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Customization of Text Messaging Using Historical Patterns to Improve Comprehension Disclosure Number: IPCOM000239254D
Publication Date: 2014-Oct-23
Document File: 3 page(s) / 49K

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The Prior Art Database


Over a period of time, the system monitors the text thread or chat conversation and previous text artifacts of the receiver in a text conversation, and builds a model of the receiving end's conversation style and vocabulary. The system then monitors the sender's outgoing message and translates that text message into a syntax, style and similar vocabulary as the receiving end's model. This will potentially improve the comprehension of the transmitted text or chat message.

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Customization of Text Messaging Using Historical Patterns to Improve Comprehension

It is a well known fact that short text conversations, chat conversations, emails, text remote support interfaces, etc. sometimes fall short of achieving the kind of rich communication offered by face to face discussions. Sometimes this causes frustration or anger and miscommunication between both parties. This ineffective communication may be caused by a mismatch between sender and receiver affective states, different personality types, vocabulary or conversational style. This method means to address this in part by monitoring outgoing text streams and translating the text to be sent into a form more palatable and understandable to the receiver based on the receiver's, vocabulary, conversational style, or understood colloquialisms.



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Receiver Profile Build 1. Receive text 1.1 determine receiver identity 1.2 examine local storage for text artifacts associated with receiver: emails, texts, chats, 1.3 optionally, monitor social platforms for text associated with user (twitter, facebook, etc) 1.4 store receiver text data 2. Text process for Vocabulary set, stems and word frequency 3. Text process for sentence length 4. Text process for sentence construction 4.1. contains dependent and independent clauses 4.2 adjective and adverb use and frequency
5. Text process for best guess locale
6. Repeat and keep improving history and statistics of receiver language

Customization Methods
add homophones ,
age appropriate word sets
harvest receiver text artifacts from facebook , twitter, etc

Vocabulary Translation 1. Get text to be sent 2. Disregarding articles and prepositions select first word 3. Look in Receiver Vocabulary set (performing stemming if necessary) 4. If word is present, it is acceptable 4.1 if not present, consult synonym table for alternatives 4.2 check synonyms for presence in Receiver vocabulary set 4.3 ideally select most frequently used synonym
5. repeat for all important words in message

Sentence complexity adjustment : Simplification by removing clauses
could go both directions but most likely from more complex to less complex
example: Kevin, who is twelve, went to school. adjusts to: Kevin went to school. Kevin is twelve.

1. Get sentence

2. Look in receiver text analysis for average sentence length

3. Compare potential send sentence length to receiver average sentence length

4. If send sentence too long, parse for independent clauses

5. Break sentence into separate sentences.

6. Repeat for remainder of sentences

Sentence complexity adjustment : Simplification by removing idioms example: Its raining cats and dogs.. adjusts to: Its raining heavily.

1. Get sentence 2. Look in receiver text analysis for evidence of idiom use by receiver 3. If idiom is present, it is acceptable
3.1 if not present, consult idiom table for alternatives
3.2 check idiom replacement words for presence in Receiver vocabulary set
3.3 replace idiom replaceme...