Patent Abstract:
There is disclosed a system and method for interpreting and describing graphic images. In an embodiment, the method of inserting a description of an image into an audio recording includes: interpreting an image and producing a word description of the image including at least one image keyword; parsing an audio recording into a plurality of audio clips, and producing a transcription of each audio clip, each audio clip transcription including at least one audio keyword; calculating a similarity distance between the at least one image keyword and the at least one audio keyword of each audio clip; and selecting the audio clip transcription having a shortest similarity distance to the at least one image keyword as a location to insert the word description of the image. The word description of the image can then be appended to the selected audio clip to produce an augmented audio recording including the interpreted word description of the image.

Full Description:
COPYRIGHT NOTICE 
       [0001]    A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of the patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
       BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention relates to systems and methods for inserting a description of images into audio recordings. 
         [0004]    2. Related Art 
         [0005]    Recording a lecture or a presentation in an audio format can be a convenient and effective way to disseminate information beyond the immediate audience. However, if the presentation contains images, diagrams and charts, the lack of visual content can have a significant impact on the effectiveness of the delivery. 
       SUMMARY OF THE INVENTION 
       [0006]    The present invention relates to a system and method for inserting a description of images into audio recordings. 
         [0007]    A method in accordance with an embodiment of the present invention begins by interpreting images through deciphering non-text content, compiling any meta-tag information, collecting optical character recognition (OCR) data, and/or the like. The method then integrates, filters, and prioritizes the information to create a useful and succinct non-visual (e.g., audio, text, etc.) description of the image. The results of this image interpretation and description augment the non-visual content when the images are not available, such as when listening to an audio recording, or hearing a text-to-speech system read the text. For example, the system can interpret common presentation and graphics programs and insert a description of the images in an audio recording of the presentation. 
         [0008]    In an aspect, there is provided a method of inserting a description of an image into an audio recording, comprising: interpreting an image and producing a word description of the image including at least one image keyword; parsing an audio recording into a plurality of audio clips and producing a transcription of each audio clip, each audio clip transcription including at least one audio keyword; calculating a similarity distance between the at least one image keyword and the at least one audio keyword of each audio clip; and selecting the audio clip transcription having a shortest similarity distance to the at least one image keyword as a location to insert the word description of the image. 
         [0009]    In an embodiment, the method further comprises appending the word description of the image to the selected audio clip to produce an augmented audio recording including at least one interpreted word description of an image. 
         [0010]    In another embodiment, the method further comprises providing at least one template to interpret the image, the at least one template including at least one image interpretation component to produce a word description of the image. 
         [0011]    In another embodiment, the method further comprises providing at least one of optical character recognition (OCR) technology, edge finding technology, color edge finding technology, curve finding technology, shape finding technology, and contrast finding technology as an image interpretation component in the at least one template. 
         [0012]    In another embodiment, the method further comprises parsing the audio recording into a plurality of audio clips of substantially the same length, and adjusting the length of each audio clip to end at a natural pause in speech. 
         [0013]    In another embodiment, the method further comprises calculating the similarity distance between the image and an audio clip by calculating the similarity distance between at least one image keyword of an image and the at least one audio keyword of an audio clip. 
         [0014]    In another embodiment, the method further comprises obtaining the similarity distance between the at least one image keyword and the at least one audio keyword by calculating a path length between these keywords in a hierarchical semantic electronic dictionary. 
         [0015]    In another aspect, there is provided a system for inserting a description of an image into an audio recording, comprising: an interpreting system for interpreting an image and producing a word description of the image including at least one image keyword; a parsing system for parsing an audio recording into a plurality of audio clips and for producing a transcription of each audio clip, each audio clip transcription including at least one audio keyword; a calculating system for calculating a similarity distance between the at least one image keyword and the at least one audio keyword of each audio clip; and a selecting system for selecting the audio clip transcription having a shortest similarity distance to the at least one image keyword as a location to insert the word description of the image. 
         [0016]    In an embodiment, the system further comprises an appending system for appending the word description of the image to the selected audio clip to produce an augmented audio recording including at least one interpreted word description of an image. 
         [0017]    In another embodiment, the system further comprises at least one template to interpret the image, the at least one template including at least one image interpretation component to produce a word description of the image. 
         [0018]    In another embodiment, the system further comprises at least one of optical character recognition (OCR) technology, edge finding technology, color edge finding technology, curve finding technology, shape finding technology, and contrast finding technology as an image interpretation component in the at least one template. 
         [0019]    In another embodiment, the system is configured to parse the audio recording into a plurality of audio clips of substantially the same length and adjust the length of each audio clip to end at a natural pause in speech. 
         [0020]    In another embodiment, the system is configured to calculate the similarity distance between the image and an audio clip by calculating the similarity distance between at least one image keyword of an image and the at least one audio keyword of an audio clip. 
         [0021]    In another embodiment, the system is configured to calculate the similarity distance between the at least one image keyword and the at least one audio keyword based a path length between these keywords in a hierarchical semantic electronic dictionary. 
         [0022]    In another aspect, there is provided a program product stored on a computer readable medium, which when executed, inserts a description of an image into an audio recording, the computer readable medium comprising program code for: interpreting an image and producing a word description of the image including at least one image keyword; parsing an audio recording into a plurality of audio clips and producing a transcription of each audio clip, each audio clip transcription including at least one audio keyword; calculating a similarity distance between the at least one image keyword and the at least one audio keyword of each audio clip; and selecting the audio clip transcription having a shortest similarity distance to the at least one image keyword as a location to insert the word description of the image. 
         [0023]    In an embodiment, the program product further comprises program code for appending the word description of the image to the selected audio clip to produce an augmented audio recording including at least one interpreted word description of an image. 
         [0024]    In an embodiment, the program product further comprises program code for providing at least one template to interpret the image, the at least one template including at least one image interpretation component to produce a word description of the image. 
         [0025]    In an embodiment, the program product further comprises program code for providing at least one of optical character recognition (OCR) technology, edge finding technology, color edge finding technology, curve finding technology, shape finding technology, and contrast finding technology as an image interpretation component in the at least one template. 
         [0026]    In an embodiment, the program product further comprises program code for parsing the audio recording into a plurality of audio clips of substantially the same length, and adjusting the length of each audio clip to end at a natural pause in speech. 
         [0027]    In an embodiment, the program product further comprises program code for calculating the similarity distance between the image and an audio clip by calculating the similarity distance between at least one image keyword of an image and the at least one audio keyword of an audio clip. 
         [0028]    In an embodiment, the program product further comprises program code for obtaining the similarity distance between the at least one image keyword and the at least one audio keyword by calculating a path length between these keywords in a hierarchical semantic electronic dictionary. 
         [0029]    These and other aspects of the invention will become apparent from the following more particular descriptions of exemplary embodiments. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0030]    These and other features of the present invention will be more readily understood from the following detailed description taken in conjunction with the accompanying drawings. 
           [0031]      FIG. 1  is a schematic diagram of a generic data processing system that can provide an operative environment for the present invention. 
           [0032]      FIG. 2  shows a schematic flowchart of an illustrative image interpretation method in accordance with an embodiment of the present invention. 
           [0033]      FIGS. 3A and 3B  show a schematic flowchart of an illustrative source determining and pre-processing method in accordance with an embodiment of the present invention. 
           [0034]      FIG. 4  shows an illustrative image file processing method in accordance with an embodiment of the present invention. 
           [0035]      FIGS. 5A and 5B  show a schematic flowchart of an illustrative component assembly method in accordance with an embodiment of the present invention. 
           [0036]      FIG. 6  shows a schematic flowchart of an illustrative sound recording pre-processing method in accordance with an embodiment of the present invention. 
           [0037]      FIG. 7  shows a schematic flowchart of an illustrative image insertion location search method in accordance with an embodiment of the present invention. 
           [0038]      FIG. 8  shows a schematic flowchart of an illustrative image insertion method in accordance with an embodiment of the present invention. 
           [0039]      FIG. 9  shows an illustrative example of an image that can be identified and described in accordance with an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0040]    As noted above, the present invention relates to a system and method for interpreting and describing graphic images. 
         [0041]    The invention can be practiced in various embodiments. A suitably configured data processing system, and associated communications networks, devices, software, and firmware can provide a platform for enabling one or more of these systems and methods. By way of example,  FIG. 1  shows a generic data processing system  100  that can include a central processing unit (“CPU”)  102  connected to a storage unit  104  and to a random access memory  106 . The CPU  102  can process an operating system  101 , application program  103 , and data  123 . The operating system  101 , application program  103 , and data  123  can be stored in storage unit  104  and loaded into memory  106 , as can be required. An operator  107  can interact with the data processing system  100  using a video display  108  connected by a video interface  105 , and various input/output devices such as a keyboard  110 , mouse  112 , and disk drive  114  connected by an I/O interface  109 . In known manner, the mouse  112  can be configured to control movement of a cursor in the video display  108 , and to operate various graphical user interface (“GUI”) controls appearing in the video display  108  with a mouse button. The disk drive  114  can be configured to accept data processing system readable media  116 . The data processing system  100  can form part of a network via a network interface  111 , allowing the data processing system  100  to communicate with other suitably configured data processing systems (not shown). The particular configurations shown by way of example in this specification are not meant to be limiting. 
         [0042]    More generally, a method in accordance with an embodiment can involve interpreting and describing an image, and synchronizing the audio or text description with the logical insertion point in the audio or text transcript. 
         [0043]    In interpreting the charts or diagrams, image pattern recognition technology can be used to identify the contents. Image processing technology can be used to extract text such as titles and notes. Meta-tagging technology can be used by the author, or by a contributor, and these tags can be used to augment and standardize the translation. Meta-tagging examples can include, for example, identifying the X and Y-axes, the chart type, segments of a chart, the legend, etc. Filtering technology can also be used to eliminate some data (such as page numbers, headers &amp; footers) and highlight other information such as the chart title. As well, OCR technology can be used to determine other textual content. This OCR information can capture not only the text content, but also the position, orientation, text size and font, etc., and this information can be used in subsequent filtering and prioritization processes described further below. 
         [0044]    Voice recognition technology can be used to assess the original source context and extract information that can help describe the content of the chart and/or help align the description of the image back into the original source content. 
         [0045]    Translation technology can be employed to rephrase the content from one context to another context that it more suitable for the final purpose. 
         [0046]    In accordance with another embodiment, the method can analyze other source content in relation to the interpreted images to align the two content types. Natural language processing and a semantic electronic dictionary can be used for measuring the semantic similarity distance between images and the other source content. Locations with the shortest similarity distance in the other source content can be used to place the images. Since most presentations often follow a logical order once a correct reference point is established, it is easier to place the interpreted image description back into the presentation. 
         [0047]    Independent control over the descriptive additions allow the user to apply this method to previous and future image from the original source content. This will help align the deciphering system to the original audio or text that can then be used as a reference point to continue deciphering and alignment. The alignment process need only happen once as users can download the annotated version of the presentation, and not the source and the augmentation information separately. 
         [0048]    An illustrative method  200  in accordance with an embodiment of the present invention is now described with reference to  FIG. 2 . As shown, a list of images (e.g., as used in a presentation) is received at block  202  as an input. Method  200  then proceeds to block  204 , where for each image, method  200  determines the image type. At block  206 , method  200  pre-processes the image based on the image type (as described in further detail with respect to  FIG. 3A and 3B  below), then proceeds to decision block  208  to evaluate the success of the image type determination. At decision block  208 , if the answer is no, method  200  proceeds to block  210  for further pre-processing, possibly using meta-tags and pattern mapping, then to block  212  where method  200  can learn new patterns. Method  200  returns to block  204  with this new information for further pre-processing. 
         [0049]    If, at decision block  208 , the answer is yes, method  200  proceeds to block  214 , where method  200  processes and generates a list of image keywords associated with the image. Method  200  then proceeds to block  216 , where method  200  can eliminate extraneous words (e.g., page number, copyright notice). Method  200  then proceeds to block  218 , where method  200  generates a description of the image based on the image keywords. Method  200  then proceeds to block  220 , where method  200  determines if there are more images. If yes, method  200  returns to block  204  and continues. If no, method  200  proceeds to connector D ( FIG. 6 ). 
         [0050]      FIGS. 3A and 3B  show a schematic flowchart of an illustrative data source determining and pre-processing method  300  in accordance with an embodiment of the present invention. Method  300  begins at block  302 , and at block  304  receives the source data or image. At decision block  306 , method  300  determines if the source is an image file (e.g., jpeg, pdf) or a data file (e.g., ppt, vsd). If a data file, method  300  proceeds to block  308 , where the data files are expected to have additional information stored digitally (e.g., doc, ppt, vsd, xis,  123 , etc.). Method  300  then proceeds to decision block  310 , where method  300  determines if the data file contains additional meta-tags to assist in the image interpretation. If no, method  300  proceeds directly to block  502  ( FIG. 5A ) via connector C. If yes, method  300  proceeds to block  312 , where method  300  parses and interprets the meta-tags. These meta-tags can be industry standards, or tags specific to the source file types. Method  300  then proceeds to connector C. 
         [0051]    If, at decision block  306 , the source is instead an image file, method  300  proceeds to block  314  via connector A 2  ( FIG. 3B ). As image files typically have less retrievable meta-data, method  300  proceeds to block  316 , where method  300  can prepare the image file for other types of analysis. This preparation can include, for example, de-skewing, noise reduction, signal to noise averaging, etc. Method  300  can then proceed to block  318 , where a pattern resulting from the preparation can be compared against patterns or templates stored in a pattern portfolio to determine the likely type of the source image. For example, the pattern or template matching can indicate that the source image is a bar chart, a pie chart, a text table, a line chart, etc. Various techniques for image analysis can be used with the present method are briefly discussed at http://en.wikipedia.org/wiki/Computer vision. For example, various methods for noise reduction are described http://www.mathtools.net/Java/lmage Processing/. Graphic image processes including de-skewing, automatic cropping, automatic border extraction, and removal of noise artifacts are described at http://www.sharewareriver. com/products/6116.htm. Optical character recognition (OCR) techniques are described at http://www.nuance.com/omnipage/professional/ and http://www.csc.liv.ac.uk/˜wda2003/Papers/Section IV/Paper 14.pdf. Using contrast techniques to segment items from an image is described at http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-Segmenta.html. Circle and curve determination techniques are described at http://homepages.inf.ed.ac.uk/cgi/rbf/CVONLINE/entries.pl7TAG382. Figure to data conversion line techniques are described at http://ichemed.chem.wisc.edu/iournal/issues/2003/Sep/abs10932.html. Color edge detection techniques for bars graphs, pie charts, etc. are described at http://ai.stanford.edu/˜ruzon/compass/color.html. Volume determination (for venn diagrams, pie charts, etc.) are described at http://www.spl.harvard.edu:8000/pages/papers/guttmann/ms/guttmann_rev.html.) 
         [0052]    Method  300  then proceeds to block  320 , where method  300  processes the source image file based on its likely type. For example, if the source content is a bar chart, a corresponding template for bar charts can be retrieved and the bar chart contents can be analyzed using the template for interpretation and description. Blocks  318  and  320  are repeated as necessary until an optimal fit is achieved in block  322 . Flow then passes to block  402  via connector B. 
         [0053]    Now referring to  FIG. 4 , shown is an image file processing method  400  in accordance with an embodiment of the present invention. Method  400  begins at block  402  and proceeds to decision block  404  to determine if a pattern in a pattern portfolio exceeds a predetermined threshold, suggesting that the source image file type has been matched. If yes, method  400  proceeds to block  502  ( FIG. 5A ) via connector C. If no, method  400  proceeds to block  406 , where method  400  pre-processes and compares the image file with the “best fit” pattern from the existing pattern portfolio. Method  400  then proceeds to decision block  408 . 
         [0054]    At decision block  408 , if a minimum threshold cannot be met, the image cannot be interpreted and described (e.g., the image can be of an abstract painting, or a sketch drawn freehand), and method  400  returns to block  302  via connector A. If, at block  408 , the minimum threshold can be met, method  400  proceeds to block  410 . At this step  410 , the system can log the image as a potential new pattern and without any further processing, flow passes to block  302  via connector A. At the end of the process, the list of potential new pattern images can be reviewed (e.g., by a system analyst), and new templates for data extraction based on the pattern can be generated. These new templates can then be saved in the pattern portfolio, so they can be used in the next rounds of automated processes. 
         [0055]    Now referring to  FIGS. 5A and 5B , shown is a schematic flowchart of a component assembly method  500  in accordance with an embodiment of the present invention. Method  500  begins at block  502  and proceeds to decision block  504 , where method  500  determines if the source file is an image file (e.g., jpeg, pdf) or a data file (e.g., ppt, vsd). 
         [0056]    If a data file, method  500  proceeds to block  506 , where method  500  applies a template to extract content from the data, including attributes, context, numerical values, etc. For example, a template for an x-y graph can extract information such as titles, name of the x-axis, name of the y-axis, details for lines drawn in the chart, and any labels for the lines. It will be appreciated that the templates can be drafted for each specific type of data file in order to extract key information. 
         [0057]    Method  500  then proceeds to block  508 , where method  500  can construct logical text structures, and populate them from the data extracted using the template. For example, in order to describe an x-y graph, the text structures can include the title, name of the x-axis, name of the y-axis, and text structures to describe straight lines by their slopes and relative positions in the x-y graph. Method  500  then proceeds to block  510 , where method  500  can store the results of the segmentation processes as identifiable components in the logical structures. Method  500  then proceeds to block  302  ( FIG. 3A ) via connector A. 
         [0058]      FIG. 5B  shows the steps of method  500  if, at decision block  504 , the source file is an image file. Method  500  proceeds to block  514  via connector C 2  where a selected pattern or template is used to segment the image file into components (e.g., legend, axis, title, etc.). 
         [0059]    Method  500  then proceeds to one or more of blocks  516 ,  518 ,  520 ,  522 ,  524 ,  526  to interpret the image file. For example, at block  516 , method  500  can use OCR to determine the text content. At block  518 , method  500  can use edge finding technology to find a line graphical component. At block  520 , method  500  can use color edge technology to find a line graphical component. At block  522 , method  500  can use curve finding technology to find a curved line graphical element. At block  524 , method  500  can use circle, ellipse, and blob finding technology to find 2-D graphical components. At block  526 , method  500  can use contrast finding technology to find bars, pie segments, etc. 
         [0060]    Method  500  then proceeds to block  528 , where method  500  can interpret each found object for numbers, labels, or other attributes such as the relative position of bars from left to right, relative percentages of pie segments, etc. Method  500  then proceeds to block  530 , where method  500  can document segmented elements discovered from applying one or more analysis techniques as described above. Method  500  then proceeds to block  532 , where method  500  can coordinate and align the components. Method  500  then proceeds to block  508  ( FIG. 5A ) via connector C 3  as described above and continues. 
         [0061]    Now referring to  FIG. 6 , shown is a schematic flowchart of an audio pre-processing method  600 . Method  600  begins at block  602  and proceeds to block  604  to receive an audio recording as an input. Method  600  then proceeds to block  606 , where method  600  divides the audio program into a vector of audio clips, each audio clip ends at a natural pause in the speech, such as the end of a sentence, and close to a fixed length (e.g., 30 seconds). 
         [0062]    Method  600  then proceeds to block  608 , where method  600  continues for each audio clip. Method  600  proceeds to block  610 , where voice recognition techniques can be used to translate the audio clip into text. At block  612 , method  600  can then use a natural language parser to parse the translated text. Method  600  can then produce a noun phrases vector that contains 0 to n noun phrases extracted from the audio clip. Method  600  then proceeds to block  616 , where method  600  maps certain common names or names not found in a dictionary to words in the dictionary. Method  600  then proceeds to block  618 , where method  600  calculates the importance value of each noun phrase, and removes less meaningful ones. Method  600  then proceeds to block  620 , where method  600  produces a keywords vector for the audio clip that contains 0 to n keywords. Method  600  then proceeds to decision block  622  to determine if there are more audio clips. If yes, method  600  returns to block  608  and continues. If no, method  600  proceeds via connector E to block  702  of method  700  of  FIG. 7 . 
         [0063]    Now referring to  FIG. 7 , shown is an image insertion location search method  700  in accordance with an embodiment. Method  700  begins at block  702 , and proceeds to block  704 , where method  700  receives as an input a pre-processed image represented by an image keyword vector containing 0 to n keywords, and a pre-processed audio program represented by a vector of audio clip keyword vectors (where each audio clip keyword vector represents an audio clip). 
         [0064]    Method  700  then proceeds to block  706 , where method  700  continues for each audio clip in the audio program. At block  708 , method  700  continues for each keyword in the image keyword vector. Method  700  then proceeds to block  710 , where method  700  continues for each keyword in an audio keyword vector representing an audio clip. Method  700  then proceeds to block  712 , where method  700  calculates the similarity distance between the current image keyword and audio keyword. At block  714 , method  700  updates the shortest distance between this image keyword and audio keyword, and goes to the next keyword in the audio clip, if present, by returning to block  710 . If not, method  700  proceeds to block  716  where method  700  assigns this shortest distance value as the similarity distance between this image keyword and audio clip. Method  700  then proceeds to block  718 , where method  700  updates the shortest distance between this image keyword and audio clip, and goes to the next keyword in the image, if present, by returning to block  708 . If not, method  700  proceeds to block  720 , where method  700  assigns this shortest distance value as the similarity distance between this image and the audio clip. Method  700  then proceeds to block  722 , where method  700  records the audio clip with the shortest distance, and goes to the next audio clip, if present, by returning to block  706 . If not, method  700  proceeds to block  724 , where method  700  identifies the audio clip with the shortest similarity distance to the image as the place to insert the image. Method  700  then proceeds to block  802  ( FIG. 8 ) via connector F. 
         [0065]    Now referring to  FIG. 8 , shown is an image insertion method  800  in accordance with an embodiment. Method  800  begins at block  802  and proceeds to block  804  to receive an input of a list of images, each image represented by an image keyword vector and a corresponding insertion point. Method  800  then proceeds to block  806 , where method  800  continues for each sound clip in a sound recording. Method  800  then proceeds to block  808  to append this sound clip to the resulting image description augmented sound recording. 
         [0066]    Method  800  then proceeds to block  810  to continue for each image in the list of images. Method  800  then proceeds to decision block  812  to determine if the image should be inserted after the current sound clip. If no, method  800  returns to block  810 . If yes, method  800  proceeds to block  814  to generate an image description audio clip from the image keywords using voice generation tools. Method  800  then proceeds to block  816 , where method  800  appends the newly generated image description audio clip at the identified insertion point. Method  800  then proceeds to decision block  818  to determine whether to return to block  810 , or to proceed to decision block  820 . At decision block  820 , method  800  determines whether to return to block  806 , or to end. 
         [0067]    As will be appreciated, the above described methods identify and describe images in text and audio, locate the appropriate point of insertion in the original audio recording using similarity distances calculated based on keywords, and insert the image description at the appropriate identified location. Thus, images that would otherwise not be viewed by a listener of the audio recording will now be described in an image description audio clip inserted into and augmenting the original sound recording. 
       EXAMPLE 
       [0068]      FIG. 9  shows an illustrative example of a graphic image  900 , that can be identified and described using the method as described above. 
         [0069]    For example, in an audio recording of a lecture, the lecturer can refer to a number of graphics or charts, such as the graphic image  900  shown in  FIG. 9 . At some point in the lecture, e.g., at a time reference of 10:25 am, the lecturer can make reference to a chart with the title “disruptors”. Then at 10:30 am he can say “diagram” and “line” which can be interpreted as him referring to a line chart. He can also specifically says at 10:35 am “to keep the diagram simple I&#39;ll just depict that ability to use improvement as a single line . . . ” 
         [0070]    In accordance with an embodiment, the system can imbed an interpreted description of the chart  900  at 10:30 am, which can state the following: Title: “disruptions”, X-axis: “time”, Y-axis: “performance”. Line A with a slope of about 10° entitled “Ability to use improvement”. Line B with a slope of about 25° entitled “Innovations”. Line B intersects Line A at time D. Line C with a slope of about 25° entitled “Disruptions”. Line C intersects line A at time E. 
         [0071]    As will be appreciated, a system and method that can interpret charts such as chart  900  and provide a verbal description can provide a listener with more context to understand the lecture than if such information was not provided. While various illustrative embodiments of the invention have been described above, it will be appreciated by those skilled in the art that variations and modifications can be made. Thus, the scope of the invention is defined by the following claims.

Technology Classification (CPC): 6