Patent Abstract:
A dictation system that allows using trainable code phrases is provided. The dictation system operates by receiving audio and recognizing the audio as text. The text/audio may contain code phrases that are identified by a comparator that matches the text/audio and replaces the code phrase with a standard clause that is associated with the code phrase. The database or memory containing the code phrases is loaded with matched standard clauses that may be identified to provide a hierarchal system such that certain code phrases may have multiple meanings depending on the user.

Full Description:
CLAIM OF PRIORITY UNDER 35 U.S.C. §§119 AND 120 
     The present application claims priority to U.S. Provisional Patent Application Ser. No. 61/319,166 filed Mar. 30, 2010, titled HIERARCHICAL QUICK NOTE TO ALLOW DICTATED CODE PHRASES TO BE TRANSCRIBED TO STANDARD CLAUSES, which is incorporated herein as if set out in full. 
     REFERENCE TO CO-PENDING APPLICATIONS FOR PATENT 
     None. 
    
    
     BACKGROUND 
     1. Field 
     The technology of the present application relates generally to dictation systems, and more particular, to a hierarchical quick note that allows the use of a short dictated code phrase to be transcribed to a standard clause. 
     2. Background 
     Originally, dictation was an exercise where one person spoke while another person transcribed what was spoken. Shorthand was developed to facilitate transcription by allowing the transcriptionist to write symbols representative of certain utterances. Subsequently, the transcriptionist would replace the shorthand symbol with the actual utterance. 
     With modern technology, dictation has advanced to the stage where voice recognition and speech-to-text technologies allow computers and processors to serve as the transcriber. Speech recognition engines receive the utterances and provide a transcription of the same, which may subsequently be updated, altered, or edited by the speaker. 
     Current technology has resulted in essentially two styles of computer based dictation and transcription. One style involves loading software on a machine to receive and transcribe the dictation, which is generally known as client side dictation. The machine transcribes the dictation in real-time or near real-time. The other style involves sending the dictation audio to a centralized server, which is generally known as server side dictation. The centralized server transcribes the audio file and returns the transcription. There are two modes of server side dictation: (a) “batch” when the transcription is accomplished after hours, or the like, when the server has less processing demands; or (b) “real-time” when the server returns the transcription as a stream of textual data. 
     As can be appreciated, the present computer based dictation and transcription systems have drawbacks. One drawback is the lack of a shorthand type of methodology. Currently, dictation systems transcribe what is spoken. Certain industries, however, have repetitive clauses and phrases that must be repeated frequently. Conventional speech recognition software, however, is not typically customized for a particular industry so the repetitive clauses and phrases must be fully enunciated so the speech recognition software can accurately transcribe the repetitive clauses and phrases. As can be appreciated, repeating common clauses and phrases is time consuming. Against this background, it would be desirous to provide a method and apparatus wherein the repetitive clauses and phrases may be incorporated into a customizable shorthand or hierarchical quick note. 
     SUMMARY 
     To attain the advantages and in accordance with the purpose of the technology of the present application, a trainable transcription module having a speech recognition engine is provided. The trainable transcription module receives code phrases or quick notes from one of a plurality of sources. The code phrases or quick notes are matched with particular transcription textual data. The speech recognition engine receives audio data and converts the audio data to converted textual data. A comparator in the trainable transcription module would compare the converted textual data to the code phrases or quick notes from one of the plurality of sources. If the textual data matches one of the code phrases or quick notes, the trainable transcription module replaces the recognized textual data with the equated particular transcription textual data in the transcription of the audio. The comparator may use patterns, such as regular expressions, to match the converted textual data, and the ‘particular transcription textual data’ may include parametric substitution of values specified (as parameters) in the converted textual data. 
     Methods for using code phrases and quick notes from one of a plurality of sources also are provided. The method includes loading code phrases or quick notes into a trainable transcription module. The code phrases or quick notes would be equated with particular transcription textual data. Audio would be received and converted to converted textual data. The converted textual data would be compared to the code phrases or quick notes. If it is determined that the converted textual data matches the code phrase or quick note, the converted textual data would be removed, replaced, or overwritten with particular transcription textual data. The replacement includes also parametric substitution. 
     In certain aspects of the technology of the present invention, the converted textual data would only be compared to the code phrases or quick notes when the converted textual data or parametric substitution has at least a certain confidence. The confidence may be configurable depending on the application, but may require, for example, a confidence of 90% or more. 
     In still other aspects of the technology, code phrases or quick notes may be established in hierarchical arrangement, such as, for example, headquarters, division, corporate, or individual. Other organization structures are contemplated. In one aspect, a code phrase ( 1 ) may be established that is non-modifiable by entities lower in the hierarchical arrangement. In another aspect, the code phrase ( 1 ) may be established that is non-modifiable by entities higher in the hierarchical arrangement. In still another aspect, the code phrase ( 1 ) may be modified by any entity in the hierarchical arrangement. 
     Features from any of the above-mentioned embodiments may be used in combination with one another in accordance with the general principles described herein. These and other embodiments, features, and advantages will be more fully understood upon reading the following detailed description in conjunction with the accompanying drawings and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of an exemplary system consistent with the technology of the present application; 
         FIG. 2  is a functional block diagram of an exemplary module consistent with the technology of the present application; 
         FIG. 3  is a diagram of an exemplary database consistent with the technology of the present application; 
         FIG. 4  is a functional block diagram illustrative of a methodology consistent with the technology of the present application; 
     
    
    
     DETAILED DESCRIPTION 
     The technology of the present application will now be explained with reference to  FIGS. 1-4 . While the technology of the present application is described with relation to a transcription module resident with a speech recognition engine, one of ordinary skill in the art will recognize on reading the disclosure that other configurations are possible. For example, the technology of the present application may be used in conjunction with a thin or fat client such that the modules, engines, memories, and the like are connected locally or remotely. Moreover, the technology of the present application is described with regard to certain exemplary embodiments. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. All embodiments described herein should be considered exemplary unless otherwise stated. 
     Referring first to  FIG. 1 , a dictation system  100  is provided. Dictation system  100  includes a microphone  102 , which may be part of a headset  104  as shown, or a more conventional stand alone microphone. Microphone  102  is coupled via a communication link  106  to a client station  108 , such as a laptop computer, a desktop computer, a portable digital assistant, a smart phone, a cellular telephone, or the like. Optionally, microphone  102  may contain a processor to pre-process the audio into a format compatible with processor  108 . Communication link  106  may be any conventional communication link such as a universal serial bus, a Bluetooth connection, or the like. Processor  108  may be connected to a remote server  110  via a network  112 , such as, for example, a LAN, a WAN, a WLAN, a WIFI, a WMax, the Internet, an Ethernet, or the like. As shown in  FIG. 2 , client station  108 , remote server  110 , both, or a combination thereof, would contain all or parts of a transcription module  202 . The transcription module  202  is identified as a trainable transcription module because it can be trained to recognize code phrases or quick notes that are equated with particular transcription textual data as will be explained in more detail below. Transcription module  202  interconnects a transcription processor  204 , speech recognition engine  206 , a memory  208 , and an interface  210 . Interface  210  receives audio files, commands, and data from client station  108  or remote server  110  and transmits converted textual data to client station  108  or remote server  110 , or the like. Transcription processor  204  may be co-located with the central processing unit, microprocessor, field programmable gate array, logic circuits, chip-sets, or the like, of either client station  108  or remote server  110 . Transcription processor  204  controls the major functions of the transcription module  202  to allow it to function as further explained below. Transcription processor  204  also processes various inputs and/or data that may be required to operate the transcription module  202 . The memory  208  may be remotely located or co-located with transcription processor  204 . The memory  208  stores processing instructions to be executed by transcription processor  204 . The memory  208  also may store data necessary or convenient for operation of the dictation system. For example, memory  208  may store code phrases or quick notes and the equated particular transcription textual data as will be explained further below. Memory  208  also may store the audio file being transcribed as well as the transcribed textual data at least until the textual data file is transmitted from the trainable transcription module. Speech recognition engine  206  converts the utterances contained in the audio file to textual data, such as a word document, or the like. Speech recognition engine  206  may operate similar to a number of available speech recognition systems including, WINDOWS® Speech, which is available from Microsoft, Inc., Lumen Vox SRE, Nuance 9 Recognizer, which is available from Nuance, Inc., Dragon® NaturallySpeaking®, which is available from Nuance, Inc., among other available systems. As shown, transcription processor  204  contains a comparator  212 , although comparator  212  may be located remotely or separately from transcription processor  204 . Comparator  212  would compare clauses in converted textual data with code phrases or quick notes stored in memory  208 . If clauses in the converted textual data match a code phrase or quick note, transcription processor  204  would replace the converted textual data clause with particular transcription textual data equated with the code phrase or quick note (as can be appreciated code phrase and quick note are used interchangeably herein). 
     As mentioned, transcription module  202  stores code phrases in memory  208 . The code phrases are equated with particular transcription textual data. Referring to  FIG. 3 , a database  300  showing an exemplary memory database is provided. Database  300  has a plurality of code phrase fields  302   1-n , a plurality of particular transcription textual data fields  304   1-n  where each code phrase is associated with a corresponding particular transcription textual data. Database  300  also has a plurality of hierarchical fields  306   1-n . A hierarchical field  306  is associated with each code phrase field  302  and particular transcription textual data field  304 . Database  300  may be entered directly from trainable transcription module  202 , downloaded from client station  108  or remote server  110  as a matter of design choice. Also, as mentioned above, many organizations have an organizational structure. The present database shows in entity field  306  what entity established the code phrases. As shown in database  300 , code phrase ( 1 ) may be associated with two different particular transcription textual data ( 1 ), ( 2 ) established by different entities ( 1 ), ( 2 ). In this case, transcription processor  204  would select the appropriate particular transcription textual data depending on the user that created the audio file. For example, code phrase ( 1 ) may be associated with a divisional entity ( 1 ) that establishes particular transcription textual data ( 1 ), In this case, an entity above or below the divisional entity on the organization chart may elect to have a different particular transcription textual data ( 2 ) associated with code phrase ( 1 ). Thus, when entity ( 2 ) uses the code phrase ( 1 ) in the audio file, the trainable transcription module  202  would select particular transcription textual data ( 2 ) instead of particular transcription textual data ( 1 ) and when entity ( 1 ) uses the code phrase ( 1 ) in the audio file, the trainable transcription module  202  would select particular transcription textual data ( 1 ) instead of particular transcription textual data ( 2 ). Notice, the entity entry may designate whether edits or changes by higher, lower, or peer entities in the hierarchical structure can edit the particular transcription textual data. 
     Referring now to  FIG. 4 , a flow chart  400  is provided illustrative of a methodology of using the technology of the present application. While described in a series of discrete steps, one of ordinary skill in the art would recognize on reading the disclosure that the steps provided may be performed in the described order as discrete steps, a series of continuous steps, substantially simultaneously, simultaneously, in a different order, or the like. Moreover, other, more, less, or different steps may be performed to use the technology of the present application, In the exemplary methodology, however, code phrases, particular transcription textual data, and the appropriate entity indicator are loaded into memory  208 , step  402 . Next, audio data is provided to the transcription module  202 , step  404 . The speech recognition engine  206  would convert the audio data (whether streamed or batch loaded) to converted textual data, step  406 . For example, the audio data may be converted to a word document or the like. 
     The converted textual data is compared to the code phrases stored in memory to determine whether the words, clauses, phrases, etc. in the converted textual data match one or more code phrases, step  408 . Determining whether the connected textual data matches one or more code phrases may include determining that the confidence of the converted textual data is above, for example, 90%. The comparison may be performed substantially as the audio is converted to converted textual data or subsequently after the entire audio file is converted. If more than one code phrase is matched, the transcription module selects the code phrase having the appropriately matched entity indicator, step  410 . The converted textual data is replaced with particular transcription textual data, step  412 . The process continues until it is determined that the entire audio file has been transcribed, step  414 , and all the code phrases or quick notes have been matched and updated. The transcription module returns the transcribed textual data, step  416 , by streaming the data to client station  108  or remote processor  110 , batch loading the data to client station  108  or remote processor  110 , or a combination thereof. Notice, instead of using converted textual data in the comparison, the process may use utterances and match certain utterances to particular transcription textual data. 
     Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 
     The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
     The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Technology Classification (CPC): 6