Abstract: Systems and methods for assisting a user of an online resource are disclosed herein. The automated assistant may receive a request for assistance to complete a task and establish an interaction with the user. The task may include a sequence of steps corresponding to a sequence of user interface (UI) elements presented on the user's display screen. The automated assistant may generate instructions for the user based on transcripts of one or more previous user interactions, and provide the instructions to the user via an assistant interface presented on the user's display screen. The automated assistant may determine whether or not the user completes a respective step of the task, and either indicate completion of the respective step or provide additional guidance.

Abstract: A method including receiving a context-specific query, specific to the user, from a user device of a user. The method also includes creating a computer-readable data structure for storing data. The computer-readable data structure is specific to the context-specific query. The method also includes determining a feature related to the context-specific query. The method also includes retrieving a user value for the feature. The user value is retrieved from a data repository storing user-specific data that includes the user value. The method also includes modifying the computer-readable data structure to generate a modified computer-readable data structure by adding the feature and the user value for the feature to the computer-readable data structure. The method also includes applying a generative model to the modified computer-readable data structure to generate an output. The output includes a context-specific answer to the context-specific query. The method also includes returning the output.

Abstract: Systems and methods are disclosed for managing a generative artificial intelligence (AI) model. Managing the generative AI model may include training or tuning the generative AI model before use or managing the operation of the generative AI model during use. Training or tuning a generative AI model typically requires manual review of outputs from the model based on the queries provided to the model to reduce hallucinations generated by the generative AI model. Once the model is in use, though, hallucinations still occur. Use of a confidence (whose generation is described herein) to train or tune the generative AI model and/or manage operation of the model reduces hallucinations, and thus improves performance, of the generative AI model.

Abstract: Systems and methods are disclosed for tuning a generative artificial intelligence (AI) model based on a knowledge base. Instead of manually generating questions relevant to the knowledge base, providing those questions to the generative AI model, and manually reviewing the answers generated by the generative AI model in order to tune the generative AI model over many iterations, a natural language processing model may be configured to leverage the knowledge base to automatically generate questions and answers based on the knowledge base. In this manner, the natural language processing model is able to generate tuning data that may be used to automatically tune the generative AI model. The systems and methods also disclose automatic tuning of the generative AI model, including testing and feedback that may be used to improve tuning of the generative AI model.

Abstract: In some examples, a system may receive log data including a depth-series of data for a sensed parameter. The system may determine a parameter value of the depth series data for individual subunits of depth corresponding to a larger unit of depth. The system may further determine a scale of graphic effects corresponding to parameter values for the depth-series data. The system may present, on a display, a visualization of the depth-series data. For instance, the visualization may include a plurality of cells arranged in a plurality of rows, with each cell corresponding to the larger unit of depth and including a plurality of subcells corresponding to the subunits of depth. Additionally, each subcell may be presented with a respective graphic effect corresponding to the parameter value determined at a corresponding depth, and the graphic effect may correspond to the parameter value on the scale of graphic effects.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: In some examples, a system may receive log data including a depth-series of data for a sensed parameter. The system may determine a parameter value of the depth series data for individual subunits of depth corresponding to a larger unit of depth. The system may further determine a scale of graphic effects corresponding to parameter values for the depth-series data. The system may present, on a display, a visualization of the depth-series data. For instance, the visualization may include a plurality of cells arranged in a plurality of rows, with each cell corresponding to the larger unit of depth and including a plurality of subcells corresponding to the subunits of depth. Additionally, each subcell may be presented with a respective graphic effect corresponding to the parameter value determined at a corresponding depth, and the graphic effect may correspond to the parameter value on the scale of graphic effects.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: Systems and methods are directed to oil and gas management. In example implementations, data relating to oil and gas management is aggregated and placed in a cube along several cube dimensions, which can include scalar, temporal and spatial dimensions. The dimensions can be structured as a hierarchy, such that output can be generated as a roll up or roll down along the hierarchy.

Abstract: Provided is a production amount prediction system including: a storage unit which stores a production amount prediction model which is based on resources information including a resources amount obtained in a previously drilled wellbore and a resources recovery probability in the vicinity thereof; an input unit which receives a trajectory coordinate of a planned wellbore as an input; a production amount prediction unit which calculates a production amount of the planned wellbore based on the production amount prediction model by using a degree of influence of the previous wellbore on the planned wellbore as at least one parameter; and a display unit which displays the production amount of resources of the planned wellbore calculated by the production amount prediction unit.

Abstract: The problem solved by this invention is to convert text information in a geology report to numerical values which reflects geological characteristics of a well's subsurface. Prior art referred above cannot be applicable to this problem. Since text information in the geology report is in the natural language form. This information is not widely used in this industry, due to the fact that the text information can be hardly extracted and summarized into numerical values and integrated into current physical geology models or statistical models. This invention makes the text information in geology report, which is often in a natural language form, easier to be integrated into current geology physical models or statistical models. Also, the numerical values extracted from the geology report can be integrated with other kinds of data, such as seismic data and well-logging data, to obtain more accurate and comprehensive analysis results.

Abstract: Systems and methods are directed to oil and gas management. In example implementations, data relating to oil and gas management is aggregated and placed in a cube along several cube dimensions, which can include scalar, temporal and spatial dimensions. The dimensions can be structured as a hierarchy, such that output can be generated as a roll up or roll down along the hierarchy.

Abstract: The problem solved by this invention is to convert text information in a geology report to numerical values which reflects geological characteristics of a well's subsurface. Prior art referred above cannot be applicable to this problem. Since text information in the geology report is in the natural language form. This information is not widely used in this industry, due to the fact that the text information can be hardly extracted and summarized into numerical values and integrated into current physical geology models or statistical models. This invention makes the text information in geology report, which is often in a natural language form, easier to be integrated into current geology physical models or statistical models. Also, the numerical values extracted from the geology report can be integrated with other kinds of data, such as seismic data and well-logging data, to obtain more accurate and comprehensive analysis results.

Abstract: Provided is a production amount prediction system including: a storage unit which stores a production amount prediction model which is based on resources information including a resources amount obtained in a previously drilled wellbore and a resources recovery probability in the vicinity thereof; an input unit which receives a trajectory coordinate of a planned wellbore as an input; a production amount prediction unit which calculates a production amount of the planned wellbore based on the production amount prediction model by using a degree of influence of the previous wellbore on the planned wellbore as at least one parameter; and a display unit which displays the production amount of resources of the planned wellbore calculated by the production amount prediction unit.