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. Examiner’s Note The Examiner encourages Applicant to schedule an interview to discuss issues related to, for example, the rejections noted below under 35 U.S.C § 112, 10 1 and § 10 3 , for moving toward allowance. Providing supporting paragraph(s) for each limitation of amended/new claim(s) in Remarks is strongly requested for clear and definite claim interpretations by Examiner. Priority Acknowledgment is made of applicant's claim for the present application filed on 09/05/2023 . 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 appl icant regards as his invention. Claim(s) 8 is/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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim(s) 8 recite(s) the limitation “ the configuration of one or more affected network devices in the enterprise network ” ( last line ) . There is insufficient antecedent basis for this limitation in the claim. I t is not clear what it is referring to. It appears it may need to read “ a configuration of one or more affected network devices in the enterprise network ”, or something else. For the purposes of examination, “ a configuration of one or more affected network devices in the enterprise network ” is used. Claim(s) 8 each recite(s) limitations that raise issues of indefiniteness as set forth above, and their dependent claims are rejected at least based on their direct and/or indirect dependency from the claim listed above . Appropriate explanation and/or amendment is required. 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. Regarding claim 1 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ … comprising: … ; determining context for generating a distilled multimedia data set based on at least one of user input and user persona; generating, based on the context, the distilled multimedia data set comprising a set of multimedia slices generated from the multimedia data … , wherein the multi-modal knowledge graph is generated … and indicates relationships among a plurality of slices of the multimedia data; and providing the distilled multimedia data set for performing one or more actions associated with the enterprise network ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The claim recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim recites an additional element(s) (“ A computer-implemented ” , “ using a multi-modal knowledge graph ” , “ using a graph neural network ” ) – using a device and /or a model to process data. The device and the model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. In particular, the claim recites an additional element(s) (“ obtaining multimedia data from one or more data sources related to operation or configuration of an enterprise network ” ) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving . Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. MPEP 2106.05(f). As discussed above, the claim recites the additional element (s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible. Regarding claim 2 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ … , and wherein generating the distilled multimedia data set includes: generating a reduced graph from the multi-modal knowledge graph based on the network persona and the user input; and selecting at least two multimedia slices for the distilled multimedia data set using the reduced graph. ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element ( “ wherein the user persona is a network persona ”) . This is a recitation of a particular type or source of model/ data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 3 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ … , and further comprising: determining the network persona based on past activities performed by the user with respect to the enterprise network. ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element ( “ wherein the network persona includes a skill level of a user ”) . This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 4 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ … , and further comprising: encoding the user input to generate an input embedding; and generating the reduced graph from the multi-modal knowledge graph based on the input embedding ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element ( “ wherein the user input includes an actionable task related to the configuration of the enterprise network ”) . This is a recitation of a particular type or source of model/ data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 5 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ … ; encoding the user input to generate a plurality of input embeddings, … ; and generating the reduced graph from the multi-modal knowledge graph based on the plurality of input embeddings to provide interactive learning using the distilled multimedia data set ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element(s) (“ obtaining the user input comprising at least two search queries ” ) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving . Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. In particular, the claim recites an additional element ( “ each of the plurality of input embeddings being specific to one of the at least two search queries ”) . This is a recitation of a particular type or source of model/ data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim recites the additional element (s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 6 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ converting an audio portion of the multimedia data to text; determining semantic relationships in the text, … ; and generating the multi-modal knowledge graph based on the semantic relationships in which the multimedia data is segmented into the plurality of slices represented by respective nodes in the multi-modal knowledge graph, … ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element ( “ wherein the semantic relationships include entities and events in the text ” , “ wherein at least one of the plurality of slices includes a portion of the text, at least one respective semantic relationship, a corresponding audio portion of the multimedia data, and a corresponding video portion of the multimedia data ” ) . This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 7 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The limitations of “ segmenting a video of the multimedia data into a plurality of video slices to map the entities and the events in the text to the video of the multimedia data ” , as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper). I f a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “ Mental Processes ” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements. Thus, the claim is directed to an abstract idea. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claim is not patent eligible. Regarding claim 8 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The claim recites the abstract idea identified above regarding claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element ( “ wherein the multimedia data comprises one or more of: a plurality of network related video learning seminars for configuring one or more network devices in the enterprise network; a plurality of network related video tutorials for obtaining operating data of the one or more network devices in the enterprise network; a plurality of network related videos for progressing a network technology along an adoption lifecycle; and a plurality of troubleshooting videos that address one or more network issues by performing the one or more actions associated with the enterprise network that change the configuration of one or more affected network devices in the enterprise network ”) . This is a recitation of a particular type or source of model/ data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h) Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h). Regarding claim 9 The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claim recites a method; therefore, it falls into the statutory category of processes. Step 2A Prong 1 : The claim recites the abstract idea identified above regarding claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element(s) (“ wherein the multimedia data is obtained from different data sources that provide video recordings associated with the operation or the configuration of the enterprise network ” ) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving . Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim recites the additional element (s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible. Regarding claim 10 The claim recites “ An apparatus comprising: a memory; a network interface configured to enable network communications; and a processor, wherein the processor is configured to perform a method comprising :” to perform precisely the method of Claim 1. As performance of an abstract idea on generic computer components (see MPEP 2106.05(f)) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, the claim is rejected for reasons set forth in the rejection of Claim 1. Regarding claim 11 The claim is rejected for the reasons set forth in the rejection of Claim 2 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 12 The claim is rejected for the reasons set forth in the rejection of Claim 3 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 13 The claim is rejected for the reasons set forth in the rejection of Claim 4 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 14 The claim is rejected for the reasons set forth in the rejection of Claim 5 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 15 The claim is rejected for the reasons set forth in the rejection of Claim 6 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 16 The claim is rejected for the reasons set forth in the rejection of Claim 7 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 1 7 The claim recites “ One or more non-transitory computer readable storage media encoded with software comprising computer executable instructions that, when executed by a processor, cause the processor to perform a method including :” to perform precisely the method of Claim 1. As performance of an abstract idea on generic computer components (see MPEP 2106.05(f)) and “Storing and retrieving information in memory” (see MPEP 2106.05(g) on Insignificant Extra-Solution Activity, and MPEP 2106.05(d) on Well-Understood, Routine, Conventional Activity) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, the claim is rejected for reasons set forth in the rejection of Claim 1. Regarding claim 18 The claim is rejected for the reasons set forth in the rejection of Claim 2 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 1 9 The claim is rejected for the reasons set forth in the rejection of Claim 3 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Regarding claim 20 The claim is rejected for the reasons set forth in the rejection of Claim 4 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception. Claim Rejections - 35 USC § 103 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 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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 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(s) 1-5, 9-14, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. ( VQA-GNN: Reasoning with Multimodal Semantic Graph for Visual Question Answering ) in view of Chang et al. ( KID: Knowledge Graph-Enabled Intent-Driven Network with Digital Twin ) Regarding claim 1 ( Note : Hereinafter, if a limitation has bold brackets (i.e. [·] ) around claim languages, the bracketed claim languages indicate that they have not been taught yet by the current prior art reference but they will be taught by another prior art reference afterwards.) Wang teaches A [ computer ] -implemented method comprising: ( Wang [ fig (s) 2 ] “ Overview of VQA-GNN: we first perform the image-level KG retrieval (§4.1) and the concept-level KG retrieval (§4.1) to build a multimodal semantic graph (§4.2), then we perform Multi-relation GNN (§4.3) to joint reason correct answer of the scene (§4.4). Here, "C-SG" and "I-SG" indicate Concept-level and Image-level semantic graph, both are subgraph of the multimodal semantic graph. ” ; ) obtaining multimedia data from one or more data sources related to operation or configuration of an enterprise [ network ] ; ( Wang [ sec(s) 4 ] “ Given an image and its related question with an answer choice, first we obtain explicit concept-level and image-level KGs from the visual and textual context, respectively (§4.1). Then we introduce a QA node to connect the concept-level and image level KGs to build a multimodal semantic graph so that we can reason about the correct answer over the joint knowledge resources (§4.2).” [ sec(s) 3 ] “ In contrast, we extract a scene graph from the image in order to provide explicit relation information in the image, and build a GNN model with the scene graph to achieve an interpretable reasoning. It does not need extra visual-language dataset for pretraining, and outperforms existing models without pretraining process (Table 1). ” [ sec(s) 2 ] “ VQA -GNN extracts a scene graph from the given input image using a off-the-shelf scene graph generator to obtain a high-level structured representation of the visual context, and then retrieves relevant linguistic/visual subgraphs from massive knowledge graphs including ConceptNet (Speer et al., 2017) and VisualGenome (Krishna et al., 2017).” [ sec(s) 5.1 ] “We evaluate VQA-GNN on the Visual Common sense Reasoning dataset (VCR). VCR consists of two tasks: visual question answering (Q→A), answer justification (QA→R).” ; ) determining context for generating a distilled multimedia data set based on at least one of user input and user persona; ( Wang [ sec(s) 1 ] “ Given the inferred scene graph and the retrieved knowledge subgraphs, we combine them into a multimodal semantic graph by introducing super nodes that link relevant concepts from the graphs. The multimodal semantic graph consists of QA -context aware concepts (from the scene graph) as well as the QA-context-agnostic concepts (from the knowledge graph). Finally, VQA-GNN performs message passing and reasons on this structured multimodal semantic graph with graph neural networks (GNNs) to score each answer candidate. Our VQA-GNN is the first unified model that fuses the strengths of Trans-VL, scene graphs, and knowledge graphs via a joint semantic graph and GNN.” [ sec(s) 4.1 ] “Step 2-1: We use grounded phases to retrieve their 1 -hop neighbor nodes from the ConceptNet KG. Step 2-2: As many retrieved concept nodes that are semantically irrelevant to the answer choice, in spired by QA-GNN (Yasunaga et al., 2021), we introduce relevance score to prune irrelevance nodes. We use a word2vec model released by the spaCy library1 to get relevance score between concept node candidates and answer choices. As a result, given an answer choice, we can retrieve a relevance subgraph from ConceptNet KG based on the rele v ance score. To better comprehend concept knowledge from the image as well, Step 3: In addition to linking adjacent object entities in the ConceptNet KGdomain, we also combine them with retrieved subgraph by matching and linking relevance concept entities , e.g., (bottle, atlocation, beverage), (book, relatedto, thing). ” ; ) generating, based on the context, the distilled multimedia data set comprising a set of multimedia slices generated from the multimedia data using a multi-modal knowledge graph, wherein the multi-modal knowledge graph is generated using a graph neural network and indicates relationships among a plurality of slices of the multimedia data; and ( Wang [ fig(s) 1 ] [ sec(s) Abs ] “Specifically, given a question image pair, we build a scene graph from the image, retrieve a relevant linguistic subgraph from ConceptNet and visual subgraph from VisualGenome, and unify these three graphs and the question into one joint graph, multimodal semantic graph .” [ sec(s) 1 ] “ Given the inferred scene graph and the retrieved knowledge subgraphs, we combine them into a multimodal semantic graph by introducing super nodes that link relevant concepts from the graphs. The multimodal semantic graph consists of QA -context aware concepts (from the scene graph) as well as the QA-context-agnostic concepts (from the knowledge graph). Finally, VQA-GNN performs message passing and reasons on this structured multimodal semantic graph with graph neural networks (GNNs) to score each answer candidate.” [ sec(s) 4.1 ] “ In addition to the local image context, with an intuition that the global image context of the correct choice is assumed to be similar to the local image context, we use a pretrained sentence-BERT model to calculate the similarity between each answer choice with all region descriptions of region image in the Visual Genome dataset so that we can get relevance region images to represent the global image context of each choice (Reimers and Gurevych, 2019). … Step 3: In addition to linking adjacent object entities in the ConceptNet KGdomain, we also combine them with retrieved subgraph by matching and linking relevance concept entities , e.g., (bottle, atlocation, beverage), (book, relatedto, thing). ” [ sec(s) 4.4 ] “For the training process , we apply the cross entropy loss to optimize the VQA-GNN model end-to-end.” [ sec(s) 5.1 ] “We joint train VQA GNN on Q→A and QA→R, with a common LM encoder, the multimodal semantic graph for Q→A, concept-level semantic graph for QA→R.” ; ) providing the distilled multimedia data set for performing one or more actions associated with the enterprise [ network ] . ( Wang [ fig(s) 2 ] “ Overview of VQA-GNN: we first perform the image-level KG retrieval (§4.1) and the concept-level KG retrieval (§4.1) to build a multimodal semantic graph (§4.2), then we perform Multi-relation GNN (§4.3) to joint reason correct answer of the scene (§4.4). Here, "C-SG" and "I-SG" indicate Concept-level and Image-level semantic graph, both are subgraph of the multimodal semantic graph. ” [ sec(s) 4.1 ] “ In addition to the local image context, with an intuition that the global image context of the correct choice is assumed to be similar to the local image context, we use a pretrained sentence-BERT model to calculate the similarity between each answer choice with all region descriptions of region image in the Visual Genome dataset so that we can get relevance region images to represent the global image context of each choice (Reimers and Gurevych, 2019). … Step 3: In addition to linking adjacent object entities in the ConceptNet KGdomain, we also combine them with retrieved subgraph by matching and linking relevance concept entities , e.g., (bottle, atlocation, beverage), (book, relatedto, thing). Hence, we can obtain a concept-level semantic graph G a cp = (V a cp ,E a cp ) to represent concept-level knowledge. ” ; ) However, Wang does not appear to explicitly teach: A [ computer ] -implemented method comprising: obtaining multimedia data from one or more data sources related to operation or configuration of an enterprise [ network ] ; providing the distilled multimedia data set for performing one or more actions associated with the enterprise [ network ] . ( Note : Hereinafter, if a limitation has one or more bold underlines, the one or more underlined claim languages indicate that they are taught by the current prior art reference, while the one or more non-underlined claim languages indicate that they have been taught already by one or more previous art references.) Chang teaches A computer -implemented method comprising: ( Chang [ fig(s) 4 ] “ The process of forming the IKG ” [ sec(s) III.A ] “ Considering the characteristics of the KG technology itself, we design a generic intent refinement model on the basis of the KG. Specifically, its working mechanism is as follows: At first, the system pre-processes the intent data after receiving it. The program reads the data of intent input and loads it into memory . Following that, the regular matching rules are invoked to eliminate special symbols. Finally, the cleaned and filtered intent data is stored in the file. ” [ sec(s) IV.B ] “We create two virtual machines based on Ubuntu 16.04 system . One is used to run the KG-based intent translation system. And the other is used to build the network simulation environment and construct the NSKG.” ; ) obtaining multimedia data from one or more data sources related to operation or configuration of an enterprise network ; ( Chang [ fig(s) 1 ] [ sec(s) II.D ] “The physical network layer is a real-world network environment. It is composed primarily of the physical entities that comprise the end-to- end network, such as terminals, switches, routers, and other network element devices ” [ sec(s) IV ] “ We input the intent as "Transmit an important video service from Xian user A to Beijing user B, the time requirement is from 10:30 on June 5, 2020, to 12:30 on June 5, 2020 ". … When the system reads the intent entity "Beijing user B" and queries the node "Beijing user B" in the NSKG, it updates the attribute values of the node, such as IP address 10.0.0.4 and port number 1257, to the node "Beijing user B" in the IKG. Furthermore, similar steps are repeated until corresponding network parameters is added for all intent entities and relations. ” [ sec(s) III.A ] “ To improve the accuracy in representing intents, the initial IKG must be extended. As a result of intent expansion, the corresponding conditions and attributes are added to the intent entities. For instance, if the user types "establish a reassurance level voice service from A to B", the phrase "reassurance-level voice service" will be parsed. The necessary requirements for "reassurance-level voice service", such as delay, bandwidth, and other parameters, are added . It’s worth noting that the intent expansion module will expand the IKG in accordance with the information from the digital twin layer. ” [ sec(s) III.B ] “The diagram of the interaction between the IKG and the NSKG is shown in Fig. 3. The system gradually implements intent expansion, parameter mapping, and intent verification through interaction between both KGs. It is in these processes that network policies are gradually formed. After implementing the parameter mapping, the system parses the IKG to understand the requirements of user intents . These intent requirements are expressed in terms of parameter information such as bandwidth, delay, and packet loss rate .” ; ) providing the distilled multimedia data set for performing one or more actions associated with the enterprise network . ( Chang [ fig(s) 1 ] [ sec(s) II.D ] “The physical network layer is a real-world network environment. It is composed primarily of the physical entities that comprise the end-to- end network, such as terminals, switches, routers, and other network element devices ” [ sec(s) IV ] “ We input the intent as "Transmit an important video service from Xian user A to Beijing user B, the time requirement is from 10:30 on June 5, 2020, to 12:30 on June 5, 2020 ". … When the system reads the intent entity "Beijing user B" and queries the node "Beijing user B" in the NSKG, it updates the attribute values of the node, such as IP address 10.0.0.4 and port number 1257, to the node "Beijing user B" in the IKG. Furthermore, similar steps are repeated until corresponding network parameters is added for all intent entities and relations. ” [ sec(s) III.A ] “ To improve the accuracy in representing intents, the initial IKG must be extended. As a result of intent expansion, the corresponding conditions and attributes are added to the intent entities. For instance, if the user types "establish a reassurance level voice service from A to B", the phrase "reassurance-level voice service" will be parsed. The necessary requirements for "reassurance-level voice service", such as delay, bandwidth, and other parameters, are added . It’s worth noting that the intent expansion module will expand the IKG in accordance with the information from the digital twin layer. ” [ sec(s) III.B ] “The diagram of the interaction between the IKG and the NSKG is shown in Fig. 3. The system gradually implements intent expansion, parameter mapping, and intent verification through interaction between both KGs. It is in these processes that network policies are gradually formed. After implementing the parameter mapping, the system parses the IKG to understand the requirements of user intents . These intent requirements are expressed in terms of parameter information such as bandwidth, delay, and packet loss rate .” ; ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Wang with the enterprise network of Chang . One of ordinary skill in the art would have been motived to combine in order to merge the semantic gap between user intents, policy generation, and the underlying network toward feasibility and effectiveness , and contribute to the ability of the intent-driven network to adapt to the network autonomously . ( Chang [ sec(s) I ] “• We present an IDN framework named the KID, aiming to merge the semantic gap between user intents, policy generation, and the underlying network. • We develop an intent language specification and a generic intent refinement model. Additionally, an intelligent translation technique is designed to achieve intelligent intent translation which takes user intents into consideration as well as network status. • Finally, we provide a use case of the presented KID framework to demonstrate the feasibility and effectiveness of the proposed scheme.” [ sec(s) II.B ] “ It contributes to the ability of the IDN to adapt to the network autonomously ”) Regarding claim 2 The combination of Wang, Chang teaches claim 1 . Wang further teaches wherein the user persona is a [ network ] persona, and wherein generating the distilled multimedia data set includes: ( Wang [ sec(s) 1 ] “ Given the inferred scene graph and the retrieved knowledge subgraphs, we combine them into a multimodal semantic graph by introducing super nodes that link relevant concepts from the graphs. The multimodal semantic graph consists of QA -context aware concepts (from the scene graph) as well as the QA-context-agnostic concepts (from the knowledge graph). Finally, VQA-GNN performs message passing and reasons on this structured multimodal semantic graph with graph neural networks (GNNs) to score each answer candidate.” [ sec(s) 4.1 ] “ In addition to the local image context, with an intuition that the global image context of the correct choice is assumed to be similar to the local image context, we use a pretrained sentence-BERT model to calculate the similarity between each answer choice with all region descriptions of region image in the Visual Genome dataset so that we can get relevance region images to represent the global image context of each choice (Reimers and Gurevych, 2019). … Step 3: In addition to linking adjacent object entities in the ConceptNet KGdomain, we also combine them with retrieved subgraph by matching and linking relevance concept entities , e.g., (bottle, atlocation, beverage), (book, relatedto, thing). Hence, we can obtain a concept-level semantic graph G a cp = (V a cp ,E a cp ) to represent concept-level knowledge. ” ; ) generating a reduced graph from the multi-modal knowledge graph based on the [ network ] persona and the user input; and ( Wang [ fig(s) 2 ] “ Overview of VQA-GNN: we first perform the image-level KG retrieval (§4.1) and the concept-level KG retrieval (§4.1) to build a multimodal semantic graph (§4.2), then we perform Multi-relation GNN (§4.3) to joint reason correct answer of the scene (§4.4). Here, "C-SG" and "I-SG" indicate Concept-level and Image-level semantic graph, both are subgraph of the multimodal semantic graph ” [ sec(s) 1 ] “ Given the inferred scene graph and the retrieved knowledge subgraphs, we combine them into a multimodal semantic graph by introducing super nodes that link relevant concepts from the graphs. The multimodal semantic graph consists of QA -context aware concepts (from the scene graph) as well as the QA-context-agnostic concepts (from the knowledge graph).” [ sec(s) 4.1 ] “Step 2-1: We use grounded phases to retrieve their 1 -hop neighbor nodes from the ConceptNet KG. Step 2-2: As many retrieved concept nodes that are semantically irrelevant to the answer choice, in spired by QA-GNN (Yasunaga et al., 2021), we introduce relevance score to prune irrelevance nodes. We use a word2vec model released by the spaCy library1 to get relevance score between concept node candidates and answer choices. As a result, given an answer choice, we can retrieve a relevance subgraph from ConceptNet KG based on the rele v ance score. To better comprehend concept knowledge from the image as well, Step 3: In addition to linking adjacent object entities in the ConceptNet KGdomain, we also combine them with retrieved subgraph by matching and linking relevance concept entities , e.g., (bottle, atlocation, beverage), (book, relatedto, thing). Hence, we ca