Abstract: A share allocation (SA) computing device includes a processor in communication with a database. The processor is configured to execute a computational model including a plurality of model layers. The plurality of model layers includes a fractional node layer configured to assign each candidate investor of a plurality of candidate investors to a corresponding node. Each node is associated with a weight, and the nodes define an interconnected neural network. The fractional node layer is also configured to apply a machine learning algorithm configured to adjust the weights of the nodes in response to respective fitness values input to the nodes, and convert the adjusted weight for each node into a corresponding fraction. The fractional node layer is further configured to allocate, to each candidate investor, a respective fractional share of an offering, the fractional share corresponding to the fraction associated with the corresponding node.

Abstract: In one aspect, a share data allocation (SDA) computing device is provided. The SDA computing device includes at least one processor in communication with a database. The at least one processor is configured to receive direct-share participant (DSP) request data indicating an amount each DSP is willing to invest and receive interest-based participant (IBP) request data indicating an amount each IBP is willing to invest. The at least one processor is also configured to apply a first mapping algorithm to the DSP request data and apply a second mapping algorithm to the IBP request data. The at least one processor is further configured to calculate a preliminary allocation of a total amount of assets among the DSPs and the IBPs according to results of an apportionment, and direct allocation of the total amount of assets among the DSPs and the IBPs according to results of a join operator.

Abstract: A share allocation (SA) computing device includes a processor in communication with a database. The processor is configured to execute a computational model including a plurality of model layers. The plurality of model layers includes a fractional node layer configured to assign each candidate investor of a plurality of candidate investors to a corresponding node. Each node is associated with a weight, and the nodes define an interconnected neural network. The fractional node layer is also configured to apply a machine learning algorithm configured to adjust the weights of the nodes in response to respective fitness values input to the nodes, and convert the adjusted weight for each node into a corresponding fraction. The fractional node layer is further configured to allocate, to each candidate investor, a respective fractional share of an offering, the fractional share corresponding to the fraction associated with the corresponding node.

Abstract: In one aspect, a share data allocation (SDA) computing device is provided. The SDA computing device includes at least one processor in communication with a database. The at least one processor is configured to receive direct-share participant (DSP) request data indicating an amount each DSP is willing to invest and receive interest-based participant (IBP) request data indicating an amount each IBP is willing to invest. The at least one processor is also configured to apply a first mapping algorithm to the DSP request data and apply a second mapping algorithm to the IBP request data. The at least one processor is further configured to calculate a preliminary allocation of a total amount of assets among the DSPs and the IBPs according to results of an apportionment, and direct allocation of the total amount of assets among the DSPs and the IBPs according to results of a join operator.

Abstract: A share allocation (SA) computing device includes a processor in communication with a database. The processor is configured to execute a computational model including a plurality of model layers. The plurality of model layers includes a fractional node layer configured to assign each candidate investor of a plurality of candidate investors to a corresponding node. Each node is associated with a weight, and the nodes define an interconnected neural network. The fractional node layer is also configured to apply a machine learning algorithm configured to adjust the weights of the nodes in response to respective fitness values input to the nodes, and convert the adjusted weight for each node into a corresponding fraction. The fractional node layer is further configured to allocate, to each candidate investor, a respective fractional share of an offering, the fractional share corresponding to the fraction associated with the corresponding node.

Abstract: An event analysis (EA) computing device is described. The EA computing device includes at least one processor in communication with a database and a plurality of user computing devices. The at least one processor is configured to receive a user interaction data signal, store a first data extracted from the user interaction data signal in a first database, (i) transmit a notification data signal to one of the user computing devices, and (ii) generate and store a user trail record in a second database as a linked element in a user trail. The processor is also configured to retrieve the first data from the first database and use the first data to implement a primary functionality of the EA computing device, retrieve the user trail and use the user trail to generate a report, and transmit the report to one of the user computing devices.

Abstract: In one aspect, a share data allocation (SDA) computing device is provided. The SDA computing device includes at least one processor in communication with a database. The at least one processor is configured to receive direct-share participant (DSP) request data indicating an amount each DSP is willing to invest and receive interest-based participant (IBP) request data indicating an amount each IBP is willing to invest. The at least one processor is also configured to apply a first mapping algorithm to the DSP request data and apply a second mapping algorithm to the IBP request data. The at least one processor is further configured to calculate a preliminary allocation of a total amount of assets among the DSPs and the IBPs according to results of an apportionment, and direct allocation of the total amount of assets among the DSPs and the IBPs according to results of a join operator.

Abstract: An asset vector analysis (AVA) computing device retrieves, from a database, investor data relating to a plurality of individual investors and past investment activity of the plurality of investors. The device computes, for each individual investor, an investor score, and transmits a notification of a public offering of assets to at least some of the individual investors. The device receives, from the individual investors, a response indicating an amount of money the respective investor is willing to invest in the public offering. The device determines a total amount of assets available to the individual investors in the public offering. The device allocates a portion of the total amount of assets available to the individual investors based at least in part on the investor score of individual investors.

Abstract: An automated parking facility access and payment processing system is provided. The system includes a parking facility server located proximate to a parking facility, a central authentication and payment processing server for authenticating and processing payment for a user of parking facilities, a gate controller positioned proximate to a barrier gate at the parking facility, and at least one of an identification module connected to a vehicle, and a user computer device including or accessing an automated parking and payment processing application. The gate controller is configured to receive, via Bluetooth communication with the at least one of an identification module and a user computer device, identification information about a user seeking entry into the parking facility, and to relay the identification information to the parking facility server.

Abstract: The invention relates to a directory proxy caching system that is constructed based on a predicate, i.e., a query from a client. Broadly stated, the predicate is formed by the query (request) issued by the client. Once the data is found in the directory, then the data is stored in a cache of a proxy server. The data is indexed in the proxy server by the predicate. The predicate is used to form an index by sorting the predicate into a normal form. Steps in sorting the predicate into the normal form include the following. Each symbol of the predicate is represented by a numerical representation, for example the ASCII value used to represent the symbol in ordinary text files. The predicate is expressed as a plurality of primitive predicates, and individual predicates of the plurality of primitive predicates are joined by logical connectors. The logical connectors, and each term in the primitive predicates are represented by numbers.

Abstract: The invention relates to a directory proxy caching system that is constructed based on a predicate, i.e., a query from a client. Broadly stated, the predicate is formed by the query (request) issued by the client. Once the data is found in the directory, then the data is stored in a cache of a proxy server. The data is indexed in the proxy server by the predicate. The predicate is used to form an index by sorting the predicate into a normal form. Steps in sorting the predicate into the normal form include the following. Each symbol of the predicate is represented by a numerical representation, for example the ASCII value used to represent the symbol in ordinary text files. The predicate is expressed as a plurality of primitive predicates, and individual predicates of the plurality of primitive predicates are joined by logical connectors. The logical connectors, and each term in the primitive predicates are represented by numbers.

Abstract: The invention relates to a directory proxy caching system that is constructed based on a predicate, i.e., a query from a client. Broadly stated, the predicate is formed by the query (request) issued by the client. Once the data is found in the directory, then the data is stored in a cache of a proxy server. The data is indexed in the proxy server by the predicate. The predicate is used to form an index by sorting the predicate into a normal form. Steps in sorting the predicate into the normal form include the following. Each symbol of the predicate is represented by a numerical representation, for example the ASCII value used to represent the symbol in ordinary text files. The predicate is expressed as a plurality of primitive predicates, and individual predicates of the plurality of primitive predicates are joined by logical connectors. The logical connectors, and each term in the primitive predicates are represented by numbers.

Abstract: The invention relates to a distributed directory service that is constructed based on a predicate, i.e., a query from a client. The predicate is formed by the query (request) issued by the client. The predicate is used as an index for retrieving data from a plurality of directory servers. Retrieving the data has the following steps. Each directory server is designated by a designation predicate giving a range of data values stored in the directory server. A client predicate indicates desired information. The client predicate is compared with the designation predicates to determine which directory servers may contain information requested by the client predicate. In the event that a particular designation predicate indicates that a portion of the desired information may be stored on a particular directory server, an inquiry is sent to the particular directory server for the desired information. The retrieved information is transferred to the client computer.