Patent Abstract:
A triggering method for a produce recognition system which uses historical ambient light level readings. The method includes the steps of obtaining first ambient light levels from an ambient light sensor of the produce data collector with a data collection aperture covered, obtaining second ambient light levels from the ambient light sensor with the data collection aperture uncovered, determining a threshold ambient light level from the first ambient light levels and a difference between the first and second ambient light levels, obtaining a third ambient light level from the ambient light sensor with a produce item adjacent the data collection aperture, comparing the third ambient light level to the threshold ambient light level, and capturing data associated with the produce item if the third ambient light level is less than the threshold ambient light level.

Full Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present invention is related to the following commonly assigned and U.S. application: 
     “A Produce data collector And A Produce Recognition System”, filed Nov. 10, 1998, invented by Gu, and having a Ser. No. 09/189,783. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to product checkout devices and more specifically to a triggering method for a produce recognition system. 
     Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes. 
     Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent. 
     A produce recognition system is disclosed in the cited co-pending application. A produce item is placed over a window in a produce data collector, the produce item is illuminated, and the spectrum of the diffuse reflected light from the produce item is measured. A terminal compares the spectrum to reference spectra in a library to determine a list of candidate identifications. 
     The produce recognition system triggers illumination and data capture if ambient light levels fall below a threshold. This method works well under certain lighting conditions, but may not work well under other conditions, especially darker operating conditions. Operator intervention may be required if the produce data collector does not trigger when the produce item is first placed over the window of the produce data collector. 
     Therefore, it would be desirable to provide a triggering method which functions under a wider range of lighting conditions without operator intervention. 
     SUMMARY OF THE INVENTION 
     In accordance with the teachings of the present invention, a triggering method for a produce recognition system is provided. 
     The method includes the steps of obtaining first ambient light levels from an ambient light sensor of the produce data collector with a data collection aperture covered, obtaining second ambient light levels from the ambient light sensor with the data collection aperture uncovered, determining a threshold ambient light level from the first ambient light levels and a difference between the first and second ambient light levels, obtaining a third ambient light level from the ambient light sensor with a produce item adjacent the data collection aperture, comparing the third ambient light level to the threshold ambient light level, and capturing data associated with the produce item if the third ambient light level is less than the threshold ambient light level. 
     It is accordingly an object of the present invention to provide a triggering method for a produce recognition system. 
     It is another object of the present invention to provide a triggering method for a produce recognition system which works under a wide range of lighting conditions. 
     It is another object of the present invention to provide a triggering method for a produce recognition system which minimizes operator intervention. 
     It is another object of the present invention to provide a triggering method for a produce recognition system which dynamically adjusts the triggering threshold based upon ambient light level histories. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Additional benefits and advantages of the present invention will become apparent to those skilled in the art to which this invention relates from the subsequent description of the preferred embodiments and the appended claims, taken in conjunction with the accompanying drawings, in which: 
     FIG. 1 is a block diagram of a transaction processing system including a produce recognition system; 
     FIG. 2 is a block diagram of a type of produce data collector; and 
     FIG. 3 is a flow diagram illustrating the produce recognition method of the present invention. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to FIG. 1, transaction processing system  10  includes bar code data collector  12 , produce data collector  14 , and scale  16 . 
     Bar code data collector  12  reads bar code  22  on merchandise item  32  to obtain an item identification number, also know as a price look-up (PLU) number, associated with item  32 . Bar code data collector  12  may be any bar code data collector, including an optical bar code scanner which uses laser beams to read bar codes. Bar code data collector  12  may be located within a checkout counter or mounted on top of a checkout counter. 
     Produce data collector  14  collects data for produce item  18 . Such data may include color and color distribution data, size data, shape data, surface texture data, and aromatic data. Reference produce data is collected and stored within produce database  30 . During a transaction, produce data collector  14  is preferably self-activated upon a drop of ambient light. 
     Transaction terminal  20  and produce data collector  14  are the primary components of the produce recognition system. 
     Scale  16  determines a weight for produce item  18 . Scale  16  works in connection with bar code data collector  12 , but may be designed to operate and be mounted separately. Scale  16  sends weight information for produce item  18  to transaction terminal  20  so that transaction terminal  20  can determine a price for produce item  18  based upon the weight information. 
     Bar code data collector  12  and produce data collector  14  operate separately from each other, but may be integrated together. Bar code data collector  12  works in conjunction with transaction terminal  20  and transaction server  24 . Scale  16  may also work in connection with bar code data collector  12 , but may be designed to operate and be mounted separately. 
     Storage medium  26  preferably includes one or more hard disk drives. Produce database  30  is preferably stored within storage medium  26 , but may also be located instead at transaction terminal  20  in storage medium  38 . PLU data file  28  is stored within storage medium  26 , but may be located instead at transaction terminal  20  in storage medium  38  or within the memory of bar code data collector  12 . 
     Display  34  and input device  36  may be part of a touch screen or located separately. 
     In the case of bar coded items, transaction terminal  20  obtains the item identification number from bar code data collector  12  and retrieves a corresponding price from PLU data file  28  through transaction server  24 . 
     In the case of non-bar coded produce items, transaction terminal  20  executes produce recognition software  21  which obtains produce characteristics of produce item  18  from produce data collector  14 , identifies produce item  18  by comparing produce data in produce database  30  with collected produce data, and retrieves an item identification number from produce database  30  and passes it to transaction software  25 , which obtains a corresponding price from PLU data file  28 . 
     In an alternative embodiment, preliminary identification of produce item  18  may be handled by transaction server  24 . Transaction server  24  receives collected produce characteristics and compares them with produce data in produce database  30 . Transaction server  24  provides a candidate list to transaction terminal  20  for display and final selection. Following identification, transaction server  24  obtains a price for produce item  18  and forwards it to transaction terminal  20 . 
     To assist in proper identification of produce items, produce recognition software  21  additionally displays a number of candidate identifications for operator selection and verification. Produce recognition software  21  preferably arranges the candidate identifications in terms of probability of match and displays their images in predetermined locations on operator display  34  of transaction terminal  20 . The operator may accept the most likely candidate returned by produce recognition software  21  or override it with a different choice using input device  36 . 
     Turning now to FIG. 2, an example produce data collector  14  which relies on spectroscopic analysis is illustrated. Other types of produce data collectors are also envisioned. 
     Example produce data collector  14  primarily includes light source  40 , spectrometer  51 , control circuitry  56 , transparent window  60 , and housing  62 . 
     Light source  40  produces light  70 . Light source  40  preferably produces a white light spectral distribution, and preferably has a range from four hundred 400 nm to 700 nm, which corresponds to the visible wavelength region of light. 
     Light source  40  preferably includes one or more light emitting diodes (LEDs). A broad-spectrum white light producing LED, such as the one manufactured by Nichia Chemical Industries, Ltd., is preferably employed because of its long life, low power consumption, fast turn-on time, low operating temperature, good directivity. Alternate embodiments include additional LEDs having different colors in narrower wavelength ranges and which are preferably used in combination with the broad-spectrum white light LED to even out variations in the spectral distribution and supplement the spectrum of the broad-spectrum white light LED. 
     Other types of light sources  40  are also envisioned by the present invention, although they may be less advantageous than the broad spectrum white LED. For example, a tungsten-halogen light may be used because of its broad spectrum, but produces more heat. 
     A plurality of different-colored LEDs having different non-overlapping wavelength ranges may be employed, but may provide less than desirable collector performance if gaps exist in the overall spectral distribution. 
     Ambient light sensor  48  senses the level of ambient light through windows  60  and  61  and sends ambient light level signals  81  to control circuitry  56 . Ambient light sensor  48  is mounted anywhere within a direct view of window  61 . 
     Spectrometer  51  includes light separating element  52  and photodetector array  54 . 
     Light separating element  52  splits light  76  in the preferred embodiment into light  80  of a continuous band of wavelengths. Light separating element  52  is preferably a linear variable filter (LVF), such as the one manufactured by Optical Coating Laboratory, Inc., or may be any other functionally equivalent component, such as a prism or a grating. 
     Photodetector array  54  produces waveform signals  82  containing spectral data. The pixels of the array spatially sample the continuous band of wavelengths produced by light separating element  52 , and produce a set of discrete signal levels. Photodetector array  54  is preferably a complimentary metal oxide semiconductor (CMOS) array, but could be a Charge Coupled Device (CCD) array. 
     Control circuitry  56  controls operation of produce data collector  14 . Control circuitry  56  produces digitized produce data waveform signals  84 . Control circuitry  56  compares ambient light level readings from ambient light sensor  48  with the threshold and triggers operation when the ambient light level readings are lower than the ambient light level threshold. Control circuitry  56  includes an analog-to-digital (A/D) converter. A twelve bit A/D converter with a sampling rate of 22-44 kHz produces acceptable results. 
     Control circuitry  56  also controls triggering of light source  40  and capture of analog produce data signals  82  from spectrometer  51 , although produce recognition software  21  may alternatively handle this task. Control circuitry  56  uses an ambient light threshold stored within memory  58  by produce recognition software  21 . Control circuitry  56  collects ambient light levels during operation, i.e., when produce item  18  is over produce data collector  14  and when produce item  18  is not over produce data collector  14 . 
     Produce recognition software  21  stores light level information in ambient light level data file  39 , which is preferably stored in storage medium  38 . Produce recognition software  21  determines average light and dark levels from the light level information and programs control circuitry  56  with a threshold ambient light level between the light and dark levels so that control circuitry  56  may properly trigger illumination and data capture. Produce recognition software  21  may automatically update the ambient light level threshold on a regular basis. 
     Transparent window  60  is mounted above auxiliary transparent window  61 . Windows  60  and  61  include an anti-reflective surface coating to prevent light  72  reflected from windows  60  and  61  from contaminating reflected light  74 . 
     Housing  62  contains light source  40 , ambient light sensor  48 , spectrometer  51 , photodetector array  54 , control circuitry  56 , auxiliary transparent window  61 , and transparent window  60 . 
     Turning now to FIG. 3, the triggering method of the present invention begins with START  90 . 
     In step  92 , produce recognition software  21  collects light and dark ambient light levels. The light levels are taken with nothing over window  60  and light source  40  off. The dark levels are taken with a reference over window  60  and light source  40  off. A suitable reference is a white piece of plastic which completely covers window  60  so as to block ambient light from entering window  60 . 
     Due to the constraint of storage space in produce data collector  14 , the most effective method of storing the history of dark and light levels has proven to be the weighted average method as described below. The current average A t  of the recent dark levels is computed using: 
     
       
           A   t =(1 −k ) A   t−1   +kD   t , 
       
     
     where t denotes the current sampling time, D t  is the current measure of dark level, and constant k is a tunable number between 0 and 1. Constant k can be considered as a “forgetting factor” which controls how fast the history is forgotten in computing the average. The larger the value of k, the quicker the history is forgotten. In reality, k is tuned to an optimal value by experimentation. 
     Likewise, the current average B t  of recent light levels is computed using the same method, while substituting the current dark level D t  with the light level L t : 
     
       
           B   t =(1 −k ) B   t−1   +kL   t . 
       
     
     In step  94 , produce recognition software  21  stores the light and dark ambient light levels in ambient light level data file  39 . 
     In step  96 , produce recognition software  21  determines a threshold from the light and dark ambient light levels and stores the threshold in memory  58 . The triggering threshold T t  is then determined from the current average of dark and light levels as follows: 
     
       
           T   t   =A   t   +p ( B   t   −A   t ), 
       
     
     where p is a weight or tunable value between 0 and 1. 
     In step  98 , control circuitry  56  monitors ambient light levels from ambient light sensor  48 . 
     In step  100 , control circuitry  56  waits for ambient light levels to fall below the threshold in memory  58 . If they do, operation proceeds to step  102 . 
     In step  102 , control circuitry  56  turns on light source  40  and begins processing of data from spectrometer  51 . The method ends in step  104 . 
     Produce recognition software  21  obtains digital produce data from control circuitry  56  and determines a list of candidate identifications from produce database  30 . Produce recognition software  21  additionally displays a number of the candidate identifications on display  34  for operator verification and selection using input device  36 . 
     Transaction terminal  20  uses the identification information to obtain a unit price for produce item  18  from transaction server  24 . Transaction terminal  20  then determines a total price by multiplying the unit price by weight information from scale  16 . 
     Although the invention has been described with particular reference to certain preferred embodiments thereof, variations and modifications of the present invention can be effected within the spirit and scope of the following claims.

Technology Classification (CPC): 6