Abstract: Computer-implemented methods and systems interface with a language model (e.g., a Large Language Model (LLM)) to assist in document revision. The methods and systems allow text to be selected within a document and an action definition to be selected from an action definition library. The text and/or the action definition may be selected using a graphical user interface (GUI). An action defined by the selected action definition is applied to the selected text to generate text. For example, the selected action definition may include a prompt, and the prompt may be combined with the selected text to generate a combined prompt. The combined prompt may be provided as an input to the LLM, which may generate the generated text. The generated text may be integrated into the document.

Abstract: A system and method for transforming text within documents using, such as by using large language models (LLMs). Users can select source text from a source document, in response to which a painting configuration is identified or generated based on the source text, such as by providing the source text and a source prompt to a large language model to produce source output, and selecting or generating the painting configuration based on the source output. The user can select destination text, in response to which the painting configuration is applied to the destination text, such as by selecting or generating a destination action definition based on the painting configuration and the destination text, and providing the destination action definition to a large language model to produce destination output. The destination text may be replaced with the destination output, or output derived therefrom.

Abstract: A computer-implemented method and system for recursive generative content generation processes merge templates containing multiple elements within document creation workflows. The method includes receiving a merge template comprising a plurality of elements, and for each element determining whether it is an action definition. If so, the method applies the action definition to generate output by providing a prompt to a language model, and inserts the generated output into a merged document. If not an action definition, the method determines whether the element is a merge field, obtains its value and inserts it into the merged document. Otherwise, the method copies the element into the merged document. The system enables sophisticated document personalization by combining traditional mail merge functionality with AI-driven content generation capabilities.

Abstract: A computer-implemented system and method transform text within documents using a generative process that enables automated application of action definitions across multiple document elements while maintaining user control. For each element in a document, the system identifies an action definition and applies it to generate output by providing prompts to a large language model. The system manifests generated outputs to users for review and, upon approval, revises corresponding elements based on approved outputs. This enables efficient processing of multiple document elements while preserving precise user control over content updates. The system supports various levels of user involvement, from fully automated processing to interactive refinement, allowing users to review outputs before document updates while maintaining coherence and quality.

Abstract: A computer-implemented method: (A) receives product data identifying a product; (B)(B) receives patent data identifying a patent claim; (C) receives prior art data identifying a set of prior art, (D) executes an optimization process to minimize an objective function, the objective function including: a first sub-objective function and a second sub-objective function, the executing the optimization process comprising: (D)(1) generating a plurality of interpretations of the patent claim, (D)(2) for each particular interpretation in the plurality of interpretations of the patent claim: (D)(2)(a) using a first language model to evaluate the first sub-objective function with the product data and the particular interpretation as inputs to the first sub-objective function, (D)(2)(b) using a second language model to evaluate the second sub-objective function with the prior art data and the particular interpretation as inputs to the second sub-objective function.

Abstract: A system and method for transforming text within documents using, such as by using large language models (LLMs). Users can select source text from a source document, in response to which a painting configuration is identified or generated based on the source text, such as by providing the source text and a source prompt to a large language model to produce source output, and selecting or generating the painting configuration based on the source output. The user can select destination text, in response to which the painting configuration is applied to the destination text, such as by selecting or generating a destination action definition based on the painting configuration and the destination text, and providing the destination action definition to a large language model to produce destination output. The destination text may be replaced with the destination output, or output derived therefrom.

Abstract: Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

Abstract: Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

Abstract: Computer-implemented methods and systems interface with a language model (e.g., a Large Language Model (LLM)) to assist in document revision. The methods and systems allow text to be selected within a document and an action definition to be selected from an action definition library. The text and/or the action definition may be selected using a graphical user interface (GUI). An action defined by the selected action definition is applied to the selected text to generate text. For example, the selected action definition may include a prompt, and the prompt may be combined with the selected text to generate a combined prompt. The combined prompt may be provided as an input to the LLM, which may generate the generated text. The generated text may be integrated into the document.

Abstract: A computer-implemented method: (A) receives product data identifying a product; (B) (B) receives patent data identifying a patent claim; (C) receives prior art data identifying a set of prior art, (D) executes an optimization process to minimize an objective function, the objective function including: a first sub-objective function and a second sub-objective function, the executing the optimization process comprising: (D)(1) generating a plurality of interpretations of the patent claim, (D)(2) for each particular interpretation in the plurality of interpretations of the patent claim: (D)(2)(a) using a first language model to evaluate the first sub-objective function with the product data and the particular interpretation as inputs to the first sub-objective function, (D)(2)(b) using a second language model to evaluate the second sub-objective function with the prior art data and the particular interpretation as inputs to the second sub-objective function.

Abstract: Computer-implemented methods and systems interface with a language model (e.g., a Large Language Model (LLM)) to assist in document revision. The methods and systems allow text to be selected within a document and an action definition to be selected from an action definition library. The text and/or the action definition may be selected using a graphical user interface (GUI). An action defined by the selected action definition is applied to the selected text to generate text. For example, the selected action definition may include a prompt, and the prompt may be combined with the selected text to generate a combined prompt. The combined prompt may be provided as an input to the LLM, which may generate the generated text. The generated text may be integrated into the document.

Abstract: A system and method for transforming text within documents using, such as by using large language models (LLMs). Users can select source text from a source document, in response to which a painting configuration is identified or generated based on the source text, such as by providing the source text and a source prompt to a large language model to produce source output, and selecting or generating the painting configuration based on the source output. The user can select destination text, in response to which the painting configuration is applied to the destination text, such as by selecting or generating a destination action definition based on the painting configuration and the destination text, and providing the destination action definition to a large language model to produce destination output. The destination text may be replaced with the destination output, or output derived therefrom.

Abstract: Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

Abstract: A computer system includes a calendar containing appointments. The system also includes one or more logic modules. Each logic module specifies a condition and a corresponding action. The profile may be applied to context data, such as data representing the current time, to perform the actions specified by the logic modules in response to detecting that the context data satisfies the conditions specified by the logic modules. In particular, the actions specified by the logic modules may be performed in response to detecting that the current time falls within the time period of an appointment on the calendar.

Abstract: A computer-implemented method: (A) receives product data identifying a product; (B) (B) receives patent data identifying a patent claim; (C) receives prior art data identifying a set of prior art, (D) executes an optimization process to minimize an objective function, the objective function including: a first sub-objective function and a second sub-objective function, the executing the optimization process comprising: (D)(1) generating a plurality of interpretations of the patent claim, (D)(2) for each particular interpretation in the plurality of interpretations of the patent claim: (D)(2)(a) using a first language model to evaluate the first sub-objective function with the product data and the particular interpretation as inputs to the first sub-objective function, (D)(2)(b) using a second language model to evaluate the second sub-objective function with the prior art data and the particular interpretation as inputs to the second sub-objective function.

Abstract: Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

Abstract: Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

Abstract: A computer system includes a calendar containing appointments. The system also includes one or more logic modules. Each logic module specifies a condition and a corresponding action. The profile may be applied to context data, such as data representing the current time, to perform the actions specified by the logic modules in response to detecting that the context data satisfies the conditions specified by the logic modules. In particular, the actions specified by the logic modules may be performed in response to detecting that the current time falls within the time period of an appointment on the calendar.

Abstract: A computer system includes a calendar containing appointments. The system also includes one or more logic modules. Each logic module specifies a condition and a corresponding action. The profile may be applied to context data, such as data representing the current time, to perform the actions specified by the logic modules in response to detecting that the context data satisfies the conditions specified by the logic modules. In particular, the actions specified by the logic modules may be performed in response to detecting that the current time falls within the time period of an appointment on the calendar.

Abstract: A computer system includes a calendar containing appointments. The system also includes one or more logic modules. Each logic module specifies a condition and a corresponding action. The profile may be applied to context data, such as data representing the current time, to perform the actions specified by the logic modules in response to detecting that the context data satisfies the conditions specified by the logic modules. In particular, the actions specified by the logic modules may be performed in response to detecting that the current time falls within the time period of an appointment on the calendar.