Abstract: A system may receive deposition planning data. The deposition planning data may indicate one or more of a deponent identifier and a plurality of deposition objectives. The system may obtain a transcript generation prompt and input the deposition planning data and the transcript generation prompt into a deposition simulation machine learning model to generate a synthetic deposition transcript. The system may define selection metrics based on the synthetic deposition transcript and determine an amount of allocatable space within a data storage cache for storing a selection of workspace objects. The data storage cache may connect to a client device via a local data transfer connection. The system may select relevant objects based on the amount of allocatable space and the selection metrics. The system may store the selected relevant objects in the allocatable space of the data storage cache.

Abstract: A system may receive deposition data relating to one or more completed deposition events. The deposition data includes one or more of transcript data and image data. The system may obtain a set of updatable fact objects based on the deposition data and a fact update prompt. The fact update prompt controls how a fact updating machine learning model (i) identifies target objects from the set of updatable fact objects and (ii) extracts or summarizes the deposition data to generate updates to the target objects. The system inputs, into the fact updating machine learning model, the fact update prompt, at least a portion of the deposition data, and at least a portion of the set of updatable fact objects to generate the updates. The system updates the target objects as stored in a data store in accordance with the generated updates.

Abstract: A system may receive deposition data relating to an active deposition event. The deposition data may include one or more of transcript data and image data. The system may obtain a deposition analysis prompt and a deposition plan. The deposition plan includes one or more of objectives for the active deposition event or questions to be asked during the active deposition event, and the deposition analysis prompt is configured to control how a deposition analysis machine learning model analyzes the deposition data with respect to the deposition plan to generate a response to the deposition data. The system may input at least a portion of the deposition data, the deposition plan, and the deposition analysis prompt into the deposition analysis machine learning model to generate the response to the deposition data and present the response on a graphical user interface.

Abstract: A system may receive deposition data relating to an active deposition event. The deposition data includes one or more of transcript data and image data. The system may generate one or more search queries to identify objects relevant to the deposition data from among a set of workspace objects and obtain a deposition analysis prompt. The deposition analysis prompt is configured to control how a deposition analysis machine learning model analyzes the deposition data and the relevant objects to generate a response to the deposition data. The system may input at least a portion of the deposition data, the relevant objects, and the deposition analysis prompt into the deposition analysis machine learning model to generate the response to the deposition data and present the response on a graphical user interface.

Abstract: A system may obtain background documents and a case context extraction prompt and generate case context data by analyzing the background documents via a case context machine learning model. The case context prompt is input into the case context machine learning model with the background documents to cause the case context machine learning model to output the case context data and controls how the case context machine learning model analyzes the background documents to identify key concepts therein. The case context data includes the identified key concepts. The system may generate search queries based on the key concepts, query, via a document search engine, a corpus of documents using the search queries to produce sets of ranked documents for the search queries, compile a seed set of documents from the sets of ranked documents, and provide the seed set of documents to a document review application executing within a workspace.

Abstract: A computer system may obtain a fact extraction prompt. The fact extraction prompt is configured to control how a fact generating machine learning model extracts or summarizes content of reference documents included in a corpus of documents. The computer system may input, into the fact generating machine learning model, the fact extraction prompt and one or more reference documents from the corpus of documents to identify one or more facts included in the one or more reference documents, populate respective data fields of one or more fact objects based upon the one or more facts identified by the fact generating machine learning model and generate one or more fact summaries for the matter based on the one or more fact objects. The one or more fact summaries include structured presentations of at least some of the one or more fact objects according to contents of the data fields.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and/or (v) train classifiers. In some embodiments, one or more processors: generate a prompt for input to the generative AI model; generate classifications for a set of documents from the corpus of documents by inputting the set of documents and the prompt to the generative AI model; based on the classifications, provide the set of documents to a review platform for manual review by a reviewer; obtain review data associated with a subset of documents from the set of documents; and train, by executing a training algorithm, a classifier using the review data as ground truth data, wherein the training algorithm is configured to analyze extracted relevant document portions of the subset of documents to train the classifier.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and/or (v) evaluate the classification performance of updated prompts.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts (and/or criteria for prompts) to classify documents; (iii) explain document classifications; and/or (iv) explain updates to prompts (and/or prompt criteria). In some embodiments, one or more processors: obtain an initial set of documents from a corpus of documents; classify documents within the initial set of documents by inputting a prompt and the documents within the initial set of documents into a generative artificial intelligence (AI) model; and evaluate classification performance of the prompt to identify (i) that the initial set of documents does not include enough documents associated with a first issue of the one or more issues, or (ii) that the corpus of documents is associated with a new issue.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts (and/or criteria for prompts) to classify documents; (iii) explain document classifications; and/or (iv) explain updates to prompts (and/or prompt criteria). In some embodiments, one or more processors: obtain at least one prompt criteria defining context for classifying a corpus of documents using a generative AI model; generate a first prompt based upon the at least one prompt criteria; input the first prompt and a first document of the corpus of documents into the generative AI model to generate a classification of the first document; and generate an explanation of why the generative AI model generated the classification based on an output of the generative AI model.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts (and/or criteria for prompts) to classify documents; (iii) explain document classifications; and/or (iv) explain updates to prompts (and/or prompt criteria). In some embodiments, one or more processors: obtain at least one prompt criteria defining context for classifying a corpus of documents using the generative AI model; generate a first prompt based upon the at least one prompt criteria; input the first prompt and a first document into the generative AI model to obtain a classification of the first document; obtain review data associated with the first document; update the at least one prompt criteria based on the classification of the first document and the review data; generate a second prompt based upon the updated at least one prompt criteria; and classify a second document by inputting the second prompt into the generative AI model.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts (and/or criteria for prompts) to classify documents; (iii) explain document classifications; and/or (iv) explain updates to prompts (and/or prompt criteria). In some embodiments, one or more processors: obtain an initial set of documents associated with an inquiry; generate initial prompt criteria by inputting the initial set of documents to a first generative AI model, wherein the initial prompt criteria defines at least (i) a relevancy requirement for the inquiry and (ii) a description of an issue; generate a prompt for input to the generative AI model based on the prompt criteria; and classify a sample of documents from a corpus of documents by inputting the sample of documents and the prompt to a second generative AI model.

Abstract: The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and/or (v) evaluate the classification performance of updated prompts.

Abstract: Systems, methods, and computer readable media for generating synthetic training data to train a machine learning model are provided. The techniques may relate to ensuring that confidential customer data is not used to train a partially-trained model that is provided to a second customer. Accordingly, the techniques may include presenting a user interface coupled to a large language model (LLM) to detect a request to generate example synthetic data having one or more characteristics. The techniques may further include presenting the example synthetic data to a user to detect feedback on the example synthetic data. Based on the feedback, the LLM may generate additional synthetic data. The techniques may then embed the synthetic data to generate an embedding space for training a machine learning model.