# US 12,180,059 B2 · Automated container fill line with expert system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,180,059 B2 - Document ID: US12180059B2 - Status: Granted - Kind: B2 - Application number: 17/396,066 - Filed: 2021-08-06 - Published: 2023-02-09 - Granted: 2024-12-31 - Earliest claimed: 2021-08-06 - Assignees: Swimc LLC - Applicants: Swimc LLC - Inventors: Jeremy J. Kiffer; Michael W. Bigach; Sergio Cordeiro; Flavio Augusto Gomes Lacerda - Web page: https://patentgrep.com/patent/US12180059B2 ## CPC - B67D 3/0003: {provided with automatic fluid control means} - B67C 3/007: {Applications of control, warning or safety devices in filling machinery (flow-control B67C3/28)} - G05B 13/028: {using expert systems only} - B67D: DISPENSING, DELIVERING OR TRANSFERRING LIQUIDS, NOT OTHERWISE PROVIDED FOR (cleaning pipes or tubes or systems of pipes or tubes B08B9/02; emptying or filling of bottles, jars, cans, casks, barrels, or similar containers, not otherwise provided for B67C; water supply E03; pipe systems F17D; domestic hot-water supply systems F24D; measuring volume, volume flow, mass flow or liquid level, metering by volume G01F; coin-freed or like apparatus G07F) - B67C: CLEANING, FILLING WITH LIQUIDS OR SEMILIQUIDS, OR EMPTYING, OF BOTTLES, JARS, CANS, CASKS, BARRELS, OR SIMILAR CONTAINERS, NOT OTHERWISE PROVIDED FOR; FUNNELS - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract An automated container fill line system comprises a container fill line configured to transport a plurality of containers at a fill line speed, and a filler configured to dispense a material into each of the containers individually and sequentially. An Expert System is configured to control the container fill line speed using a set of hierarchical rules and one or more machine learning models associated with equipment of the container fill line. ## Claims ### Claim 1 A method implemented by an automated container fill line system, comprising: transporting a plurality of containers along a container fill line at a container fill line speed, wherein the container fill line includes equipment comprising a filler, upstream equipment situated upstream of the filler, and downstream equipment situated downstream of the filler; dispensing, by the filler, a material into each of the containers individually and sequentially; and controlling the container fill line speed by an Expert System using a set of hierarchical rules and one or more machine learning models, wherein the one or more machine learning models are associated with the upstream equipment and the downstream equipment. ### Claim 2 (depends on 1) The method according to claim 1, wherein the Expert System controls the container fill line speed in response to at least: a capacity of the equipment of the container fill line to process the containers; and a capacity of the filler of the container fill line to fill the containers while the containers are transported along the container fill line at the container fill line speed. ### Claim 3 (depends on 1) The method according to claim 1, comprising detecting, by the Expert System, whether the equipment is experiencing anomalous behavior using the one or more machine learning models. ### Claim 4 (depends on 1) The method according to claim 1, wherein the Expert System adjusts the container fill line speed by: increasing the container fill line speed in response to detecting non-violation of the hierarchical rules and detecting an absence of anomalous equipment behavior; and decreasing the container fill line speed in response to detecting violation of one or more of the hierarchical rules or detecting presence of anomalous equipment behavior. ### Claim 5 (depends on 4) The method according to claim 4, wherein: increasing the container fill line speed comprises increasing a current container fill line speed to achieve a throughput equivalent to a requested or maximum number of containers per minute processed by the container fill line; and decreasing the container fill line speed comprises reducing the current container fill line speed to achieve a throughput equivalent to a number of containers per minute ranging from zero to less than the requested or maximum number of containers per minute. ### Claim 6 (depends on 1) The method according to claim 1, wherein the one or more machine learning models define models trained using historical equipment speed data acquired during past operation of the equipment. ### Claim 7 (depends on 1) The method according to claim 1, wherein the set of hierarchical rules comprises a first set of rules directed to fill line process values that vary dynamically and a second set of rules directed to constant parameter values. ### Claim 8 (depends on 1) The method according to claim 1, wherein the Expert System integrates one or more new hierarchical rules into the set of hierarchical rules to provide for enhanced adjustment to the container fill line speed. ### Claim 9 (depends on 1) The method according to claim 1, comprising one or more of: writing alerts generated by the Expert System to a database and communicating the alerts from the database to a user interface in response to a user request or a system request; writing, by the Expert System to the database, events of predetermined relevance and information associated with the events; and writing, by the Expert System to the database, anomalies of predetermined relevance and information associated with the anomalies. ### Claim 10 (depends on 1) The method according to claim 1, comprising calculating, by the Expert System, statistics during operation of the container fill line and storing the statistics in a memory. ### Claim 11 (depends on 1) The method according to claim 1, wherein the material comprises liquid material or a semi-liquid material. ### Claim 12 (depends on 1) The method according to claim 1, wherein the material comprises a powder or an aerosolized material. ### Claim 13 An automated container fill line system, comprising: a container fill line configured to transport a plurality of containers at a container fill line speed, wherein the container fill line includes equipment comprising a filler, upstream equipment situated upstream of the filler, and downstream equipment situated downstream of the filler; a filler configured to dispense a material into each of the containers individually and sequentially; and an Expert System configured to control the container fill line speed using a set of hierarchical rules and one or more machine learning models, wherein the one or more machine learning models are associated with the upstream equipment and the downstream equipment. ### Claim 14 (depends on 13) The system according to claim 13, wherein the Expert System is configured to control the container fill line speed in response to at least: a capacity of the equipment of the container fill line to process containers; and a capacity of the filler of the container fill line to fill the containers while the containers are transported along the container fill line at the container fill line speed. ### Claim 15 (depends on 13) The system according to claim 13, wherein the Expert System is configured to detect whether the equipment is experiencing anomalous behavior using the one or more machine learning models. ### Claim 16 (depends on 13) The system according to claim 13, wherein the Expert System is configured to adjust the container fill line speed by: increasing the container fill line speed in response to detecting non-violation of the hierarchical rules and detecting an absence of anomalous equipment behavior; and decreasing the container fill line speed in response to detecting violation of one or more of the hierarchical rules or detecting presence of anomalous equipment behavior. ### Claim 17 (depends on 13) The system according to claim 13, wherein the Expert System is configured to: increase the container fill line speed by increasing a current container fill line speed to achieve a throughput equivalent to a requested or maximum number of containers per minute processed by the container fill line; and decrease the container fill line speed by reducing the current container fill line speed to achieve a throughput equivalent to a number of containers per minute ranging from zero to less than the requested or maximum number of containers per minute. ### Claim 18 (depends on 13) The system according to claim 13, wherein the one or more machine learning models define models trained using historical equipment speed data acquired during past operation of the equipment. ### Claim 19 (depends on 13) The system according to claim 13, wherein the set of hierarchical rules comprises a first set of rules directed to fill line process values that vary dynamically and a second set of rules directed to constant parameter values. ### Claim 20 (depends on 13) The system according to claim 13 wherein the Expert System is configured to integrate one or more new hierarchical rules into the set of hierarchical rules to provide for enhanced adjustment to the container fill line speed. ### Claim 21 (depends on 13) The system according to claim 13, wherein the Expert System is operably coupled to a database and configured to one or more of: write alerts to the database and communicate the alerts from the database to a user interface in response to a user request or a system request; write events of predetermined relevance and information associated with the events to the database; and write anomalies of predetermined relevance and information associated with the anomalies to the database. ### Claim 22 (depends on 13) The system according to claim 13, wherein the Expert System is configured to calculate statistics during operation of the container fill line and store the statistics in a memory. ### Claim 23 (depends on 13) The system according to claim 13, wherein the material comprises a liquid material or a semi-liquid material. ### Claim 24 (depends on 13) The system according to claim 13, wherein the material comprises a powder or an aerosolized material. ### Claim 25 A non-transient computer readable medium containing program instructions for causing a computer-implemented Expert System to: control transporting of a plurality of containers along a container fill line at a container fill line speed, wherein the container fill line includes equipment comprising a filler, upstream equipment situated upstream of the filler, and downstream equipment situated downstream of the filler; control dispensing of a material, by the filler, into each of the containers individually and sequentially; and control the container fill line speed using a set of hierarchical rules and one or more machine learning models, wherein the one or more machine learning models are associated with the upstream equipment and the downstream equipment. ### Claim 26 (depends on 25) The non-transient computer readable medium according to claim 25, wherein the Expert System controls the container fill line speed in response to at least: a capacity of the equipment of the container fill line to process unfilled containers; and a capacity of the filler of the container fill line to fill the containers while the containers are transported along the container fill line at the container fill line speed. ### Claim 27 (depends on 25) The non-transient computer readable medium according to claim 25, wherein the instructions cause the Expert System to detect whether the equipment is experiencing anomalous behavior using the one or more machine learning models. ### Claim 28 (depends on 25) The non-transient computer readable medium according to claim 25, wherein the instructions cause the Expert System to adjust the container fill line speed at least in part by: increasing the container fill line speed in response to detecting non-violation of the hierarchical rules and detecting an absence of anomalous equipment behavior; and decreasing the container fill line speed in response to detecting violation of one or more of the hierarchical rules or detecting presence of anomalous equipment behavior. ### Claim 29 (depends on 25) The non-transient computer readable medium according to claim 25, wherein the instructions cause the Expert System to: increase the container fill line speed by increasing a current container fill line speed to achieve a throughput equivalent to a requested or maximum number of containers per minute processed by the container fill line; and decrease the container fill line speed by reducing the current container fill line speed to achieve a throughput equivalent to a number of containers per minute ranging from zero to less than the requested or maximum number of containers per minute.