With embodiments, the AI FCM tool 110 provides the ability to understand at the point of fact creation where a fact fits in the larger document context and what that fact should convey and to whom. The AI FCM tool 110 not only indicates what is missing, but what the threshold of acceptability is. The AI FCM tool 110 also offers suggestions (e.g., examples, code, etc.) for how to complete/compute a fact (e.g., assisting in the production of the fact in an active way, such as: “Here's how to do that”). The AI FCM tool 110 ensures that a fact meets the policy requirements at the time of creation, reducing the likelihood of incomplete or sub-standard information flowing downstream to other users.

The AI FCM tool 110 improves quality, efficiency, and consistency of FactSheets within a team and across teams. The AI FCM tool 110 increases visibility and aids in the coordination of a multi-person documentation effort. The AI FCM tool 110 increases awareness of documentation work that has been done and indicates what still needs to be done.

In certain embodiments, the AI FCM tool 110 provides a technique for the coordination and collaboration of fact producers via the inclusion of an intermediate and dynamic preview of the documentation state across fact producers within their working environment (e.g., via a user tool). In such embodiments, the AI FCM tool 110 enables selection of a default preview mechanism in a working environment and enables selection of a template (or a default selection of a template), which defines the facts that are to be collected and their associated renderings. FIG. 4A illustrates a there is a “See FactSheet” selector that gives access to a preview of a (partially completed) FactSheet. This preview may be rendered in different ways (compact, extended, side panel, hover, etc.) and within that views may show the facts (provided and missing), which are most relevant at this time for this role.

The AI FCM tool 110 retrieves facts from the facts repository using the definitions in the template, displays a preview based on the template's facts, and indicates missing or required facts in the preview. The AI FCM tool 110 also dynamically updates the preview to reflect the current state of the fact repository across multiple fact producers (i.e., the preview is updated as facts are being provided by multiple fact producers).

In certain embodiments, the AI FCM tool 110 provides a preview that identifies to the user missing facts and other policy-related requirements they are responsible for. The AI FCM tool 110 provides the user a way to store facts to the facts repository and displays those facts in the preview. The AI FCM tool 110 allows for version visualization and coordination (e.g., allows multiple producers to agree on which facts to save). The AI FCM tool 110 allows fact validation within prior to being committed to the fact repository. The fact validation may include validating a value (e.g., based on a policy indicating the value should have 3 decimals), determining whether a computation is accurate, etc. by using the policies. The AI FCM tool 110 provides conflict resolution when different fact producers have provided facts that conflict (e.g., with input from the fact producers or others). The AI FCM tool 110 provides fact quality evaluation based on the policies (e.g., a certain value is not allowed). The AI FCM tool 110 provides similar examples and definitions. The AI FCM tool 110 performs policy enforcement (immediate checks, acceptable values, etc.) on facts.

In certain embodiments, the AI FCM tool 110 enables collaborative and dynamic rendering and affirming of facts. The FactSheet preview is dynamic and collaboration is supported through affordances.

In certain embodiments, the AI FCM tool 110 provides a preview that supports governance and collaboration by enabling the dynamic creation of FactSheets across users and tools.

FIG. 8 illustrates, in a block diagram, details of a machine learning model 800 in accordance with certain embodiments. In certain embodiments, the one or more models 130 are implemented using the components of the machine learning model 800.

The machine learning model 800 may comprise a neural network with a collection of nodes with links connecting them, where the links are referred to as connections. For example, FIG. 8 shows a node 804 connected by a connection 808 to the node 806. The collection of nodes may be organized into three main parts: an input layer 810, one or more hidden layers 812, and an output layer 814.

The connection between one node and another is represented by a number called a weight, where the weight may be either positive (if one node excites another) or negative (if one node suppresses or inhibits another). Training the machine learning model 800 entails calibrating the weights in the machine learning model 800 via mechanisms referred to as forward propagation 816 and backward propagation 822. Bias nodes that are not connected to any previous layer may also be maintained in the machine learning model 800. A bias may be described as an extra input of 1 with a weight attached to it for a node.

In forward propagation 816, a set of weights are applied to the input data 818 . . . 820 to calculate the output 824. For the first forward propagation, the set of weights may be selected randomly or set by, for example, a system administrator. That is, in the forward propagation 816, embodiments apply a set of weights to the input data 818 . . . 820 and calculate an output 824.

In backward propagation 822 a measurement is made for a margin of error of the output 824, and the weights are adjusted to decrease the error. Backward propagation 822 compares the output that the machine learning model 800 produces with the output that the machine learning model 800 was meant to produce, and uses the difference between them to modify the weights of the connections between the nodes of the machine learning model 800, starting from the output layer 814 through the hidden layers 812 to the input layer 810, i.e., going backward in the machine learning model 800. In time, backward propagation 822 causes the machine learning model 800 to learn, reducing the difference between actual and intended output to the point where the two come very close or coincide.

The machine learning model 800 may be trained using backward propagation to adjust weights at nodes in a hidden layer to produce adjusted output values based on the provided inputs 818 . . . 820. A margin of error may be determined with respect to the actual output 824 from the machine learning model 800 and an expected output to train the machine learning model 800 to produce the desired output value based on a calculated expected output. In backward propagation, the margin of error of the output may be measured and the weights at nodes in the hidden layers 812 may be adjusted accordingly to decrease the error.

Backward propagation may comprise a technique for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the technique may calculate the gradient of the error function with respect to the artificial neural network's weights.

Thus, the machine learning model 800 is configured to repeat both forward and backward propagation until the weights of the machine learning model 800 are calibrated to accurately predict an output.

The machine learning model 800 implements a machine learning technique such as decision tree learning, association rule learning, artificial neural network, inductive programming logic, support vector machines, Bayesian models, etc., to determine the output value 824.

In certain machine learning model 800 implementations, weights in a hidden layer of nodes may be assigned to these inputs to indicate their predictive quality in relation to other of the inputs based on training to reach the output value 824.

With embodiments, the machine learning model 800 is a neural network, which may be described as a collection of “neurons” with “synapses” connecting them.

With embodiments, there may be multiple hidden layers 812, with the term “deep” learning implying multiple hidden layers. Hidden layers 812 may be useful when the neural network has to make sense of something complicated, contextual, or non-obvious, such as image recognition. The term “deep” learning comes from having many hidden layers. These layers are known as “hidden”, since they are not visible as a network output.

In certain embodiments, training a neural network may be described as calibrating all of the “weights” by repeating the forward propagation 816 and the backward propagation 822.

In backward propagation 822, embodiments measure the margin of error of the output and adjust the weights accordingly to decrease the error.

Neural networks repeat both forward and backward propagation until the weights are calibrated to accurately predict the output 824.

In certain embodiments, the inputs to the machine learning model 800 are a fact, a fact definition, and metadata, and the outputs of the machine learning model 800 are one or more outputs from a group comprising: suggestions, examples, and supporting information about the fact.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

FIG. 9 illustrates a computing environment 900 in accordance with certain embodiments. Computing environment 900 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as the AI FCM tool 110. In addition to block 110, computing environment 900 includes, for example, computer 901, wide area network (WAN) 902, end user device (EUD) 903, remote server 904, public cloud 905, and private cloud 906. In this embodiment, computer 901 includes processor set 910 (including processing circuitry 920 and cache 921), communication fabric 911, volatile memory 912, persistent storage 913 (including operating system 922 and block 110, as identified above), peripheral device set 914 (including user interface (UI) device set 923, storage 924, and Internet of Things (IoT) sensor set 925), and network module 915. Remote server 904 includes remote database 930. Public cloud 905 includes gateway 940, cloud orchestration module 941, host physical machine set 942, virtual machine set 943, and container set 944.

COMPUTER 901 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 930. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 900, detailed discussion is focused on a single computer, specifically computer 901, to keep the presentation as simple as possible. Computer 901 may be located in a cloud, even though it is not shown in a cloud in FIG. 9. On the other hand, computer 901 is not required to be in a cloud except to any extent as may be affirmatively indicated.

PROCESSOR SET 910 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 920 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 920 may implement multiple processor threads and/or multiple processor cores. Cache 921 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set