Patent Publication Number: US-2016238435-A1

Title: Improvements in or relating to counting by weight counting systems

Description:
FIELD 
     This invention relates to a method to automatically adjust the maximum number of items (Slice Limit) to be applied to a counting by weight cash counting process at one time to ensure the maximum possible accumulated tolerance cannot result in an error and that optimum efficiency is maintained. 
     BACKGROUND 
     Within industries where cash handling processes occur, it is common practice to use cash counting equipment to monitor cash flow. One method of doing this is by the use of counting by weight cash counting device(s) to count banknotes, coins and other cash representative items such as tokens or vouchers. Such device(s) comprise an extremely accurate, micro-controller based electronic scale, designed specifically for the purpose of counting cash, controlled by software designed to resolve the measured weight for each denomination being processed into a number of items of that denomination. The software process used to implement this technology in ideal conditions simply consists of weighing an unknown number of identical items, then dividing the weight obtained by the weight of a single item to determine the number of items weighed. 
     In practice any item that is produced repetitively (such as banknotes) will have a weight tolerance, small variations between one produced item and the next, that will affect the maximum number of items that may be counted without the risk of a counting error caused by these tolerances accumulating. In some situations this error may be ignored e.g. if very low value items are being counted an error of 1 or 2 pieces may be negligible and considered acceptable, or it may be impossible to ever experience this error e.g. the number of pieces required, if each item weighs the minimum within its tolerance range, for the total weight to indicate an error of −1 piece produces a total weight beyond the range of the weighing device. However, in the situation where low weight, high value objects (such as banknotes) are counted by weight, it is necessary to apply a slice limit—a maximum number of items that can be applied to the weighing device (such as a counting by weight cash counting device) at one time to ensure the maximum possible accumulated tolerance cannot result in an error. The use of slice limits in conjunction with other methods such as tapered weight acceptance bands and note weight learning allow for extremely accurate counting of banknotes using counting by weight cash counting device(s). 
     A problem exists in the case of counting banknotes by weight when there is a weight variation caused by differing note issues being in circulation simultaneously or circulated notes physical condition causes variance between notes i.e. brand new versus used, the note weight learning algorithm will adjust its expected weight of a single banknote according to the average weight of the notes being counted but the weight range of the notes in circulation from the lowest weight of the lightest note to the highest weight of the heaviest note will not be recognised. In order to continue counting accurately and efficiently, the slice limit must be adjusted according to the weight range of the banknotes being processed. As slice limits are currently normally determined by careful and accurate manual weighing of many samples of a denomination to build up a statistical profile of note weight variations, the task of determining a new slice limit takes considerable time and is only relevant to that point in time. Furthermore, this determination is normally triggered by experiencing a higher than acceptable number of count errors but, not necessarily by an improving weight range that would allow for a larger and more efficient slice limit. The slice limit should in fact change over time to maintain accuracy and efficiency as any series notes are added to or removed from circulation causing a change in the data set. 
     The present invention aims to address these problems by providing an automatic and continuous method of determining and applying the slice limit for optimum accuracy and efficiency of counting. 
     According to a first aspect of the present invention there is provided a method of adjusting the slice limit of a counting by weight counting system, the method comprising the steps of:
         providing said system with one or more properties of an item to be counted;   adding one or more of said items to said counting by weight counting system;   measuring the weight of the total number of said items added to said counting by weight counting system;   determining the total number of said items added to said counting by weight counting system, said determination being based on at least one predetermined weight of one of said items;   determining the average weight of said items added to said counting by weight counting system;   determining the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system;   determining the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system; and   determining a new slice limit of the counting by weight counting system, said determination being based on the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system, and the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system.       

     In the present invention the term “slice limit” is considered to mean the maximum number of items that may be applied to a counting by weight counting system at one time to ensure the maximum possible accumulated tolerance cannot result in an error. 
     The counting by weight counting system may be a counting by weight cash counting system. 
     The items being counted may be any item that is produced repetitively. The items being counted may be cash, i.e. bank notes, bank coins or any other cash representative items such as tokens or vouchers. 
     The one or more properties of the items that may be provided to the counting by weight counting system may be an item identifier, item value, item monetary value, code, item currency, or the like. If the item is cash the denomination of the bank note or bank coin may be provided to the counting by weight counting system. 
     The method may comprise the additional step of removing one or more items from the counting by weight counting system before the total number of said items is determined if the total measured weight of the items exceeds the current slice limit. 
     The method may comprise the additional step of removing one or more items from the counting by weight counting system before the total number of said items is determined if the total measured weight is out with a predetermined tolerance band for the determined total number of items. 
     The method may comprise the additional step of displaying the total number of items added to the counting by weight counting system. 
     The method may comprise the additional step of recording or storing the total number of items added to the counting by weight counting system. The method may comprise the additional step of recording or storing the measured weight of the number of items added to the counting by weight counting system. 
     The determination of the total number of said items added to said counting by weight counting system may be based on an average predetermined weight of one of said items. 
     The determination of the total number of said items added to said counting by weight counting system may be based on a plurality of predetermined weights of one of said items. The determination of the total number of said items added to said counting by weight counting system may be based on an average of a plurality of predetermined weights of one of said items. 
     Said plurality of predetermined weights of one of said items may be representative of the weight of one of said items over a period of time. Said plurality of predetermined weights of one of said items may be representative of the average weight of one of said items over a period of time. 
     Said plurality of predetermined weights of one of said items may be representative of the historical weight of one of said items. Said plurality of predetermined weights of one of said items may be representative of the historical average weight of one of said items. 
     Said plurality of predetermined weights may be stored in the counting by weight counting system. Said plurality of predetermined weights may be stored on computer readable media in the counting by weight counting system. 
     Said plurality of predetermined weights may be stored in a buffer. The buffer may be a circular buffer. The buffer may be readable by a computer. 
     The method may comprise the additional step of adding the determined average weight of said items added to said counting by weight counting system to the plurality of predetermined weights of one of said items. The method may comprise the additional step of adding the determined average weight of said items added to said counting by weight counting system to the stored plurality of predetermined weights of one of said items. The method may comprise the additional step of adding the determined average weight of said items added to said counting by weight counting system to the circular buffer of stored predetermined weights of one of said items. 
     The step of determining the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system may use the formula: 
     
       
         
           
             μ 
             = 
             
               
                 1 
                 N 
               
                
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   W 
                   i 
                 
               
             
           
         
       
     
     where:
         μ=the average weight of said items added to the counting by weight counting system (the current (calculated) expected note weight (mean of item learned weights in circular buffer  1  ( 21 )));   W i =the at least one predetermined weight of one of said items (note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 )));   N=the number of predetermined weights of one of said items (the number of previous measurements over which the average is calculated (number of records in circular buffer  1  ( 21 ))); and   i=index of previous measurements.       

     The step of determining the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system may use the formula: 
     
       
         
           
             
               σ 
               W 
             
             = 
             
               
                 
                   1 
                   N 
                 
                  
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                     
                       ( 
                       
                         
                           W 
                           i 
                         
                         - 
                         μ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         σ W =the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system (the current (calculated) standard deviation based on item learned weights in circular buffer  1  ( 21 ));   W i =the at least one predetermined weight of one of said items (note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 )));   N=the number of predetermined weights of one of said items (the number of previous measurements (number of records in circular buffer  1  ( 21 )));   i=index of previous measurements; and   μ=the average weight of said items added to the counting by weight counting system (the current expected note weight (the result from Calculation at step  2 )))       

     The step of determining the new slice limit of the counting by weight counting system may additionally include a sensitivity factor. The sensitivity factor may be selected based upon the relationship between the determined standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system and simulated standard deviations of predetermined weights and average weights of items added to simulated counting by weight counting systems. 
     The step of determining the new slice limit of the counting by weight counting system may use the formula: 
     
       
         
           
             
               SL 
               n 
             
             = 
             
               1 
               + 
               
                 INT 
                  
                 
                   ( 
                   
                     
                       SL 
                       o 
                     
                     · 
                     
                        
                       
                         
                           - 
                           k 
                         
                         · 
                         
                           
                             σ 
                             W 
                           
                           μ 
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     where:
         SL n =the determined new slice limit (the calculated new slice limit);   SL o =the current slice limit (the default slice limit (from register SL o  ( 110 )));   σ W =the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system (the current standard deviation (the result from Calculation at step  3 )));   μ=the average weight of said items added to the counting by weight counting system (the current expected note weight (the result from Calculation at step  2 ))); and   k=sensitivity factor.       

     The sensitivity factor may be 50. The sensitivity factor may be between 0 and 100. 
     The counting by weight counting system may be configured to operate with a plurality of different items. The plurality of different items may be different denominations of bank notes, bank coins etc. 
     The determined weights, mean weights, standard deviations of mean weights and slice limits may be stored for each type of item counted by the counting by weight counting system. 
     The determined weights, mean weights, standard deviations of mean weights and slice limits may be stored on computer readable media in the counting by weight counting system. 
     According to a second aspect of the present invention there is provided a counting by weight counting apparatus comprising:
         a property input module capable of providing said apparatus with one or more properties of an item to be counted;   a weight measuring module capable of measuring the weight of a total number of said items added to said counting by weight counting apparatus;   a total item number determination module capable of determining the total number of said items added to said counting by weight counting apparatus, said determination being based on at least one predetermined weight of one of said items;   an average weight determination module capable of determining the average weight of said items added to said counting by weight counting apparatus;   a mean value determination module capable of determining the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting apparatus;   a standard deviation module capable of determining the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting apparatus; and   a slice limit determination module capable of determining a new slice limit of the counting by weight counting apparatus, said determination being based on the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting apparatus, and the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting apparatus.       

     According to a third aspect of the present invention there is provided a computer program product encoded with instructions that when run on a computer of a counting by weight counting apparatus, cause the counting by weight counting apparatus to receive input data and information and perform a method of adjusting the slice limit thereof, the method comprising the steps of:
         providing said apparatus with one or more properties of an item to be counted;   measuring the weight of the total number of said items added to said counting by weight counting system;   determining the total number of said items added to said counting by weight counting system, said determination being based on at least one predetermined weight of one of said items;   determining the average weight of said items added to said counting by weight counting system;   determining the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system;   determining the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system; and   determining a new slice limit of the counting by weight counting system, said determination being based on the mean value of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system, and the standard deviation of said at least one predetermined weight of one of said items and said average weight of said items added to said counting by weight counting system.       

     The computer program product may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. The instructions or code associated with a computer-readable medium of the computer program product may be executed by a computer, e.g., by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, ASICs, FPGAs, or other equivalent integrated or discrete logic circuitry. 
     According to further aspects of the invention there is provided a counting by weight cash counting process ( 200 ) for resolving the measured weight derived from a counting by weight cash counting device, for each denomination being processed, into a number of items of that denomination, said resolution being susceptible to changes in the weight tolerance range within the total population of the type of items to be counted, the process comprising the stages:
         selecting the currency and denomination of items to be counted;   calculating the number of items from the applied weight;   checking if the calculated number of items is within the current slice limit for the currency and denomination being processed. If calculated number of items is greater than the slice limit, prompting operator to remove some items from the counting by weight cash counting device;   checking if the measured weight is within the tolerance bands for the calculated number of items. If measured weight is outside the tolerance bands, prompting operator to remove some items from the counting by weight cash counting device;   display the calculated number of items;   record the calculated number of items and applied weight;   check if item weight learning is enabled. If enabled, calculate and update item learned weight for processed currency and denomination; and   if item weight learning is enabled, check if automatic slice limit adjustment ( 100 ) is also enabled. If both item weight learning and automatic slice limit adjustment ( 100 ) are enabled, calculate and update slice limit for processed currency and denomination.       

     The method for automatic slice limit adjustment ( 100 ), the method comprising the steps:
         step  1  ( 20 )—storing calculated item learned weight to circular buffer  1  ( 21 );   step  2  ( 30 )—calculating the mean of learned weights in circular buffer  1  ( 21 );   step  3  ( 40 )—calculating standard deviation from learned weights in circular buffer  1  ( 21 );   step  4  ( 50 )—calculating new slice limit for current currency and denomination; and   step  5  ( 70 )—storing new slice limit to register SL n  ( 120 ).       

     Steps  2 ,  3  and  4  of the method for automatic slice limit adjustment ( 100 ) comprise a slice limit determination algorithm ( 60 ) for determination of adjustment required to slice limit for the relevant currency and denomination. 
     The slice limit determination algorithm ( 60 ) comprises the steps of:
         calculating the mean of learned weights in circular buffer  1  ( 21 ) using the formula:       

     
       
         
           
             μ 
             = 
             
               
                 1 
                 N 
               
                
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   W 
                   i 
                 
               
             
           
         
       
     
     where:
         μ=the current (calculated) expected note weight (mean of item learned weights in circular buffer  1  ( 21 ))   W i =note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 ))   N=the number of previous measurements over which the average is calculated (number of records in circular buffer  1  ( 21 ))   i=index of previous measurements;   calculating standard deviation from learned weights in circular buffer  1  ( 21 ) using the formula:       

     
       
         
           
             
               σ 
               W 
             
             = 
             
               
                 
                   1 
                   N 
                 
                  
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                     
                       ( 
                       
                         
                           W 
                           i 
                         
                         - 
                         μ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         σ W =the current (calculated) standard deviation based on item learned weights in circular buffer  1  ( 21 )   W i =note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 ))   N=the number of previous measurements (number of records in circular buffer  1  ( 21 ))   i=index of previous measurements   μ=the current expected note weight (the result from calculation ( 30 ) of mean of learned weights in circular buffer  1  ( 21 )); and   calculating new slice limit for current currency and denomination using the formula:       

     
       
         
           
             
               SL 
               n 
             
             = 
             
               1 
               + 
               
                 INT 
                  
                 
                   ( 
                   
                     
                       SL 
                       o 
                     
                     · 
                     
                        
                       
                         
                           - 
                           k 
                         
                         · 
                         
                           
                             σ 
                             W 
                           
                           μ 
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     where:
         SL n =the calculated new slice limit   SL o =the default slice limit (from register SL o  ( 110 ))   σ W =the current standard deviation (the result from the calculation ( 40 ) of standard deviation from learned weights in circular buffer  1  ( 21 )   μ=the current expected note weight (the result from the calculation ( 30 ) of mean of learned weights in circular buffer  1  ( 21 ))   k=sensitivity factor.       

     The circular buffer  1  ( 21 ) possesses a capacity to store a pre-set number of records where all records contained in the circular buffer  1  ( 21 ) are included in the calculation ( 30 ) of the mean of learned weights in circular buffer  1  ( 21 ) and in the calculation ( 40 ) of standard deviation of learned weights in circular buffer  1  ( 21 ). 
     In an alternative configuration the capacity of circular buffer  1  ( 21 ) to store records may be variable and automatically adjustable based on, for instance, the rate of change of learned weight values, such variability enabling the method for automatic slice limit adjustment ( 100 ) to respond more quickly to significant changes in the weight tolerance range within the total population of the type of items to be counted. 
     In another alternative configuration the circular buffer  1  ( 21 ) may possess a capacity to store a pre-set number of records but the number of most recent records included in the calculation ( 30 ) of the mean of learned weights in circular buffer  1  ( 21 ) and in the calculation ( 40 ) of standard deviation of learned weights in circular buffer  1  ( 21 ) may be variable and automatically adjustable based on, for instance, the rate of change of learned weight values, such variability enabling the method for automatic slice limit adjustment ( 100 ) to respond more quickly to significant changes in the weight tolerance range within the total population of the type of items to be counted. 
     Aspects and embodiments of the invention are set out in the accompanying claims. 
     In general terms, one embodiment of a first aspect of the invention can provide a method to automatically and continuously adjust the slice limit utilising an automatic slice limit determination algorithm. The calculations involved in the slice limit adjustment method requires data from counting operations to be recorded and historical data to be analysed in order to determine the statistical profile of banknotes being weighed and how this profile is changing over time. 
     This method is not expected to eliminate all possible errors that may occur with the introduction of a new series of banknotes: As this method will be based on a learning algorithm, time will be required for adjustments to take place over a number of counts before changes to the slice limit become effective—if a large number of new series notes are counted on a weighing device before any learning has taken place, an error will still be possible, but it is expected that situations where this method is used will demonstrate considerable improvement over situations where it is not used. 
     Similarly, this method is not expected to continuously achieve maximum slice limit and hence, maximum efficiency: As this method will be based on a learning algorithm, time will be required for adjustments to take place over a number of counts before changes to the slice limit become effective, but it is expected that changes will occur much more rapidly than is presently the situation as an improving denomination weight range and reducing frequency of count errors is currently unlikely to prompt a new manual determination of slice limit. 
     This method also allows the system, rather than an individual, to determine the slice limit. This means that human error can be avoided, and appropriate calibration of the device can be undertaken efficiently by the system itself, rather than manually. 
    
    
     
       BRIEF DESCRIPTION OF FIGURES 
       An embodiment of the invention will now be described, by way of example only, with reference to the following figures, in which:— 
         FIG. 1 —shows a flow diagram illustrating a typical counting by weight cash counting process for a single denomination; 
         FIG. 2 —shows a flow diagram illustrating a method of adjusting the slice limit of a counting by weight counting system according to the present invention; 
         FIG. 3 —shows a flow diagram illustrating a part of the automatic slice adjustment method; 
         FIG. 4 —shows a chart illustrating typical outputs from P(x) and x [data set, 1000 points] formulae; 
         FIG. 5 —shows a chart illustrating a distribution of old and new series banknotes combined; 
         FIG. 6 —shows a chart illustrating the equivalent Gaussian distribution P′(x); 
         FIG. 7 —shows a chart illustrating calculated values of σ and σW simulated with fixed slice limit 
         FIG. 8 —shows a chart illustrating simulation results—automatic slice limit adjustment turned off; and 
         FIG. 9 —shows a chart illustrating simulation results—automatic slice limit adjustment turned on. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION 
     Embodiments of the current invention propose an automatic slice limit adjustment method ( 100 ) wherein the method is an integral part of the counting by weight cash counting process ( 200 ) as employed in counting by weight cash counting device(s). This allows a device(s) to monitor counting operation results, apply the slice limit determination algorithm and automatically adjust the slice limit as required for optimum count accuracy and efficiency. It allows a device(s) to adjust the slice limit after each counting operation as compared to the intermittent, manual determination and application of slice limit adjustments that occur currently. 
     The following describes one embodiment of the automatic slice limit adjustment method ( 100 ) by description and example: 
     Attention is first drawn to  FIG. 1 , showing one embodiment of a counting by weight cash counting process as currently used.  FIG. 2 , shows one embodiment of a counting by weight cash counting process including an automatic slice adjustment method ( 100 ) according to the present invention. 
     Automatic Slice Limit Determination Algorithm Simulation: 
     Attention is now drawn to  FIG. 4 , showing typical outputs from P(x) and x [data set, 1000 points] formulae.  FIG. 5  shows Distribution of old and new series banknotes combined.  FIG. 6  shows Equivalent Gaussian distribution P′(x).  FIG. 7  shows calculated values of σ and σW simulated with fixed slice limit.  FIG. 8  shows simulation results—automatic slice limit adjustment turned off.  FIG. 9  shows simulation results—automatic slice limit adjustment turned on. 
     Relation Between Tolerance and Standard Deviation: 
     In order to accurately model the effect of adjusting the slice limit when counting banknotes by weight, it is primarily important to define how the tolerances of these notes are interpreted. If the weight and tolerance for a denomination of banknote is stated as X.XXXXg±Y.YYYYg, it is easy to infer that note weights will be evenly and exclusively distributed across this range. This is not the case however as can be understood from the following rationale: 
     When banknotes are produced, the tolerance will be very tight, producing almost identically sized sheets (within a fraction of a millimeter), each having the same additional features applied (holograms, security thread, photo luminescent ink, varnish etc.). Through the banknotes life, the weight will change through: wear &amp; tear of the varnish breaking and small tears in the paper; oil from human skin and atmospheric moisture absorbed into the note; objects stuck to the surface of the note either intentionally (adhesive tape etc.) or unintentionally. This effectively widens the tolerance of circulated notes, with most remaining close to their original weight, but some varying in weight by a considerable amount. 
     The result is a statistical distribution in the weight of circulated banknotes closely resembling a Gaussian distribution. There will be small changes because of the nature of these items—a Gaussian distribution would yield a positive probability (albeit very small) that a banknote could have a weight of 50% its original weight when produced (a note that had lost half its mass would have been removed from circulation). Similar extreme cases would also exist in the positive direction. However, as the tolerances of bank notes are usually no more than ±5%, these errors will be negligible, with a Gaussian distribution model producing results slightly worse than will be observed in practice. 
     For the purposes of modeling the statistical distribution, it will be assumed that the tolerance limits for a denomination of banknotes correspond to three standard deviations from the mean value (T=±3σ). The probability density function will be described by: 
     
       
         
           
             
               P 
                
               
                 ( 
                 x 
                 ) 
               
             
             = 
             
               
                 1 
                 
                   σ 
                    
                   
                     
                       2 
                        
                       π 
                     
                   
                 
               
                
               
                  
                 
                   
                     - 
                     
                       1 
                       2 
                     
                   
                    
                   
                     
                       ( 
                       
                         
                           x 
                           - 
                           μ 
                         
                         σ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         x=weight (independent variable)   P(x)=probability density function (dependent variable)   σ=standard deviation (property of data set)   μ=mean (property of data set)       

     When a data set (of weights) is required to be modeled, each data point will be calculated using two random numbers (between 0 and 1) A and B (random numbers re-generated for each new point), with the function: 
         x =μ+(√{square root over (− 2 ·ln( A ))})·(2·cos(2·π· B ))·σ
 
     This is a standard formula used to calculate Gaussian distributed data points for the purpose of performing Monte Carlo simulations. Typical outputs from these formulae, are shown in  FIG. 4  for data set of 1000 points, σ=0.005, μ=1.000. 
     Typical Scenario: Introduction of a New Series of Banknotes 
     The real life problem described now of the case of circulated banknotes when a new series is released, used as an example will also apply to cases where changes in physical condition occur to some banknotes in circulation. In this case the probability density function when all banknotes in circulation is considered will be the superposition of two Gaussian distributions (weighted according to the ratio of old to new banknotes in circulation), each with a different mean and standard deviation. It is most likely that the standard deviation of the new series banknotes will be smaller than the standard deviation of the old series of banknotes as these will not have been subjected to the factors mentioned earlier that would cause weight fluctuations to the same degree as the old series. The probability density function of the old series banknotes and new series banknotes will be described by: 
     
       
         
           
             
               
                 
                   P 
                   1 
                 
                  
                 
                   ( 
                   x 
                   ) 
                 
               
               = 
               
                 
                   1 
                   
                     
                       σ 
                       1 
                     
                      
                     
                       
                         2 
                          
                         π 
                       
                     
                   
                 
                  
                 
                    
                   
                     
                       - 
                       
                         1 
                         2 
                       
                     
                      
                     
                       
                         ( 
                         
                           
                             x 
                             - 
                             
                               μ 
                               1 
                             
                           
                           
                             σ 
                             1 
                           
                         
                         ) 
                       
                       2 
                     
                   
                 
               
             
             , 
             
               
 
             
              
             
               
                 
                   P 
                   2 
                 
                  
                 
                   ( 
                   x 
                   ) 
                 
               
               = 
               
                 
                   1 
                   
                     
                       σ 
                       2 
                     
                      
                     
                       
                         2 
                          
                         π 
                       
                     
                   
                 
                  
                 
                    
                   
                     
                       - 
                       
                         1 
                         2 
                       
                     
                      
                     
                       
                         ( 
                         
                           
                             x 
                             - 
                             
                               μ 
                               2 
                             
                           
                           
                             σ 
                             2 
                           
                         
                         ) 
                       
                       2 
                     
                   
                 
               
             
           
         
       
     
     where:
         x=weight (independent variable)   P1(x)=probability density function old series banknotes (dependent variable)   P2(x)=probability density function new series banknotes (dependent variable)   σ 1 =standard deviation old series banknotes (property of data set) σ 2 =standard deviation new series banknotes (property of data set)   μ 1 =mean old series banknotes (property of data set)   μ 2 =mean new series banknotes (property of data set)       

     When combining these functions it is necessary to satisfy the requirement of any probability density function: 
     
       
         
           
             
               
                 ∫ 
                 
                   - 
                   ∞ 
                 
                 ∞ 
               
                
               
                 
                   P 
                    
                   
                     ( 
                     x 
                     ) 
                   
                 
                  
                 
                     
                 
                  
                 
                    
                   x 
                 
               
             
             = 
             1 
           
         
       
     
     This is achieved by weighting each of the individual notes probability density functions by a constant before adding them to obtain the overall function: 
         P ( x )= k   1   ·P   1 ( x )+ k   2   ·P   2 ( X ) 
     where:
         x=weight (independent variable)   P(x)=probability density function all notes in circulation (dependent variable)   P 1 (x)=probability density function old series banknotes (dependent variable)   P 2 (x)=probability density function new series banknotes (dependent variable)   k 1 =ratio of old series banknotes to total banknotes in circulation (constant)   k 2 =ratio of new series banknotes to total banknotes in circulation (constant)       

     The properties of k 1  and k 2  must be such that k 1 +k 2 =1 and k 1 :k 2 =ratio of old series banknotes in circulation: new series banknotes in circulation. 
     When generating a data set for the purpose of performing Monte Carlo simulations, the same function is used as before, now modified to calculate data points for either old series data set or new series data set: 
         x   1 =μ 1 +(√{square root over (−2·ln( A ))})·(2 cos(2·π· B ))·σ 1  
 
         x   2 =μ 2 +(√{square root over (−2·ln( A ))})·(2 cos(2·π· B ))·σ 2  
 
     where:
         x 1 =data point calculated for old series banknotes (data set member)   x 2 =data point calculated for new series banknotes (data set member)       

     A complete data set of n points should consist of k 1 ·n points calculated using x 1  and k 2 ·n points calculated using x 2 . 
     A typical distribution of P(x)=k 1 ·P 1 (x)+k 2 ·P 2 (x) and data set of 1000 points for σ 1 =0.005, μ 1 =1.000, k 1 =0.6, σ 2 =0.002, μ 2 =1.010, k 2 =0.4 is shown in  FIG. 5 . 
     Simplification of Double Peak Back to Gaussian Distribution: 
     In order to obtain tolerance limits from the double peak distribution that is found when two series of banknotes are in circulation simultaneously, it is necessary to simplify the double peak to a Gaussian distribution that will be representative of the data set containing both types of notes. A mean for this distribution can be found independently of the standard deviations of the constituent distributions: 
       μ=( k   1 ·μ 1 )+( k   2 ·μ 2 )
 
     The overall standard deviation for the Gaussian distribution is affected by the two original standard deviations, the difference between the two original means and the ratio of the sizes of the two original data sets. This is equivalent to the standard deviations of two non-overlapping sub-populations, which can be calculated as: 
       σ=√{square root over ( k   1 ·σ 1   2   +k   2 ·σ 2   2   +k   1   ·k   2 ·(μ 1 −μ 2 ) 2 )}
 
     The resulting Gaussian distribution is shown on  FIG. 6  as P′(x), superimposed over the double peak distribution obtained in  FIG. 5 , using the same parameters. Note that the Monte Carlo simulation set has been re-calculated using new random numbers and will look slightly different to those shown in  FIG. 5 , but as the functions used to generate the data set are the same as before, the data set follows a similar distribution. 
     Returning to the original definition of T=±3σ, it is this calculation of a that should be used to determine the tolerance limits, and subsequently the slice limit for two series of a denomination of note in circulation at one time. 
     Practical Limitations: 
     The calculations defined so far may be used to run simulations of expected results or to analyse weights obtained from note sample, but where continuous adjustment is to be made to the weighing device as it is being used, much of the required data will not be known. In the case of k 1  and k 2 , the ratio of old series notes to new series notes in circulation will not be known to the weighing device and cannot be determined from recorded data—the individual tolerances of the two series of notes may overlap, making it impossible to determine from which series some individual notes have come from based on their weight. The standard deviation, σ 1 , and the mean value, μ 1 , of the old series of notes may be estimated from historical data if it has been recorded, but standard deviation, σ 2 , and the mean value, μ 2 , of the new series of notes may never be determined as they will be mixed with old series notes when they are first presented to the weighing device. 
     Consideration must be given to what information is available to the weighing device and how it can be used to determine the changes that should be made to the slice limit. Historical data is information that may be available if internal storage (non-volatile memory) is used to record any measurements that are made. For each measurement that is made, it will be possible to record the average weight of a note within the measured sample and the number of notes that were measured in that sample. 
     The important piece of information that is required is the standard deviation of the notes that are currently in circulation, or a close estimate. It is already possible to continuously update the expected weight of a single banknote using the note weight learning algorithm, which could use a running average of the last N measurements: 
     
       
         
           
             μ 
             = 
             
               
                 1 
                 N 
               
                
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   W 
                   i 
                 
               
             
           
         
       
     
     where:
         μ=the current (calculated) expected note weight (running average)   W i =note weights obtained from previous measurements   N=the number of previous measurements over which the average is calculated   i=index of previous measurements       

     Using a similar process, it will be possible to determine a continuously updated estimate of the current standard deviation. The ideal information for calculating standard deviation is the weight of each individual note and the total number of notes weighed, according to the standard deviation formula: 
     
       
         
           
             σ 
             = 
             
               
                 
                   1 
                   N 
                 
                  
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                       
                   
                    
                   
                     
                       ( 
                       
                         
                           x 
                           i 
                         
                         - 
                         μ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         σ=standard deviation   x i =individual note weights   N=the number of individual notes   i=index of individual notes   μ=the current expected note weight       

     However, the weight of each individual note (x i ) is not known from the historical measurement data. The weights obtained from previous measurements (W i ), that are used to calculate the average note weight are in fact themselves averages over the number of notes weighted for each measurement. This does not matter too much when calculating an overall average—the only effect this will have is measurements that were made with a smaller number of notes will contribute more to the calculation than measurements that were made with a larger number of notes. It would be possible to obtain an exact average by applying a weighting to each previous measurement according to the number of notes counted for that measurement, but this is an unnecessary complication and would make very little difference to the result. When calculating the standard deviation it is more significant that each measurement is an average—the standard deviation calculation depends upon the differences between each individual note weight and the average contributing to the final result. As only an average is known for each measurement that will consist of a number of notes, loss of fidelity can be expected when calculating the standard deviation by applying the note weights from previous measurements to the standard deviation formula: 
     
       
         
           
             
               σ 
               W 
             
             = 
             
               
                 
                   1 
                   N 
                 
                  
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                       
                   
                    
                   
                     
                       ( 
                       
                         
                           W 
                           i 
                         
                         - 
                         μ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         σ W =standard deviation based on previous measurements   W i =note weights obtained from previous measurements   N=the number of previous measurements   i=index of previous measurements   μ=the current expected note weight       

     Simulation: 
     In order to observe the effect of calculating a standard deviation based on counting by weight measurements (σ W ), a simulation can be set up to compare this standard deviation with the actual standard deviation (σ). A simulation is prepared with inputs for mean weight and standard deviation of two series of notes and a starting slice limit. A table is set up, providing space for 100 simulated measurements—this equivalent to measuring 100 slices of a denomination of notes. For each simulated measurement, a random number of notes (up to the slice limit) are used, and a probability (between 0 and 1) of each note within the measurement being an old series note is entered. A Monte Carlo data set is added to the table with provision for up to 50 notes per measurement (a slice limit of more than 50 would never be used)
         this data is calculated to have the following properties:
           Data for each measurement is only populated up to the row corresponding to the number of notes used for the measurement.   A random number between 0 and 1 is used to determine if each note will be from the old or new series—if the random number is below the probability of an old note for the measurement the calculation is performed based on the mean and standard deviation for old notes, otherwise the mean and standard deviation for new notes is used.   Each data point will be calculated using the formula for Gaussian distributed data points for Monte Carlo simulations.   
               

     The following data is calculated from previous measurements:
         Learned note weight—for the first measurement, the mean weight for old series notes is used, thereafter a running average of the last 10 measured note weights.   σ (from last 10 measurements)—the actual standard deviation from the last 10 measurements, using individual note data from the Monte Carlo data set.   σ W  (from last 10 measurements)—the standard deviation based on the available information from the last 10 measurements (initially=0).       

     The following data is calculated from current measurements:
         Total weight for current measurement   Calculated number of notes (using total weight and learned note weight). This is calculated only by dividing total weight by note weight and rounding up or down (actual calculation provides the integer part of total weight+note weight+0.5).   Calculated note weight for this measurement. This is the total weight on the scale divided by the calculated number of notes.       

     For each measurement, the error is calculated—the difference between the actual number of notes applied to the simulation and the calculated number of notes. The frequency of these errors will be affected by the mean and standard distribution of the two series of notes: if the mean values are close together and the standard deviation is very small, the simulation could be run thousands of times without seeing an error but if the mean values are very far apart or the standard deviations indicate a wide tolerance, errors will be inevitable on almost every run of the simulation. Properties of mean and standard deviation should be used in the simulation that are realistic, i.e. likely to resemble a real situation, and not so severe as to eliminate any chance of improving the measurement accuracy. 
     For the purpose of the simulation, the note properties are chosen to be: 
     
       
         
           
               
               
               
             
               
                   
                   
               
             
            
               
                   
                 Old Series Notes Mean weight 
                 1.00 
               
               
                   
                 Old Series Notes Standard deviation 
                 0.02 
               
               
                   
                 New Series Notes Mean weight 
                 1.10 
               
               
                   
                 New Series Notes Standard deviation 
                 0.02 
               
               
                   
                   
               
            
           
         
       
     
     These values represent a new series note being produced with a 10% increase in weight. Both old and new series have a tolerance of about ±0.06 g. The starting slice limit is 25 and this is currently fixed over the entire range of the simulation. The probability of old series note is set to 1 for the first 10 measurements, then reducing by 0.01 on each measurement until a value of 0.5 is reached which is held for the rest of the simulation. This may be similar to the effect when a new series of banknotes is introduced—initially it is quite rare to come across them, but as time passes they become more common. 
     When the simulation is run several times, it can be seen that almost every time there are some errors. In occasional cases, a few errors close together cause the learned note weight to be calculated inaccurately—this can increase the risk of future errors. 
     The values of σ and σ W  can be plotted on a chart to see how they have responded to the random nature of the Mote Carlo analysis as shown on  FIG. 7 . The calculation of σ starts very close to the actual value of standard deviation (0.02). As the new series notes begin to take part in the simulation, the calculated value of σ increases to about 0.054. This matches the expected value obtained from the formula for standard deviation of two non-overlapping sub-populations: 
       σ=√{square root over ( k   1 ·σ 1   2   +k   2 ·σ 2   2   +k   1   ·k   2 ·(μ 1 −μ 2 ) 2 )}=0.0539
 
     The calculation of σ W  starts at 0 (this is unavoidable as there is no previous data to base the calculation upon). It can be seen that σ W  closely follows the shape of and σ, albeit plotted on a vertical axis scaled down by a factor of 0.1. The calculation of σW can be seen to change more erratically than the calculation of σ. 
     Using different profiles for the probability of old notes and running the simulation several times, a similar result is observed each time—the calculated value of σ closely follows the expected value, the calculated value of σ W  does likewise, but more erratically and reduced by a factor of 0.1. It may also be observed that if the slice limit is changed, this factor will also change, e.g. for a slice limit of 10 the factor becomes approximately 0.15 and for a slice limit of 1 the factor becomes 0.3. 
     A modification is now made to the simulation to allow the slice limit to be modified according to the calculated value of σ W . For each measurement the slice limit is calculated, and a check box is added to determine if this calculated slice limit is used or the starting slice limit is used across the entire simulation. 
     The slice limit adjustment calculation should have the following properties:
         Use the original slice limit as starting value.   Reduce the slice limit based upon the ratio of σ W  from previous measurements to the current learned note weight.   The sensitivity to the ratio of σ W  from previous measurements to the current learned note weight should be adjustable by a factor.   The calculated slice limit must never be zero or negative.       

     The last of these requirements implies that a negative exponential function should be used to calculate the slice limit adjustment, in order to prevent reaching zero. The factor used to adjust the sensitivity will determine the original rate. This provides the formula for automatically reducing the slice limit: 
     
       
         
           
             
               SL 
               n 
             
             = 
             
               INT 
                
               
                 ( 
                 
                   
                     SL 
                     o 
                   
                   · 
                   
                      
                     
                       
                         - 
                         k 
                       
                       · 
                       
                         
                           σ 
                           W 
                         
                         μ 
                       
                     
                   
                 
                 ) 
               
             
           
         
       
     
     where:
         SL n =the calculated new slice limit   SL o =the default slice limit   σ W =standard deviation based on previous measurements   μ=the current expected note weight   k=sensitivity factor       

     As this is a negative exponential function, the part inside the brackets may fall below 1 which, when the integer is taken gives a result of zero (it is expected that the value of k that is selected would reduce the probability of this happening, but for the purpose of the model it is possible for this to happen). It will also be the case that the exponential nature of this function will prevent the original value of SL o  ever being reached after values of σ W  become active. For these reasons, an offset of +1 should be added to the formula: 
     
       
         
           
             
               SL 
               n 
             
             = 
             
               1 
               + 
               
                 INT 
                  
                 
                   ( 
                   
                     
                       SL 
                       o 
                     
                     · 
                     
                        
                       
                         
                           - 
                           k 
                         
                         · 
                         
                           
                             σ 
                             W 
                           
                           μ 
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     The effect of this on the simulation is that the second measurement always has a slice limit of SL o +1, but this will only ever happen one time while the counting by weight equipment is being used, and could be compensated for, as the algorithm is refined. 
     Simulation Results: 
     Results from the simulation can be observed by producing a chart showing the change in slice limit and the errors over the range of measurements. Initially, the simulation is run with the automatic slice limit adjustment turned off. This simulation was run 20 times and the total errors from each run summed to give a grand total of 56. A typical chart of the slice limit and errors from these simulations is shown in  FIG. 8 . This shows the slice limit remaining at the starting value of 25 throughout the simulation and 4 errors occurring. 
     After turning on the automatic slice limit calculation, a sensitivity factor of k=50 was selected, based upon observations of the relation between σ and σW. 
     The simulation was then run with automatic slice limit adjustment turned on 20 times and the total errors from each run summed to give a grand total of 26. A typical chart of the slice limit and errors from these simulations is shown in  FIG. 9 . This shows the slice limit reacting to changes in standard deviation over the range of the simulation and 2 errors occurring. 
     Conclusion: 
     The results have shown that using an estimate of the standard deviation based on recent measurements, it is possible to make adjustments to the slice limit reducing the number of errors. These simulations do not use tapered weight acceptance bands, which would decrease or eliminate the remaining errors, and the note weight learning algorithm is a simple running average. Integration of automatic slice limit adjustment with currently used methods for reducing the probability of error would provide a satisfactory improvement to the performance of counting by weight cash counting devices when a weight variation is caused by differing note issues in legal circulation or circulated notes physical condition causes variance between notes. 
     The limitation of this method is that it is a learning algorithm and as such will only perform when changes are made gradually. This is usually the case as new banknotes come into circulation; they generally appear with a gradually increasing frequency. An example of a condition where this method would fail to react would be: a current learned note weight is 1.00 g; a new series of notes is released with a weight of 1.05 g; a slice of 20 new notes is immediately applied to the counting by weight device. In this situation the applied weight is 21.00 g and the counting by weight device will count this as 21 notes. The reason is the device has not had a chance to learn a new mean weight and standard deviation. This situation however is unrealistic and is only likely to occur if forced. 
     By mathematically modeling this process, it is possible to simulate unrealistic values. It is important when investigating the selection of the sensitivity constant that realistic values are considered, as would be the case when initially determining a slice limit—the result is then more likely to result in a constant that may be used universally for this method. 
     Automatic Slice Limit Adjustment Method ( 100 ): 
     Attention is now drawn to  FIG. 3 , showing one embodiment of an automatic slice limit adjustment method ( 100 ) according to the present invention and  FIG. 2 , showing one embodiment of a counting by weight cash counting process including an automatic slice adjustment method ( 100 ) according to the present invention. 
     The start ( 10 ) of the automatic slice limit adjustment method ( 100 ) is triggered by the counting by weight cash counting process ( 200 ) if item weight learning and automatic slice limit adjustment are enabled and a counting by weight cash counting operation is being undertaken. 
     Once the automatic slice limit adjustment method ( 100 ) has been triggered ( 10 ) step  1  ( 20 ) of the method is to store the learned weight of the items being counted to circular buffer  1  ( 21 ). If circular buffer  1  ( 21 ) is full the learned weight of the items being counted over-writes the oldest record in the circular buffer  1  ( 21 ). If the circular buffer  1  ( 21 ) is not full the learned weight of the item being counted is stored in the next empty storage position in circular buffer  1  ( 21 ). 
     Once the learned weight of the items being counted has been stored in circular buffer  1  ( 21 ) the automatic slice limit determination algorithm ( 60 ), comprising three calculation steps: 
     STEP  2 —Calculation of mean of item learned weights in circular buffer  1  ( 21 )-( 30 )
 
STEP  3 —Calculation of standard deviation from learned weights—( 40 )
 
STEP  4 —Calculation of new slice limit—( 50 ), is triggered.
 
STEP  2  ( 30 )—Calculation of mean of item learned weights in circular buffer  1  ( 21 ) is conducted using the formula:
 
     
       
         
           
             μ 
             = 
             
               
                 1 
                 N 
               
                
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   W 
                   i 
                 
               
             
           
         
       
     
     where:
         μ=the current (calculated) expected note weight (mean of item learned weights in circular buffer  1  ( 21 ))   W i =note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 ))   N=the number of previous measurements over which the average is calculated (number of records in circular buffer  1  ( 21 ))   i=index of previous measurements
 
STEP  3  ( 40 )—Calculation of standard deviation from learned weights is conducted using the formula:
       

     
       
         
           
             
               σ 
               W 
             
             = 
             
               
                 
                   1 
                   N 
                 
                  
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                       
                   
                    
                   
                     
                       ( 
                       
                         
                           W 
                           i 
                         
                         - 
                         μ 
                       
                       ) 
                     
                     2 
                   
                 
               
             
           
         
       
     
     where:
         σ W =the current (calculated) standard deviation based on item learned weights in circular buffer  1  ( 21 )   W i =note weights obtained from previous measurements (item learned weights in circular buffer  1  ( 21 ))   N=the number of previous measurements (number of records in circular buffer  1  ( 21 ))   i=index of previous measurements   μ=the current expected note weight (the result from Calculation at step  2 ))
 
STEP  4  ( 50 )—Calculation of new slice limit is conducted using the formula:
       

     
       
         
           
             
               SL 
               n 
             
             = 
             
               1 
               + 
               
                 INT 
                  
                 
                   ( 
                   
                     
                       SL 
                       o 
                     
                     · 
                     
                        
                       
                         
                           - 
                           k 
                         
                         · 
                         
                           
                             σ 
                             W 
                           
                           μ 
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     where:
         SL n =the calculated new slice limit   SL o =the default slice limit (from register SL o  ( 110 ))   σ W =the current standard deviation (the result from Calculation at step  3 ))   μ=the current expected note weight (the result from Calculation at step  2 ))   k=sensitivity factor       

     Once the automatic slice limit determination algorithm ( 60 ) is complete in step  5  ( 70 ) the calculated new slice limit (SL n ) is stored in register SL n  ( 120 ) for use by the counting by weight process ( 200 ) when next initiated for the same currency and denomination. 
     The automatic slice limit adjustment method ( 100 ) then exits ( 80 ) and returns control back to the counting by weight cash counting process ( 200 ). 
     An automatic slice limit adjustment method ( 100 ) is disclosed. An automatic slice limit adjustment method ( 100 ) is an integral part of a counting by weight cash counting process ( 200 ). The automatic slice limit adjustment method ( 100 ) comprises five steps ( 20 ,  30 ,  40 ,  50  &amp;  70 ): 
     Step  1  ( 20 )—Store item weight from the currently running counting by weight cash counting process ( 200 ) in circular buffer  1  ( 21 ).
 
Step  2  ( 30 )—Calculate mean of item weights in circular buffer  1  ( 21 ).
 
Step  3  ( 40 )—Calculate standard deviation based on weights in circular buffer  1  ( 21 ).
 
Step  4  ( 50 )—Calculate new slice limit.
 
Step  5  ( 70 )—Store new slice limit in register SL n  ( 120 ).
 
     The calculation steps; Step  2 , Step  3  &amp; Step  4  comprise the automatic slice limit determination algorithm ( 60 ). 
     In Step  5  ( 70 ) the calculated new slice limit is stored in register SL n  ( 120 ) for access by the counting by weight cash counting process ( 200 ) when next initiated for the same currency and denomination. 
     Modifications and improvements may be made to the above without departing from the scope of the invention.