# US 11,443,225 B2 · Systems and methods for intelligently providing supporting information using machine-learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,225 B2 - Document ID: US11443225B2 - Status: Granted - Kind: B2 - Application number: 15/485,516 - Filed: 2017-04-12 - Published: 2018-10-04 - Granted: 2022-09-13 - Earliest claimed: 2017-03-28 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Li Li; Xiaoyu Peng; Kehua Pan - Web page: https://patentgrep.com/patent/US11443225B2 ## CPC - G06N 20/00: Machine learning - G06N 5/04: Inference or reasoning models - G06N 5/045: Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence - G06Q 10/02: Reservations, e.g. for tickets, services or events - G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - H04L 67/52: specially adapted for the location of the user terminal - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Systems and methods for intelligently providing users with supporting information based on big-data analyses of a data set. Machine-learning algorithms may be executed using the data set to identify correlations between data objects of the data set. The correlations can be used to recommend supporting information to a user. A user interface can be provided to enable a user to initiate a process associated with an event. In response to receiving the input, the system can identify variables associated with the request. Based on these variables, the system can retrieve output data of the machine-learning algorithms to identify the supporting information for the user. ## Claims ### Claim 1 A computer-implemented method comprising: collecting, at an expense tracking system, a training data set for training a machine-learning model, the training data set representing one or more travel events that previously occurred, each travel event of the one or more travel events corresponding to an expense incurred by a user and one or more event parameters that identify a characteristic of the travel event; defining one or more evaluation metrics using the one or more event parameters, each evaluation metric classifying the one or more travel events into an event type; training the machine-learning model using the training data set, the trained machine-learning model comprising a plurality of nodes including at least a first node associated with a first event type training comprising assigning a weight to each of the plurality of nodes and generating one or more trained correlations between at least two nodes of the plurality of nodes, each node representing a value associated with a travel event of the one or more travel events, wherein each of at least one node of the plurality of nodes corresponds to a hierarchical tree structure; detecting a first communication from a computing device operated by a particular user, the first communication corresponding to a request to initiate a process associated with a particular travel event, the request being associated with an actual or anticipated expense, the particular travel event corresponding to the first node having a first event type, and the first communication being received at the expense tracking system; in response to detecting the first communication, determining one or more variables from the request, each variable of the one or more variables representing a characteristic of the particular travel event, wherein the one or more variables comprise a client variable that includes a request for a reimbursement for a portion of the actual or anticipated expense; in response to determining the one or more variables, accessing one or more rules associated with the client variable, wherein the one or more rules includes selecting nodes representing values associated with a travel events associated with a reimbursement action; mapping the one or more variables to the plurality of nodes of the machine-learning model; identifying, based at least in part on the mapping, one or more nodes for each of the one or more variables, the one or more nodes being included in the plurality of nodes of the machine-learning model, and the one or more trained correlations being used to identify the one or more nodes, wherein the identified one or more nodes are restricted to a subset of nodes based on the accessed one or more rules, and wherein restricting the identified one or more nodes to the subset of nodes comprises including nodes representing values associated with a travel events associated with a reimbursement action in the subset of nodes; retrieving one or more travel event values, each travel event value of the one or more travel event values associated with an action corresponding to an event type; generating, by inputting the one or more variables into the trained machine-learning model and based on the one or more travel event values, an output predicting supporting information that identifies a plurality of actions comprising one or more first actions and one or more second actions, each action of the plurality of actions having occurred in association with the particular travel event, and wherein each of the plurality of actions are of a different event type from the particular travel event and at least one action of the plurality of actions is of a second event type; and transmitting a second communication to the computing device, the second communication being responsive to the first communication, comprising the one or more travel event values, and identifying the supporting information. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein collecting the training data set is continuously performed, such that when a new travel event has occurred, the new travel event is included in the training data set. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: updating the machine-learning model when the new travel event is included in the training data set, such that at least one weight that corresponds to a node of the plurality of nodes is updated. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the particular travel event corresponds to a future travel event, wherein the supporting information indicates which of the one or more first actions or the one or more second actions are recommended. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular travel event, wherein the third communication is received after the first communication is received and before the particular travel event occurs; identifying that the first communication and the third communication each correspond to the particular travel event; and transmitting an alert message to the additional computing device, the alert message including a notification that the particular user associated with the first communication is also associated with the particular travel event. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising: transmitting a fourth communication to the additional computing device, the fourth communication being responsive to the third communication. ### Claim 7 (depends on 5) The computer-implemented method of claim 5, wherein the particular user associated with the first communication and a different user associated with the third communication are each associated with a same entity. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: detecting that a particular node of the one or more nodes is associated with a predicted occurrence, the predicted occurrence corresponding to an event parameter exceeding a defined threshold; and accessing a workflow associated with the predicted occurrence, the workflow including an identification of one or more documents associated with the predicted occurrence, the one or more documents identifying a procedure for obtaining a reimbursement associated with the predicted occurrence. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the particular travel event is a work-related travel event that has previously occurred in association with an entity employing the particular user, at least one node of the one or more nodes corresponds to a workflow for identifying one or more documents that identify a procedure for obtaining a reimbursement associated with the particular travel event, and the reimbursement being provided by the entity. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the trained machine-learning model having been trained to detect co-occurrences, among events that have previously occurred, of two or more events having different characteristics and being of different event types, and wherein the trained machine-learning model having been further trained to detect one or more client preferences. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, wherein the supporting information includes any one or more items from a group including a specific document, third-party data, a schedule, an identifier of a user, or a recommendation. ### Claim 12 A system, comprising: one or more processors; and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including: collecting, at an expense tracking system, a training data set for training a machine-learning model, the training data set representing one or more travel events that previously occurred, each travel event of the one or more travel events corresponding to an expense incurred by a user and one or more event parameters that identify a characteristic of the travel event; defining one or more evaluation metrics using the one or more event parameters, each evaluation metric classifying the one or more travel events into an event type; training the machine-learning model using the training data set, the trained machine-learning model comprising a plurality of nodes including at least a first node associated with a first event type, the training comprising assigning a weight to each of the plurality of nodes and generating one or more trained correlations between at least two nodes of the plurality of nodes, each node representing a value associated with a travel event of the one or more travel event, wherein each of at least one node of the plurality of nodes corresponds to a hierarchical tree structure; detecting a first communication from a computing device operated by a particular user, the first communication corresponding to a request to initiate a process associated with a particular travel event, the request being associated with an actual or anticipated expense, the particular travel event corresponding to the first node having a first event type, and the first communication being received at the expense tracking system; in response to detecting the first communication, determining one or more variables from the request, each variable of the one or more variables representing a characteristic of the particular travel event, wherein the one or more variables comprise a client variable that includes a request for a reimbursement for a portion of the actual or anticipated expense; in response to determining the one or more variables, accessing one or more rules associated with the client variable, wherein the one or more rules includes selecting nodes representing values associated with a travel events associated with a reimbursement action; mapping the one or more variables to the plurality of nodes of the machine-learning model; identifying, based at least in part on the mapping, one or more nodes for each of the one or more variables, the one or more nodes being included in the plurality of nodes of the machine-learning model, and the one or more trained correlations being used to identify the one or more nodes, wherein the identified one or more nodes are restricted to a subset of nodes based on the accessed one or more rules, and wherein restricting the identified one or more nodes to the subset of nodes comprises including nodes representing values associated with a travel events associated with a reimbursement action in the subset of nodes; retrieving one or more travel event values, each travel event value of the one or more travel event values associated with an action corresponding to an event type; generating, by inputting the one or more variables into the trained machine-learning model and based on the one or more travel event values, an output predicting supporting information that identifies a plurality of actions comprising one or more first actions and one or more second actions, each action of the plurality of actions having occurred in association with the particular travel event, and wherein each of the plurality of actions are of a different event type from the particular travel event and at least one action of the plurality of actions is of a second event type; and transmitting a second communication to the computing device, the second communication being responsive to the first communication, comprising the one or more travel event values, and identifying the supporting information. ### Claim 13 (depends on 1) The computer-implemented method of claim 1, wherein: generating the output predicting supporting information is based on selecting nodes of the subset of nodes corresponding to higher weights; and the second communication further comprises the weights corresponding to the one or more travel event values. ### Claim 14 (depends on 1) The computer-implemented method of claim 1, further comprising: detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular travel event; identifying that the first communication and the third communication each correspond to the particular travel event; generating an inference that an additional user of the additional computing device is interested in coordinating aspects of the particular travel event with a particular user of the computing device; and sending a message comprising the inference to the computing device. ### Claim 15 (depends on 1) The computer-implemented method of claim 1, further comprising: detecting a third communication from an additional computing device, wherein the third communication corresponds to a request to initiate a process associated with an additional travel event different than the particular travel event; determining that the particular user of the computing device and an additional user of the additional computing device share a particular user attribute; identifying in real-time a first location of the computing device and a second location of the additional computing device; determining that the first location and the second location are within a predefined distance of each other; and transmitting an alert message to the computing device, the alert message including a notification that an additional user associated with the third communication is within the distance of the computing device. ### Claim 16 (depends on 1) The computer-implemented method of claim 1, wherein: the first communication from the computing device is a request to obtain a reimbursement and the one or more variables include a request for a reimbursement for a portion of the actual or anticipated expense; the second communication transmitted to the computing device includes one or more documents related to obtaining a reimbursement for the one or more travel event values in the second communication. ### Claim 17 (depends on 1) The computer-implemented method of claim 1, wherein the particular user is a first user and the particular travel event is a first particular travel event, the method further comprising: receiving a document corresponding to a second particular travel event associated with a second user, the second particular travel event being a same type of travel event as the first particular travel event and having occurred prior to the detecting the first communication from the computing device; and sending, to the first user as part of the second communication transmitted to the computing device, the document corresponding to the second particular travel event. ### Claim 18 (depends on 12) The system of claim 12, wherein the trained machine-learning model having been trained to detect co-occurrences, among events that have previously occurred, of two or more events having different characteristics and being of different event types, and wherein the trained machine-learning model having been further trained to detect one or more client preferences. ### Claim 19 (depends on 12) The system of claim 12, wherein the operations further comprise: detecting a third communication from an additional computing device, wherein the third communication corresponds to another request to initiate another process associated with the particular travel event, wherein the third communication is received after the first communication is received and before the particular travel event occurs; identifying that the first communication and the third communication each correspond to the particular travel event; and transmitting an alert message to the additional computing device, the alert message including a notification that the particular user associated with the first communication is also associated with the particular travel event. ### Claim 20 (depends on 12) The system of claim 12, wherein the operations further comprise: detecting that a particular node of the one or more nodes is associated with a predicted occurrence, the predicted occurrence corresponding to an event parameter exceeding a defined threshold; and accessing a workflow associated with the predicted occurrence, the workflow including an identification of one or more documents associated with the predicted occurrence, the one or more documents identifying a procedure for obtaining a reimbursement associated with the predicted occurrence. ### Claim 21 (depends on 12) The system of claim 12, wherein the particular travel event is a work-related travel event that has previously occurred in association with an entity employing the particular user, at least one node of the one or more nodes corresponds to a workflow for identifying one or more documents that identify a procedure for obtaining a reimbursement associated with the particular travel event, and the reimbursement being provided by the entity. ### Claim 22 (depends on 12) The system of claim 12, wherein the supporting information includes any one or more items from a group including a specific document, third-party data, a schedule, an identifier of a user, or a recommendation.