more.
At step 1, the independent claims are directed to a method (claim 1), a machine (claim 8), and a non-transitory machine-readable medium (claim 15), which are statutory categories of invention.
At step 2A prong II, claim 1 (representative) recites an abstract idea. Claim 1 recites: detecting a commercial intent during a conversation between participants in a group chat; and in response to detecting the commercial intent, performing operations comprising: extracting keyword candidates from the conversation based on at least one of a frequency of mention across a plurality of messages in the conversation, a context in which a keyword is mentioned, and relationships between the participants as determined from respective profiles of the participants; assigning a relevance score to each keyword candidate using a meaning of the conversation by performing operations comprising: determining the relevance score based on a similarity between the vector representation of the conversation and the vector representation of the keyword candidate; assigning a commercial score to each keyword candidate to detect commercially related keywords; selecting keywords using a weighted combination of the relevance scores and commercial scores by performing operations comprising: applying a first weight to the relevance score and a second weight to the commercial score for each keyword candidate to produce a combined score; and selecting keyword candidates having highest combined scores as the keywords; detecting abusing language in the keywords by analyzing the keywords; generating, by a moderation component, a moderation response comprising a report of abusive language detected in the keywords; filtering the abusive language from the keywords using the moderation response before transmitting the keywords as filtered to one or more content servers; receiving content from the one or more content servers, the one or more content selected by the one or more content servers using the keywords as filtered; and providing, by the chatbot, content to the participants of the group chat during the conversation.
These limitations recite Certain Methods of Organizing Human Activity, as they recite a method of collecting data, analyzing the data, and using that analysis to target an advertisement (content) to a user.
At step 2A prong II, the claims recite the additional elements of a chatbot of a chatbot system, generating a vector representation of the conversation and a vector representation of each keyword candidate that encode semantic meaning of the conversation and the keyword candidate; using a machine learning model trained on a dataset of keywords labeled as commercially related or not commercially related; using a machine learning model trained on datasets of keywords labeled as abusive or non-abusive and contextual analysis that considers a context around the keywords to determine whether the keywords are used in an abusive manner, processors, memory, and non-transitory machine-readable medium.
A chatbot of a chatbot system, processors, memory, and non-transitory machine-readable medium are all generic computing components recited at a high level of generality. Generating a vector representation is mere instructions to apply the exception (see MPEP 2106.05(f)(1), where the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished). Neither the claims nor the specification discuss how a vector representation is generated other than to discuss off-the-shelf computing components capable of carrying out this process (see paragraphs 73, 82, 86, 100). Using a machine learning model amounts to merely using the words “apply it” or merely using a computer as a tool to perform the abstract idea. The specification lays out a variety of known machine learning models which may be used (see paragraph 130) but does not specify a particular model, how it is trained, or how it is improved in the current invention. Therefore, the generic computing components, mere instructions to apply an exception, and recitation of technology at the apply it level, when considered individually and as a whole, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
At step 2B, the additional elements are again considered. The chatbot of a chatbot system, processors, memory, and non-transitory machine-readable medium are all generic computing components recited at a high level of generality, such that they do not amount to significantly more than the abstract idea. Generating a vector representation is mere instructions to apply the exception, and using a machine learning model amounts to merely using the words “apply it” or merely using a computer as a tool to perform the abstract idea, neither of which amounts to significantly more than the abstract idea. When considered as a whole, the claim shows that the additional elements invoke computers as a tool to implement the abstract idea, rather than showing any innovation of the additional elements themselves that might amount to significantly more than the abstract idea. As a result, the claims do not amount to significantly more than the abstract idea, and the independent claims are ineligible.
The dependent claims further limit the abstract idea, and do not introduce any new additional elements. Therefore, the dependent claims are ineligible for the same reasons listed above.
Potentially Allowable Subject Matter
The claims would be allowable over the prior art, and would be in condition for allowance if and when the 101 rejection is able to be overcome. The prior art fails to teach the specific combination of elements recited.
Hoang (US 2022/0172021) teaches generating embeddings (vectors) of what users say when interacting with a chatbot and assigning scores to the embeddings, but fails to teach the claimed weighting, filtering, and selection of keywords for advertising purposes.
Chahal (US 10,331,713) teaches scoring and weighting keywords, and creating vectors of keywords. However, Chahal does this to generate word clouds that can be sold to advertisers as user data, not for use in a chatbot environment where the keywords are directly used to select advertising content for a user.
Itzhak (US 9,946,788) teaches weighting keyword vectors and scoring the vectors for the purpose of targeted advertising, but does not do so in the same way as the claimed invention, and does not include any filtering as claimed.
Yao (US 9,026,542) teaches generating vectors from text, scoring and weighting the keywords, and switching between a first and second language. However, Yao does not teach first and second weights and first and second scores as claimed.
Iida (US 2018/0089164) teaches using trained models to determine if inappropriate language is being used in a generative AI system, but fails to teach generating a moderation response and updating responses based on only the filtered language.
Hyeonho (KR 20240079134) teaches filtering inappropriate language when using a chatbot, but for the purposes of preventing the chatbot from using inappropriate language, and not for controlling content being delivered.
Saravanan (NPL) teaches that keyword vector analysis using ML models can be used to filter abusive language in social media settings, but not for the purposes of targeted advertising.
Song (NPL) teaches detecting and mitigating for use of abusive language in chatbots, but for purposes of benefitting individuals and the overall training of the chatbots themselves.
While the prior art generally teaches the claimed concepts, it does not teach the details of the relevance score and commercial score, first and second weights, generating a moderation response based on filtering out abusive language, in a single reference or in a plurality of references that are able to be combined without impermissible hindsight. Therefore, the prior art fails to teach, either singly or in combination, the claimed invention.
Response to Arguments
Applicant's arguments filed April 30, 2026 have been fully considered but they are not persuasive. Applicant argues that the claims should not be rejected under 101 as they integrate the abstract idea into a practical application. Examiner respectfully disagrees.
Applicant contends that a specific technical solution to a technical problem is laid out in the claims. As addressed above, the generation of vectors is mere instructions to apply the exception, and while they are carried out by computers, the process of doing so is not described in the specification other than in a conclusory nature. This is not reflective of a technical solution. Applicant further points to using machine learning to analyze keywords, which is also applying the exception as explained above. The moderation response and filtering of abusive language are part of the abstract idea, as they are steps that an individual might take when assessing a user to determine what type of information to target to the user. While the applicant discusses operating on a specific data object within a specific technical solution, this is merely an allegation of a technical solution to a technical problem without any specific support for this position. Selecting keywords and applying weights is further part of the abstract idea.
Applicant generally alleges that the claims include a technical solution to a technical problem without providing any explanation of how the claim limitations achieve this goal or where in the specification there might be support for this position. Based on this lack of support, examiner finds these arguments unpersuasive.
Applicant’s arguments regarding the 103 rejection are moot in light of the indication of potentially allowable subject matter above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ILANA L SPAR whose telephone number is (571)270-7537. The examiner can normally be reached 8-4 M-F.
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/ILANA L SPAR/ Supervisory Patent Examiner, Art Unit 3622
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