Machine Learning Controller for Prize Dispensing Entertainment Machines

Abstract

A system to rapidly set up new claw machines for different styles of prizes, while also providing an excellent player experience through dynamically changing claw machine behavior through machine learning algorithms. These systems can be readily installed on most any crane machine by replacing the controller card. The system is configured by means of a control wand physically connected to the controller. Teaching the machine about the prize is accomplished by physically inserting a sample prize into the grabber, and executed a command via control wand to tell the machine to learn. Once configured and taught the prize, the machine is autonomous, and will maintain its programmed profit margin throughout the prizes dispensed by learning player behavior and making adjustments after each play.

Claims

1. A machine learning controller for prize dispensing entertainment, comprising: the operator teaches the machine about the prize to be dispensed; the grabber teach option is selected from the user interface; a toy is placed manually into the grabber, and teaching begins; the strength required to hold the toy is monitored by the machine; when the toy falls through the toy detector, machine stores how much force was recorded just before it dropped; and the machine teaching is complete; the process also determines the initial values of the machine learning algorithm, automatically populating the initial conditions for the control algorithm; and as the toys are dispensed from the machine, the machine will change its settings automatically to maintain the desired profit margin.

2. The machine learning controller of claim 1, wherein device operation is divided into sections, the initial setup, and the playing of the machine.

3. The machine learning controller of claim 2, wherein an operator first configures machine up for the toys to be dispensed; using options in the to tell the machine: prize cash value; cost per play of game; bonus options; and desired profit margin.

4. The machine learning controller of claim 1, wherein the grabber options can be automatically set.

5. The machine learning controller of claim 1, wherein manual configuration is completed by the manual configuration options.

6. The machine learning controller of claim 1, wherein The machine is now configured; The machine waits for the new game to start, typically by insertion of payment means; once payment is registered, a player uses their skill to position the grabber over the desired prize; once the grabber is in physical contact with the prizes, a grabber power control algorithm is executed,

7. The machine learning controller of claim 6, wherein grabber power control algorithm is executed according to the following steps: a. the grabber actuates with sufficient force to grab and hold prize; b. the grabber power ramps down by one power step; c. the algorithm waits a pseudorandom short interval; d. steps b and c repeat until the claw is over the prize dispensing means; e. the grabber is released; and f. a prize, if still in the grabber when opened, falls by the prize detector.

8. The machine learning controller of claim 7, wherein after the player's game is complete, the controller moves on to grabber learn; and this algorithm is the internal machine learning algorithm which varies the players experience via its output variable, power step.

9. The machine learning controller of claim 8, wherein the internal machine learning algorithm performs the following steps: increments total cash in the machine; increments total plays by the machine; if a prize was dispensed, adds the prize value to the total cash out counter; the profit cumulative machine profit margin is calculated; if the profit margin is greater than the desired profit margin, the power step is increased, making the machine harder to win, as the grabber power will decrease faster; if the profit margin is less than the desired profit margin, the power step is decreased, making the machine easier to win, as the grabber power will decrease slower; if the profit margin is near the desired, then power step is unchanged in the next play; the grabber power step is stored for the next play.

10. The machine learning controller of claim 9, wherein with each new game cycle, the machine uses the above data points to derive a new claw power profile for the next time the claw is used; the electrical control circuit for the grabber then uses that data to vary the amount of strength on the claw in a quasi-random continuous fashion during game play.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated herein an form a part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the invention.

[0016] FIG. 1 illustrates setup mode and mode operation.

[0017] FIG. 2 illustrates the grabber teach algorithm used to teach the machine about the prize that it dispenses.

[0018] FIG. 3 illustrates the grabber learn algorithm applied once through each game cycle, that allows the system to learn player behavior.

[0019] FIG. 4 illustrates the grabber power control algorithm, which uses the value derived from grabber learn to effect grabber behavior.

[0020] FIG. 5 illustrates the menu options relevant to the learn mode vs the manual configuration

[0021] FIG. 6 illustrates the cash menu options needed by the control algorithms to operate the machine learning algorithms.

DETAILED DESCRIPTION OF THE INVENTION

[0022] In the following detailed description of the invention of exemplary embodiments of the invention, reference is made to the accompanying drawings (where like numbers represent like elements), which form a part hereof, and in which is shown by way of illustration specific exemplary embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, but other embodiments may be utilized and logical, mechanical, electrical, and other changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims.

[0023] In the following description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it is understood that the invention may be practiced without these specific details. In other instances, well-known structures and techniques known to one of ordinary skill in the art have not been shown in detail in order not to obscure the invention. Referring to the figures, it is possible to see the various major elements constituting the apparatus of the present invention.

[0024] Device operation is divided into sections: the initial setup, and the playing of the machine As shown in FIG. 1, the operator first configures machine up for the toys to be dispensed 101, using options in the CASH OPTION MENU 102 FIG. 6 to tell the machine: Prize cash value; Cost per play of game; Bonus options and Desired profit margin.

[0025] Next, in FIG. 1, the operator teaches the machine about the prize to be dispensed. FIG. 6 illustrates the grabber power menu options, wherein the grabber options can be automatically set. Automatic setting is covered in FIG. 2, grabber teach, whereby: The grabber teach option is selected from the user interface. The toy is placed manually into the grabber, and teaching begins. The strength required to hold the toy is monitored by the machine. When the toy falls through the toy detector, machine stores how much force was recorded just before it dropped and the machine teaching is complete.

[0026] The process also determines the initial values of the machine learning algorithm, automatically populating the initial conditions for the control algorithm. Manual configuration is still possible via the manual configuration options of FIG. 5.

[0027] The machine is now configured. FIG. 1 waits for the new game to start 104, typically by insertion of payment means. Once payment is registered 105, the player uses their skill to position the grabber over the desired prize 106. Once the grabber is in physical contact with the prizes, the grabber power control algorithm 107 FIG. 4 is executed, whereby: The grabber opens 108 and then actuates with sufficient force to grab and hold prize; The grabber power ramps down by one power step; the algorithm waits a pseudorandom short interval; Steps 2 and 3 repeat until the claw is over the prize dispensing means; the grabber is released; and the prize, if still in the grabber when opened, falls by the prize detector 109.

[0028] Now referring to FIG. 2, the automatic learning process is taught. The process begins 201 and the learning mode is enabled 202. The grabber opens 203 and waits for the operator to place the price grabber over the playing field 204. If there is a prize in the grabber 205, the grabber is closed with 100% force. The grabber force is measured and decreased by one step in step 207. If a prize is triggered by the prize sensor 208, the grabber force is again decreased by one step in step 207. The grabber force is recorded when the claw is opened 209 and the process ends 210.

[0029] After the player's game is complete 302, the controller moves on to grabber learn process 301 presented in FIG. 3. This algorithm is the internal machine learning algorithm which varies the players experience via its output variable, power step, used in FIG. 4. This algorithm performs the following steps: Increments total cash in the machine 303. Increments total plays by the machine. If a prize was dispensed 304, adds the prize value to the total cash out counter 307. The profit cumulative machine profit margin is calculated 305. If the profit margin is greater than the desired profit margin 306, the power step is increased 308, making the machine harder to win, as the grabber power will decrease faster. If the profit margin is less than the desired profit margin 310, the power step is decreased 309, making the machine easier to win, as the grabber power will decrease slower. If the profit margin is near the desired, then power step is unchanged in the next play. The grabber power step is stored for the next play 311 and the process ends 312.

[0030] Now referring to FIG. 4, the internal machine learning algorithm which varies the players experience via its output variable, power step is shown. The process begins 4-1 with first determining if the grabber is in contact with prizes 402. The grabber power is initially set near its maximum 403. The grabber power is calculated by grabber power less the power step 404. If the claw is over the chute 405, the grabber power goes to zero 407 and the process ends. If the grabber is not over the chute 405, the method waits for a pseudorandom short time 406 before repeating the grabber power level determination and checking to see if the claw is over the chute.

[0031] FIG. 5 illustrates the grabber options menu 501, where the operating parameters of the grabber are either automatically set 502 or manually set 503. The grabber can be manually set to how strong the grabber is to grab a prize 504, how strong the grabber is to carry a prize 505, and how strong a grabber is to win a prize 506.

[0032] FIG. 6 illustrates the cash options menu 601, which contains the set prize cash value 6-2, set cost per play 603, set bonus options 604, and set desired profit margin 605.

[0033] With each new game cycle, the machine uses the above data points to derive a new claw power profile for the next time the claw is used. The electrical control circuit for the grabber then uses that data to vary the amount of strength on the claw in a quasi-random continuous fashion during game play. This provides a much more challenging and skill-intense player experience, while also providing the profit margin required by the game operators. The game operators do not know when the game machine will win, as the claw strength is controller and varied internally, and can be different on every play, based on the output data from the learning algorithm.

[0034] In addition, as the toys are dispensed from the machine, the machine will change its settings automatically to maintain the desired profit margin. This allows a consistent player experience when the machine is new, and full, as well as when it is nearly empty, providing an exciting customer experience.

[0035] Thus, it is appreciated that the optimum dimensional relationships for the parts of the invention, to include variation in size, materials, shape, form, function, and manner of operation, assembly and use, are deemed readily apparent and obvious to one of ordinary skill in the art, and all equivalent relationships to those illustrated in the drawings and described in the above description are intended to be encompassed by the present invention.

[0036] Furthermore, other areas of art may benefit from this method and adjustments to the design are anticipated. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents, rather than by the examples given.