# US 12,764,701 B2 · Local planning optimization using machine learning and signal strength > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,764,701 B2 - Document ID: US12764701B2 - Status: Granted - Kind: B2 - Application number: 18/301,980 - Filed: 2023-04-17 - Published: 2024-10-17 - Granted: 2026-10-13 - Earliest claimed: 2023-04-17 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Aaron K. Baughman; Eduardo Morales; Jeremy R. Fox - Web page: https://patentgrep.com/patent/US12764701B2 ## CPC - H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management - H04B 17/318: Received signal strength - H04L 41/16: using machine learning or artificial intelligence - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - H04B: TRANSMISSION - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A computer-implemented process using a queue management system includes the following operations. A user and a plurality of configurable nodes are identified in a scope of the queue management system. A proximity between the user and a particular one of the plurality of configurable nodes is dynamically determined in real-time. Using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes is determined. Based upon the probability measure exceeding a threshold, the user is added to a queue managed by the queue management system. Based upon the user being added to the queue, a state of the one of the plurality of plurality of configuration nodes is altered by the queue management system. ## Claims ### Claim 1 A computer-implemented method using a queue management system, the computer-implemented method comprising: identifying, in a scope of the queue management system, a user and a plurality of configurable nodes; dynamically determining, in real-time, a proximity between the user and a particular one of the plurality of configurable nodes; determining, using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes; adding, based upon the probability measure exceeding a threshold, the user to a queue managed by the queue management system; and altering, by the queue management system and based upon the user being added to the queue, a state of the one of the plurality of configurable nodes. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the proximity includes a scalar distance and a change of distance, and the scalar distance is a non-Euclidian distance. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the user is associated with a personal communication device and each of the plurality of configurable nodes is respectively associated with a unique communication node, and the proximity is determined using mobile signal strength. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of configurable nodes are transportation nodes. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of configurable nodes includes a plurality of different types of configurable nodes. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the threshold is dynamically adjusted using the machine learning engine. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the determining of the probability measure includes determining a probability that the user will wait for a particular type of configurable node of the plurality of configurable nodes. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the determining of the probability measure includes determining a probability that the user will travel a particular distance to the particular one of the plurality of configurable nodes. ### Claim 9 A computer hardware system including a queue management system, the computer hardware system comprising: a hardware processor configured to perform following executable operations: identifying, in a scope of the queue management system, a user and a plurality of configurable nodes; dynamically determining, in real-time, a proximity between the user and a particular one of the plurality of configurable nodes; determining, using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes; adding, based upon the probability measure exceeding a threshold, the user to a queue managed by the queue management system; and altering, by the queue management system and based upon the user being added to the queue, a state of the one of the plurality of configurable nodes. ### Claim 10 (depends on 9) The computer hardware system of claim 9, wherein the proximity includes a scalar distance and a change of distance, and the scalar distance is a non-Euclidian distance. ### Claim 11 (depends on 9) The computer hardware system of claim 9, wherein the user is associated with a personal communication device and each of the plurality of configurable nodes is respectively associated with a unique communication node, and the proximity is determined using mobile signal strength. ### Claim 12 (depends on 9) The computer hardware system of claim 9, wherein the plurality of configurable nodes are transportation nodes. ### Claim 13 (depends on 9) The computer hardware system of claim 9, wherein the plurality of configurable nodes includes a plurality of different types of configurable nodes. ### Claim 14 (depends on 9) The computer hardware system of claim 9, wherein the threshold is dynamically adjusted using the machine learning engine. ### Claim 15 (depends on 9) The computer hardware system of claim 9, wherein the determining of the probability measure includes determining a probability that the user will wait for a particular type of configurable node of the plurality of configurable nodes. ### Claim 16 (depends on 9) The computer hardware system of claim 9, wherein the determining of the probability measure includes determining a probability that the user will travel a particular distance to the particular one of the plurality of configurable nodes. ### Claim 17 A computer program product, comprising: a computer readable storage medium having stored therein program code, the program code, which when executed by a computer hardware system including a queue management system, causes the computer hardware system to perform: identifying, in a scope of the queue management system, a user and a plurality of configurable nodes; dynamically determining, in real-time, a proximity between the user and a particular one of the plurality of configurable nodes; determining, using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes; adding, based upon the probability measure exceeding a threshold, the user to a queue managed by the queue management system; and altering, by the queue management system and based upon the user being added to the queue, a state of the one of the plurality of configurable nodes. ### Claim 18 (depends on 17) The computer program product of claim 17, wherein the proximity includes a scalar distance and a change of distance, the scalar distance is a non-Euclidian distance, the user is associated with a personal communication device and each of the plurality of configurable nodes is respectively associated with a unique communication node, and the proximity is determined using mobile signal strength. ### Claim 19 (depends on 17) The computer program product of claim 17, wherein the plurality of configurable nodes are transportation nodes, and the plurality of configurable nodes includes a plurality of different types of configurable nodes. ### Claim 20 (depends on 17) The computer program product of claim 17, wherein the determining of the probability measure includes determining a probability that the user will wait for a particular type of configurable node of the plurality of configurable nodes, and the determining of the probability measure includes determining a probability that the user will travel a particular distance to the particular one of the plurality of configurable nodes.