Abstract: A fire suppressing insulation. This insulation will create a barrier that blocks a fire to protect a structure from damage and will assist in suppressing the fire. Also, a fire suppression system including two walls, such as an interior wall and an exterior wall or two interior walls. The walls are spaced apart from each other by a divider to define an interior volume. At least a portion of the interior volume includes fire suppressing insulation that will form a fire suppressing intumescent barrier between the walls when exposed to the heat of a fire.

Abstract: A system for dispensing flame retardant foam on the exterior of a structure. The structure has an opening that enables distribution of flame retardant foam onto the roof. The system includes a foam distribution system having a foam expansion chamber and a roof plenum for delivery through the roof opening. Foam solution and compressed air are combined in a conduit and supplied to the foam expansion chamber. The structure may have at least one wall. In that case, the system may have a wall plenum for delivery of flame retardant foam from the foam expansion chamber through a first wall-associated opening. Each such wall will have an opening associated with the wall that enables distribution of flame retardant foam onto that wall.

Abstract: Systems and methods for digital integration of financial features between a user and a service provider is provided. For example, if a user device is registered with the service provider, the user device may receive an electronic notification (e.g., calendar entry) related to outstanding account operations. The user device may be able to complete the outstanding account operations by accessing the electronic notification and embedded financial information.

Abstract: Embodiments are directed towards infrastructure benchmarking based on dynamic cost modeling. Raw customer models that represent a customer's business system may be ingested into a benchmarking system and mapped to customer models based on one or more standard models. Benchmarking information that may be generated from one or more community models that are one or more customer models of other customers that may be available to the benchmarking application. Additional third-party information, such as, industry survey information may also be used for generating benchmarking information. The benchmarking information may be used to generate reports that include comparisons between one or more customer models and the one or more community models. The benchmarking information may be continuously updated based on changes to customer models and/or data, community models and/or data, external information.

Abstract: Embodiments are directed towards for models for visualizing resource allocation. A plurality of data models may be provided to a benchmarking engine. The benchmarking engine may be employed to provide a plurality of benchmark models based on the plurality of data models. If an organization is selected, additional actions may be performed including: providing one or more of the plurality of benchmark models based on a data model that corresponds to the organization and one or more model objects of the data model; and, employing a visualization engine to provide a visualization that compares one or more resource allocations associated with the one or more model objects with the one or more benchmark models, such that the visualization is displayed on a hardware display for viewing by a user.

Abstract: Embodiments are directed to identifying allocation discrepancies. Data models and Benchmark models may be provided to an analysis engine. Discrepancy models may be provided to the analysis engine, such that each discrepancy model may be arranged to include one or more rules. The analysis engine may be employed to search for discrepancies in the data models based on the discrepancy models and the benchmark models. If discrepancies may be identified by the analysis engine, one or more notifications may be provided to one or more users. Also, the discrepancy models may be modified based on subsequent feedback provided by the one or more users. Correlations in the resource allocation values may be identified based on machine learning that includes one or more of linear regression, deep learning neural networks, or the like. And, additional discrepancy models may be provided based on the identified correlations.

Abstract: Embodiments are directed to identifying allocation discrepancies. Data models and Benchmark models may be provided to an analysis engine. Discrepancy models may be provided to the analysis engine, such that each discrepancy model may be arranged to include one or more rules. The analysis engine may be employed to search for discrepancies in the data models based on the discrepancy models and the benchmark models. If discrepancies may be identified by the analysis engine, one or more notifications may be provided to one or more users. Also, the discrepancy models may be modified based on subsequent feedback provided by the one or more users. Correlations in the resource allocation values may be identified based on machine learning that includes one or more of linear regression, deep learning neural networks, or the like. And, additional discrepancy models may be provided based on the identified correlations.

Abstract: Embodiments are directed towards for models for visualizing resource allocation. A plurality of data models may be provided to a benchmarking engine. The benchmarking engine may be employed to provide a plurality of benchmark models based on the plurality of data models. If an organization is selected, additional actions may be performed including: providing one or more of the plurality of benchmark models based on a data model that corresponds to the organization and one or more model objects of the data model; and, employing a visualization engine to provide a visualization that compares one or more resource allocations associated with the one or more model objects with the one or more benchmark models, such that the visualization is displayed on a hardware display for viewing by a user.

Abstract: Embodiments are directed towards infrastructure benchmarking based on dynamic cost modeling. Raw customer models that represent a customer's business system may be ingested into a benchmarking system and mapped to customer models based on one or more standard models. Benchmarking information that may be generated from one or more community models that are one or more customer models of other customers that may be available to the benchmarking application. Additional third-party information, such as, industry survey information may also be used for generating benchmarking information. The benchmarking information may be used to generate reports that include comparisons between one or more customer models and the one or more community models. The benchmarking information may be continuously updated based on changes to customer models and/or data, community models and/or data, external information.