Invented by Michael S. Santarone, Jason E. Duff, Michael Wodrich, Middle Chart LLC
The Middle Chart LLC invention works as followsMethods and apparatuses for updating data descriptive procedure within a building to create a user interface that represents the structure according to an interest direction and from a particular vantage point. A sensor records the procedure, and the position of that condition is determined by wireless communication with multiple reference transceivers. A virtual model is augmented with the positional coordinates of the procedure and its conditions.
Background for Methods for updating data in a model virtual of a building
The traditional methods of assessment involve the physical inspection and preparation of an inspector report. The reports have traditionally been limited to only those physical components of the inspected structure that are visible with the naked eye in a particular moment. Inspections can include data on historical, zoning and environmental conditions, as well as installed components, but in some advanced iterations.
The limitations of previously known inspection techniques lead to systemic inefficiencies when it comes to the assessment and mitigation risk. The inspection, which is limited to snapshot data collection by nature, creates a disconnect between the assessed risk and the actual or real risk. This gap leads to structural inefficiencies within the industry of risk assessment and mitigation, which in turn lead to an inefficient use and leverage of collateral. It also forces investors to engage further in inefficient hedging behavior when considering collateralized and asset-backed securities.
It may also be desirable to allow investors to take remedial measures to alleviate conditions that add additional risk to an investing.
Accordingly the present invention provides an apparatus and method for measuring the physical conditions of a Structure multiple times in time, and aggregating the data generated from the physical measurements. Quantified measurements can be recorded as SVCM. The physical conditions of a structure can be filtered, classified, and arranged hierarchically, allowing a logical systems to quickly determine real-time or near-real-time events and respond accordingly. The SVCM can categorize data according to a hierarchy that is capable of influencing a Structure’s capability to demonstrate the physical conditions required and/or desired for deployment. Measured conditions can indicate, for example, that a structure complies to applicable building codes, American National Standards Institute standards, Costar Ratings, or other deployment rating schemes.
Deployment factors can also include whether the Structure requires repairs or upgrades in order to meet a certain rating schema, or if it will interrupt or disrupt its deployment. Another aspect is that physical conditions can be monitored to provide objective indicators of risk for a Structure being used as collateral or as an asset in the event of an upcoming occurrence.
The SVCM of Structures, as well as the incorporation into AVM of the Structures of the data collected by SVCM, solves the problem of limited Inspection by first providing a more accurate initial inspection through the inclusion of obfuscated features and then BIM by which greater accuracy is provided for the model by human visual inspection. SVCM and the incorporation into AVM the data collected from Structures solves the problem of limited inspection by first providing a more accurate initial inspection, including obfuscated as built features. BIM of the Structures themselves provides greater detail accuracy to the model than human visual inspection.
The sheer volume of information prevents efficient use. The mere incorporation of raw SVCM into an AVM produces vast amounts of unusable data that, without categorization or hierarchical organization, are in some ways not better than traditional inspection methods. The volume of data generated is too large to be categorized and arranged manually. More advanced techniques are required to make the data usable.
Generally, the data collected by SVCM for an AVM in a Structure will be categorized by asset type and data type. In certain embodiments, the data can be divided into categories such as historical data, structural information, environmental information, and usage data. In some embodiments, the data can be arranged both categorically and chronologically in a class. Users may then access this information on demand. In other embodiments, data may be subdivided by logical functions that are well known to those in the know. This allows predictive analytics to be applied on a collateral asset.
The data categories for an asset or asset category are defined and then the classes or categories that are defined are given logical numerical identifiers which are understood by normal logical processes.
SVCM data must be filtered, categorized and matched to the established asset classes or asset classes. The data collected by SVCM has a unique origin and, in some cases, a direction. SVCM data is therefore easier to categorize logically than data collected in other ways. Data collected by SVCM can be matched and assigned to corresponding data based upon pre-set parameters. Smart contracts and Blockchain technologies may be used in some versions of the invention to categorize, collect, and relay data collected by SVCM.
In one iteration of the present invention, pre-set logical processes may be employed to trigger mechanical responses within a given asset or relating to a particular asset to mitigate a risk. In certain iterations, pre-set logic processes can be used to trigger mechanical reactions within an asset or related to an asset in order to mitigate a risk.
Categorized data collected by SVCM for an asset can be added to AVMs of a particular asset, and a risk profile of the asset is then established. The system can display data points that indicate a greater risk of a given asset’s viability graphically or in other ways. In certain iterations, automated processes can be initiated based on the achievement of specific risk indexes that mitigate, reduce or otherwise address risk.
Users involved in risk assessment and mitigating can access the information by adding the direction of the sensor that is gathering the data during the SVCM. Some data points recorded by SVCM can be correlated to known risk factors, or they may be assigned other costs. This allows for more accurate and current loan-to-value ratios to be monitored and correcting imbalances in LTV. Smart Contracts or Blockchain Technology may be used in some embodiments to take automatic or nearly automatic corrective actions when LTV exceeds prescribed parameters.
The present invention is an automated apparatus that allows for better modeling, deployment and updating of structures. This improved modeling is based on the generation of As Built and Experiential Data using one or both Smart Devices or Sensors located within or proximate to a Structure. Automated apparatus can also be used to model compliance to one or more performance levels of the Structure for processing of a product.
In another aspect of the invention, a virtual structure extends past a design phase of the Structure to an “As Built” stage of the Structure and includes generation and analysis of Experiential Data capturing conditions realized by the Structure during a Deployment stage of the Structure. The Structure is also included in the generation and analysis Experiential Data that captures conditions experienced by the Structure at a deployment stage.
In general As Built and Experienced Data generated by the present invention includes one or more: image data, measurements, component placement specifications; solid state devices; electrical; and electronic devices (or combinations thereof); and generate data capturing situations experienced by a structure. A user can also enter data into the Augmented Virtual Mode. For example, data that describes an action performed by a technician. The As Built Data and the Experiential Data can be aggregated to a single structure or multiple structures. A Structure can also be made up of multiple Structures or a single one.
As Built Data” is collected to quantify details on how a physical Structure was constructed. In the present invention, Structures are designed and modeled within a virtual 3D environment. In a virtual environment, As Built Data and a design model are combined to create an AVM. As Built Data can reflect: fabrication of Structure, repair, maintenance, upgrades and improvements.
Experiential Data can also be generated and input into the AVM Virtual Model of the Structure. The Experiential Data can include data that is indicative of a particular factor which may be tracked or measured with respect to the Structure. Experiential data is generated typically by Sensors located in or near the Structure. Examples include: accelerometers, force transducers, temperature sensors, amp meters, ohmmeters and switches; light wavelength capture devices (such infrared temperature profiles), water flow meters and air flow meters. Experiential Data can include, for example, information about the operation of machinery or equipment in the Structure, vibration measurements, electrical current draw, machine run times, run rates, and machine parameters, interior and/or external temperatures, opening and closing of doors and windows, weight loads, preventive maintenance, cleaning cycles, air circulation, mold contents, thermal profiles, and the like. Automated apparatus collects empirical data both during the construction of the structure and during deployment of the structure.
By way of an additional example, water consumption of a Structure or a class of structures will be analyzed in order to determine whether it is wise to modify the Structure or class. The automated apparatuses of the present invention include As Built Data that can be accessed when modeling proposed upgrades and modifications. Relevant As-Built Features can include features that may be obvious to the user, like utility requirements, electrical supply, chemical waste disposal and air handling equipment. Other As Built Features that are not obvious but have a correlation with unstructured questions may be included.
Unstructured query analyses may also consider relevant the location of appliances, machinery, equipment and utilities in relation to other appliances and machines. Unstructured queries of data quantifying actual chemical, atmospheric and water use may indicate that certain configurations are better suited to achieving an objective. It may be found that a single-story structure is more likely than a multistory structure to maintain a constant internal temperature, lighting or ambient particulate.
As discussed in more detail below, captured data can include empirical quantifications such as the number of times that a piece or machinery is turned on and off, vibrations inside a structure, temperature within a structure, doors opening and shutting, quantities of products processed, and hours spent within a structure. Data captured can also be used for determining how a Structure has been used. For example, production cycles, quality and yield, rates, volume, etc. As will be discussed in more detail below, empirical Sensor Data associated with the behavior of specific personnel within a structure may also be correlated to Structure Performance depending on who occupies that Structure, when and for how long.Click here to view the patent on Google Patents.