Abstract: A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.

Abstract: Content generation leverages an unsupervised, generative pre-trained language model (e.g., a generative-AI). In this approach, a model derived by applying to given content relevant competitive content and one or more optimization targets is received. Based on optimization criteria encoded as embedding signals in the model, a determination is made regarding whether a template suitable for use as an input to the generative-AI exists in a set of templates. If so, the model embedding signals are merged into the template, or the template itself is transformed using the embedding signals, in either case creating a modified template. If, however, no template suitable as the input exists, the model and other information are input to a natural language processor to generate a generative-AI input. Either the modified template or the generative-AI input, as the case may be, is then applied through the generative-AI to generate an output competitively-optimized with respect to the optimization targets.

Abstract: A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.

Abstract: Content generation leverages an unsupervised, generative pre-trained language model (e.g., a generative-AI). In this approach, a model derived by applying to given content relevant competitive content and one or more optimization targets is received. Based on optimization criteria encoded as embedding signals in the model, a determination is made regarding whether a template suitable for use as an input to the generative-AI exists in a set of templates. If so, the model embedding signals are merged into the template, or the template itself is transformed using the embedding signals, in either case creating a modified template. If, however, no template suitable as the input exists, the model and other information are input to a natural language processor to generate a generative-AI input. Either the modified template or the generative-AI input, as the case may be, is then applied through the generative-AI to generate an output competitively-optimized with respect to the optimization targets.

Abstract: Content generation leverages an unsupervised, generative pre-trained language model (e.g., a generative-AI). In this approach, a model derived by applying to given content relevant competitive content and one or more optimization targets is received. Based on optimization criteria encoded as embedding signals in the model, a determination is made regarding whether a template suitable for use as an input to the generative-AI exists in a set of templates. If so, the model embedding signals are merged into the template, or the template itself is transformed using the embedding signals, in either case creating a modified template. If, however, no template suitable as the input exists, the model and other information are input to a natural language processor to generate a generative-AI input. Either the modified template or the generative-AI input, as the case may be, is then applied through the generative-AI to generate an output competitively-optimized with respect to the optimization targets.

Abstract: A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.

Abstract: A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.