Turning next to FIG. 5, illustrated is a flow diagram of an exemplary method (generally designated 345) for processing received oral messages and translating the same into at least substantially equivalent text messages in accordance

with the principles of the present invention. For purposes of illustration, concurrent reference is made to FIGS. 1 through 4. Although the present discussion is directed largely to process step 345 of FIG. 3, the principles hereof may

suitably be adapted for process step 320 as well.

To begin, translating controller 205 accesses data repository 200 (particularly, data patterns 405 stored therein) and stores the received message input therein (process step 500). Translating controller 205 may store the message input

directly or indirectly by controlling messaging How to remove Avast email signature from my email controller 210. Translating controller 205 parses the message input, separating the same into sub-parts (process step 505). At least some of these sub-parts have one or more measurable

characteristics associated therewith.

Translating controller 205 attempts to recognize the message input by selectively comparing data patterns 405 and the sub-parts (process step 510). More particularly, according to the present embodiment, particular data patterns are

emphasized during the comparison process in response to the content of data record 415, the emphasized data patterns and the measurable characteristics are suitably compared.

response to the one or more data record(s) associated with subscriber 120, controls messaging controller 210.

Tu, illustrated is a conceptual configuration of a conventional memory 400 that includes data repository 200. Data repository 200 includes a plurality of data patterns 405 (i.e., speech models constructed using

knowledge of acoustics, language, dictionaries, grammars or the like) and a plurality of subscriber records 410.

According to this embodiment, a particular subscriber Gmail Numeric Code 6922 issue record 415 is associated with subscriber 120 and, among its other attributes, includes pointers to particular ones of the plurality of data patterns 405. These particular data

patterns are a “subset” of data patterns that represent oral phrases common to subscriber 120. Thus, while data patterns 405 are collectively specific to a general type of non-realtime messaging system, namely, message paging system

100, this subset of data patterns is particularly related to subscriber 120.

Referring back to the above-given example, assume again that a freight delivery company subscribes to a suitably arranged message paging system and each of its drivers carries a conventional alphanumeric message pager. Assume further

that one of its drivers is subscriber 120 and that subscriber 120 works directly for “Joe,” is married to “Jane,” delivers a lot of “widgets,” and his primary delivery stops are at “ABC Company,” “XYZ Inc.,” and “OPQ Limited.” Data

record 415 may suitably define a data pattern subset of {joe jane widgets abc co. company xyz inc. incorporated opq ltd. limited}. Data patterns 405, used in combination with a suitable subset thereof, may cooperatively provide a

context sensitive vocabulary that can increase the likelihood that translating controller 205 will successfully translate (recognize, at least in part) a received oral message, but can also decrease the time required to translate the

same.

As above-discussed with reference to APPENDIX A, data patterns 405 may be static or dynamic. Similarly, the subset of data patterns may also be static or dynamic. The subset may be defined once, periodically, etc., or it may be defined

and redefined in response to the frequency with which various ones of data patterns 405 are used. For instance, system 100 may determine that subscriber 120 is regularly stopping at “DEF Co.” and modify the above-identified subset

associated with subscriber 120 accordingly. Alternatively, assume subscriber 120 is injured and placed on disability leave. Assume further that the regular stops assigned to subscriber 120 are reassigned to another driver. Under this

scenario, system 100 may associate the data pattern subset associated with subscriber 120 with that of this second driver.

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