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 § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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 1-30 are rejected under 35 U.S.C. 102(a)(1)/(a)/(2) as being anticipated by Galitsky (US 2020/0286463), hereinafter Galitsky.
Regarding claim 1, Galitsky discloses a computer-implemented method, executed on a computing device, comprising: generating a plurality of synthetic Al-based users, wherein each of these synthetic Al-based users has a plurality of interests within a content platform (para [0071], "More specifically, certain aspects enable autonomous agents ("chatbots") that deliver content in the form of virtual persuasive dialogue. A virtual dialogue is defined as a multi-turn adversarial argumentation dialogue between agents.", para [0500], "An autonomous agent implemented by dialogue application 102 crates and presents a virtual persuasive dialogue session. As a result, the session not only provides a user with content on his topic of interest but imitates his conversations with proponents and a dispute with opponents. The user's opinion may evolve over time with subsequent interactions with an autonomous agent.", para [0509], "In a more specific example, dialogue application 102 forms a list of candidate topics. Then dialogue application 102 clusters this list and selects the members of the candidate list which are as close to the centers of cluster as possible. Dialogue application 102 can add a small number of expressions as topics of a given search results to show along the other search results to a list. Dialogue application 102 can present each of the topics to a user device."); enabling the plurality of synthetic Al-based users to generate content within the content platform based, at least in part, upon the plurality of interests (para [0350], "Dialogue chatbot systems need to be capable of understanding and matching user communicative intentions, reason with these Intentions, build their own respective communication intentions and populate these intentions with actual language to be communicated to the user. para [0606], "In some implementations, server 5312 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 5302, 5304, 5306, and 5308. As an example, data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. "); Identifying a piece of content for addressing with the plurality of synthetic Al-based users, thus defining target content (para [0588], Dialogue application 102 identifies, from the electronic documents, a question and an answer that are relevant to the selected topic. The answer is in rhetorical agreement with the question and can be verified, for example, by a classifier trained to detect rhetoric agreement of a question CDT and an answer CDT. Together, the question and the answer form a virtual conversation that can be depicted as between one or more agents or users."); and generating a response to the target content (para [0512], "For example, in return, the user inputs, at utterance 4505, "I think Marxism does not necessarily associated with the political correctness." In response, based on the presented opinion, dialogue application 102 forms a virtual dialogue from available documents and pages, simulating a conversation between virtual proponents and opponents and virtual bots.").
Regarding claim 2, Galitsky discloses the computer-implemented method of claim 1 wherein the plurality of synthetic Al-based users includes: a plurality of content platform bot accounts (para [0279], "The authors formed the training sets from their own accounts and also public Facebook accounts available via API over a number of years (at the time of writing Facebook API for getting messages is unavailable). In addition, we used 860 email threads from Enron dataset. Also, we collected the data of manual responses to postings of an agent", para [0606], "In some implementations, server 5312 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 5302, 5304, 5306, and 5308. As an example, data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.").
Regarding claim 3, Galitsky discloses the computer-implemented method of claim 1 wherein enabling the plurality of synthetic Al-based users to generate content within a content platform based, at least in part, upon the plurality of interests includes: enabling the plurality of synthetic Al-based users to generate one or more original pieces of content for posting within the content platform (para [0169], "Dialogue application 102 can create, analyze, and compare communicative discourse trees. Communicative discourse trees are designed to combine rhetoric information with speech act structures. CDTs include with arcs labeled with expressions for communicative actions. By combining communicative actions, CDTs enable the modeling of RST relations and communicative actions.", para [0254], "A question sentence can be an explicit question, a request, or a comment. Dialogue application 102 creates question communicative discourse tree from input text. Using the example discussed in relation to FIGS. 13 and 15, an example question sentence is "are rebels responsible for the downing of the flight." Dialogue application 102 can use process 1500 described with respect to FIG. 15. The example question has a root node of "elaborate.", para [0500]. "An autonomous agent implemented by dialogue application 102 crates and presents a virtual persuasive dialogue session. As a result, the session not only provides a user with content on his topic of interest but imitates his conversations with proponents and a dispute with opponents. The user's opinion may evolve over time with subsequent interactions with an autonomous agent. Process 4400 can use machine learning. For example, dialogue application 102 can train classifier 120 to perform one or more functions described in process 4400 and use classifier 120 instead of algorithmic techniques. For example purposes, process 4400 is discussed with respect to FIG. 45."). Regarding claim 4, Galitsky discloses the computer-implemented method of claim 1 wherein enabling the plurality of synthetic Al-based users to generate content within a content platform based, at least in part, upon the plurality of interests includes: enabling the plurality of synthetic Al-based users to recycle one or more pieces of content for posting within the content platform (para [0134], "The second type has a response that is, e.g., a good answer, to the question. The answer could take the form of, for example, in some aspects, the Al constructing an answer from its extensive knowledge base(s) or from matching the best existing answer from searching the internet or intranet or other publicly/privately available data sources.").
Regarding claim 5, Galitsky discloses the computer-implemented method of claim 1 wherein identifying a piece of content for addressing with the plurality of synthetic Al-based users, thus defining target content includes: vectorizing the target content, thus defining vectorized target content (para [0232], "To handle meaning of words expressing the subjects of CAs, a word can be applied to a vector model such as the "word2vector" model. More specifically, to compute generalization between the subjects of communicative actions, the following rule can be used: if subject1=subject2, subject1 (circumflex over ()}subject2=. Here subject remains and score is 1."); and comparing the vectorized target content to a pool of vectorized known undesirable information and/or a pool of vectorized known desirable information to classify the target content (para [0330], "The outcome is that after the model is trained, the word vectors are mapped into a vector space such that Distributed Representations of Sentences and Documents semantically similar words have similar vector representations. This kind of model can potentially operate on discourse relations, but it is hard to supply as rich linguistic information as we do for tree kernel learning. There is a corpus of research that extends word2vec models to go beyond word level to achieve phrase-level or sentence-level representations. For instance, a simple approach Is using a weighted average of all the words in the document, (weighted averaging of word vectors), losing the word order similar to how bag-of-words approaches do. A more sophisticated approach is combining the word vectors in an order given by a parse tree of a sentence, using matrix-vector operations.").
Regarding claim 6, Galitsky discloses the computer-implemented method of claim 1 wherein generating a response to the target content includes: generating a response that contradicts the target content (para [0347], "Argumentation detail relation is important because many cases in scientific publications, where some background information (for example the definition of a term) is important for understanding the overall argumentation. A support relation between an argument component Resp and another argument component Req indicates that Resp supports (reasons, proves) Req. Similarly, an attack relation between Resp and Req is annotated if Resp attacks (restricts, contradicts) Req. The detail relation is used, if Resp is a detail of Req and gives more information or defines something stated in Req without argumentative reasoning. Finally, we link two argument components (within Req or Resp) with the sequence relation, if the components belong together and only make sense in combination, i.e., they form a multi-sentence argument component.").
Regarding claim 7, Galitsky discloses the computer-implemented method of claim 1 wherein generating a response to the target content includes: generating a response that reinforces the target content (para [0094], "In an aspect, dialogue application 102 can use classifier 120, which can be trained with training data 125. Classifier 120 can be trained to identify rhetorical similarity between text, to determine whether argumentation is present in text, and/or to determine one or more chains of argumentation in text (e.g., two sentences that support each other in advancing an argument). Classifier 120 can be a predictive model, a classification model, or other model type that is trained to detect a presence or absence of features in text. An example of a model is a support vector machine. Examples of learning approaches include nearest neighbor models and tree kernel models.").
Regarding claim 8, Galitsky discloses the computer-implemented method of claim 1 wherein generating a response to the target content includes: generating a response to the target content using a generative Al model (para [0133], "The underlying rational for having an AI chatbot respond like a human is that the human brain can formulate and understand the request and then give a good response to the human request much better than a machine. Thus, there should be significant Improvement in the request/response of a chatbot, if human B is mimicked. So an initial part of the problem is how does the human brain formulate and understand the request? To mimic, a model is used. RST and DT allow a formal and repeatable way of doing this.", para [0167], "By using an iterative process, dialogue application 102 provides a training pair to classifier 120 and receives, from the model, a level of complementarity. Dialogue application 102 calculates a loss function by determining a difference between the determined level of complementarity and an expected level of complementarity for the particular training pair.", para [0501], Dialogue area 4500 depicts utterances 4501-4510. In particular, utterances 4507 and 4510 are examples of virtual persuasive dialogues generated by dialogue application 102.").
Regarding claim 9, Galitsky discloses the computer-implemented method of claim 8 wherein generating a response to the target content using a generative AI model includes: generating a response to the target content using the generative Al model and a contradiction instruction script when the response to the target content contradicts the target content (para [0295], "Agreement by sentiment shows the contribution of proper sentiment match in RR pair. The sentiment rule includes, in particular, that if the polarity of RR is the same, response should confirm what request is saying. Conversely, if polarity is opposite, response should attack what request is claiming. Agreement by logical argumentation requires proper communication discourse where a response disagrees with the claim in request.", para [0347], "Argumentation detail relation is important because many cases in scientific publications, where some background information (for example the definition of a term) is important for understanding the overall argumentation. A support relation between an argument component Resp and another argument component Req indicates that Resp supports (reasons, proves) Req. Similarly, an attack relation between Resp and Req is annotated if Resp attacks (restricts, contradicts) Req. The detail relation is used, if Resp is a detail of Req and gives more information or defines something stated in Req without argumentative reasoning. Finally, we link two argument components (within Req or Resp) with the sequence relation, if the components belong together and only make sense in combination, i.e., they form a multi-sentence argument component.", para [0516], "Argumentation makes an utterance persuasive. But an utterance is even more persuasive if a sequence of utterances includes utterances that are linked by an explanation chain. Therefore, in an aspect, the dialogue application 102 can detect an explanation chain in a result. Detecting an argumentation chain can be performed in conjunction with or instead of detecting argumentation.").
Regarding claim 10, Galitsky discloses the computer-implemented method of claim 8 wherein generating a response to the target content using a generative AI model includes: generating a response to the target content using the generative AI model and a reinforcement instruction script when the response to the target content reinforces the target content (para [0347], "Argumentation detail relation is important because many cases in scientific publications, where some background information (for example the definition of a term) is important for understanding the overall argumentation. A support relation between an argument component Resp and another argument component Req indicates that Resp supports (reasons, proves) Req. Similarly, an attack relation between Resp and Req is annotated if Resp attacks (restricts, contradicts) Req. The detail relation is used, If Resp is a detail of Req and gives more information or defines something stated in Req without argumentative reasoning. Finally, we link two argument components (within Req or Resp) with the sequence relation, if the components belong together and only make sense in combination, i.e., they form a multi-sentence argument component.", para [0500], An autonomous agent implemented by dialogue application 102 crates and presents a virtual persuasive dialogue session. As a result, the session not only provides a user with content on his topic of interest but imitates his conversations with proponents and a dispute with opponents. The user's opinion may evolve over time with subsequent interactions with an autonomous agent. Process 4400 can use machine learning. For example, dialogue application 102 can train classifier 120 to perform one or more functions described in process 4400 and use classifier 120 Instead of algorithmic techniques. For example purposes, process 4400 is discussed with respect to FIG. 45.").
Claims 11-20 correspond to claims 1-10, and are analyzed accordingly.
Claims 21-30 correspond to claims 1-10, and are analyzed accordingly.
Conclusion
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/MARK D FEATHERSTONE/Supervisory Patent Examiner, Art Unit 2111
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