# US 2022/0036293 A1 · Cash level optimization with autocalibration

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- Number: US 2022/0036293 A1
- Document ID: US20220036293A1
- Status: Published application
- Kind: A1
- Application number: 17/300,514
- Filed: 2021-07-27
- Published: 2022-02-03
- Earliest claimed: 2020-07-28
- Assignees: PlanFocus Software GmbH
- Applicants: PlanFocus Software GmbH
- Inventors: Joachim Paul Walser; Clemens Sickinger; Karsten Bohlen
- Web page: https://patentgrep.com/patent/US20220036293A1

## CPC

- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G07F 19/206: {Software aspects at ATMs}
- G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 10/0877: {by inventory control or reporting using inventory tracking or counting}
- G06Q 20/1085: {involving automatic teller machines [ATMs]}
- G07F 19/209: {Monitoring, auditing or diagnose of functioning of ATMs}
- 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
- G07F: COIN-FREED OR LIKE APPARATUS (coin sorting G07D3/00; coin testing G07D5/00; {handling coins or paper currencies apart from payment activated apparatus G07D; payment architectures, schemes or protocols G06Q20/00})
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

The disclosure concerns a computer-implemented method of optimizing the cash level of a cash device (20), wherein the cash level indicates an amount of currency in at least one cash box of the cash device (20), wherein the method comprises the following steps: iteratively optimizing, in an automated manner, a calibration (26) of the cash device (20), wherein the calibration (26) comprises a plurality of parameters; and generating a plan (29) based at least in part on the calibration (26), wherein the plan (29) indicates when and/or how the cash device (20) is to be serviced.

## Claims

### Claim 1

A system for optimizing efficiency of cash flow, comprising:
at least one computing device comprising at least one storage device, at least one memory device, at least one network connection, and at least one user interface device;
at least one network connected computing device comprising at least one network connected storage device, at least one network connected memory device, at least one network connected network connection;
wherein the at least one computing device and the at least one network connected computing device are connected via a computer network;
at least one cash point device connected to the computer network, and the at least one cash point device configured to provide at least one current cash levels, and at least one set of historical data to the at least one network connected computing device;
wherein the at least one network connected computing device aggregates the at least current cash levels and the at least one set of historical data, and creates a set of aggregated current status indicators and a set of aggregated historical data;
wherein the at least one computing device can access the aggregated current cash levels, the set of aggregated historical data, and the at least one computing device is configured to utilize a forecaster engine;
an autocalibrator engine and
an evaluator engine to evaluate the quality of a calibration data set;
wherein the forecaster engine analyzes the aggregated current status indicators and the set of aggregated historical data to generate a forecast dataset for each of the at least one cash point device;
wherein the autocalibrator engine analyzes the aggregated current cash levels, the forecast dataset and the set of aggregated historical data to repeatedly generate candidate calibration datasets;
wherein, as part of a feedback loop, the evaluator engine analyzes the aggregated current cash levels, the set of aggregated historical data, the forecast dataset, and candidate calibration datasets to generate an evaluation dataset that contains the relative quality of the calibration datasets according to a pre-defined evaluation measure;
wherein the evaluation dataset is further processed by the autocalibrator engine to determine how the evaluation dataset will lead to a modification of the calibration datasets; and
wherein, as part of a feedback loop, if changes need to be made, the autocalibrator engine eliminates calibration datasets of low quality and sends modified candidate calibration datasets to the evaluator engine;
wherein, as the termination of a feedback loop, based on a termination criterium of the autocalibrator engine, the resulting optimized calibration dataset is further processed by an optimizer engine on at least one computing device that in response generates a plan dataset that is transmitted to the at least one networked computing device for distribution via the computer network.

### Claim 2 (depends on 1)

The system of claim 1, wherein the feedback loop includes the evaluator engine feeding the autocalibrator engine that when reaching the termination of the feedback loop provides the optimzed calibration dataset to the optimizer for further processing.

### Claim 3 (depends on 1)

The system of claim 1, wherein the autocalibrator engine and the evaluator engine iteratively process datasets to generate the optimized calibration dataset.

### Claim 4 (depends on 1)

The system of claim 1, wherein the autocalibrator engine utilizes a genetic algorithm to generate the candidate calibration datasets.

### Claim 5 (depends on 1)

The system of claim 1, wherein the at least one cash point device receives the plan dataset by the computer network.

### Claim 6 (depends on 1)

The system of claim 1, wherein the autocalibrator engine utilizes a genetic algorithm that comprises a plurality of parameters as part of its analysis.

### Claim 7 (depends on 6)

The system of claim 6, wherein the plurality of parameters can be chosen from a set of cash denominations, a stock level threshold, a buffer factor, and a minimum balance.

### Claim 8 (depends on 7)

The system of claim 7, wherein the genetic algorithm performs mutation, combination, and selection operations based on the plurality of parameters to generate the candidate calibration datasets.

### Claim 9

A system for optimization comprising:
at least one cashpoint node that is in communication with at least one server node through a computer network;
wherein each or the at least one server node is operated by or on behalf of a banking institution;
at least one computing device node in communication with the at least one server node through the computer network;
wherein the at least one computing device node executes a forecasting engine configured to create a forecast dataset of a set of expected transactions with each of the at least one cashpoint nodes in communication with the at least one server node;
wherein the forecast engine utilizes historical and current data from the cashpoint node to generate the forecast dataset;
wherein, as part of a feedback loop, the at least one computing device node executes a calibration engine that, in part based on the forecast dataset, is configured to create a number of candidate calibration datasets;
wherein the at least one computing device node executes an evaluator engine configured to evaluate the quality of calibration datasets and provide an evaluation dataset;
wherein, as a finalization of a feedback loop, the at least one computing device node executes a calibration engine to create an optimized calibration dataset selected from the candidate calibration datasets based on the evaluation dataset of the candidate calibration datasets;
wherein the at least one computing device node executes an optimizer engine configured to use the optimized calibration dataset and generates a plan dataset for the at least one cashpoint node; and
wherein the at least one server node or the at least one cashpoint node can send a service order based on the plan dataset, and the order triggers a servicing of the at least one cashpoint node.

### Claim 10 (depends on 9)

The system of claim 9, wherein the feedback loop includes the evaluator engine feeding the calibration engine that when reaching the termination of the feedback loop provides the optimized calibration dataset to the optimizer for further processing.

### Claim 11 (depends on 9)

The system of claim 9, wherein the calibration engine and the evaluator engine iteratively process datasets to generate the optimized calibration dataset.

### Claim 12 (depends on 9)

The system of claim 9, wherein the calibration engine utilizes a genetic algorithm to generate the candidate calibration datasets.

### Claim 13 (depends on 9)

The system of claim 9, wherein the at least one cash point device receives the plan dataset by the computer network.

### Claim 14 (depends on 9)

The system of claim 9, wherein the autocalibrator engine utilizes a genetic algorithm that comprises a plurality of parameters as part of its analysis.

### Claim 15 (depends on 14)

The system of claim 14, wherein the plurality of parameters can be chosen from a set of cash denominations, a stock level threshold, a buffer factor, and a minimum balance.

### Claim 16 (depends on 15)

The system of claim 15, wherein the genetic algorithm performs mutation, combination, and selection operations based on the plurality of parameters to generate the candidate calibration datasets.

### Claim 17

A method of optimization of a calibration for a cash level optimizer device comprising:
receiving a dataset of cash level history, and current cash levels for a cash device with a computing device connected to the cash device via a computer network;
preparing a forecast dataset comprising set of expected transactions, based on the cash level history and current cash levels;
optimizing a calibration for the optimizer engine consisting of a set of variable parameters based on a prepared forecast dataset, and a set of historical transactions with the computing device;
generating a plan based on the optimized calibration, the current cash levels of the cash device and the forecast dataset, and providing the plan to a banking institution or cash-in-transit company to allow for optimal servicing of the cash device;
wherein the calibration optimization is performed using a domain specific metaheuristic search algorithm;
wherein the metaheuristic search algorithm iteratively creates datasets consisting of candidate calibrations whose parameters are restricted by one or more constraints received from a constraint system device;
wherein each dataset consisting of the candidate calibrations is evaluated using an evaluation engine which produces an evaluation dataset according to a pre-defined evaluation measure that determines the quality of each candidate calibration;
wherein the optimized calibration is determined as the calibration candidate with the best quality upon meeting a termination criterium, optionally storing one or more values of the parameters that are part of the calibration datasets in a neural network, preferably a feedforward neural network;
wherein the metaheuristic search algorithm takes into account plurality of parameters that are part of a calibration containing one or more of the following parameters, a stock level threshold of the cash device, a buffer factor of the cash device, a minimum balance for each of the denominations of the cash device, and cost parameters to service the cash device, and wherein the set of services costs are retrieved from a third party service by the computing device.

### Claim 18 (depends on 17)

The method of claim 17, wherein the evaluation engine returns the evaluation dataset to the meatheuristic search algorithm until the termination criterium is met.

### Claim 19 (depends on 17)

The method of claim 17, wherein the metaheuristic search algorithm is a genetic algorithm, which performs mutation, combination, and selection operations to the evaluation dataset to generate datasets consisting of candidate calibrations.

### Claim 20 (depends on 17)

The method of claim 17, wherein the termination criterium can have at least two thresholds that allow for the optimized calibration to be provided to the banking institution or cash-in-transit company when the first of the at least two thresholds is met while continuing to refine the optimization until the second or final threshold of the at least two thresholds is met.