1. A surgical tool system comprising:
a surgical tool shaft having means for transmitting a mechanical force and means for delivering electrical energy andor control signals, wherein the surgical tool shaft is hollow and the means for transmitting a mechanical force and means for delivering electrical energy andor control signals are affixed therein; and
a plurality of surgical tool tips wherein each tip is detachably connectable to a distal end of the shaft, wherein at least some of the surgical tool tips include end effectors which mechanically couple to the mechanical force transmitting means and at least some of the surgical tool tips comprise electrical motors which couple to the means for delivering energy andor control signals and to an end effector.
2. A surgical tool system as in claim 1, further comprising a handle attached to a proximal end of the shaft, wherein the handle provides a user interface for controlling the mechanical force transmitting means or the motor.
3. A surgical tool system as in claim 2, further comprising a battery in the handle which is connectable to the motor through wires or a cable in the shaft.
4. A surgical tool system as in claim 1, wherein the motor comprises a piezoelectric element, a DC motor, a stepping motor, a variable-reluctance motor, a wobble-plate motor, an ultrasonic motor, or a friction motor.
5. A surgical tool system as in claim 1, further comprising a gear system between the motor and the end effector in the surgical tool tip.
6. A surgical tool system as in claim 1, wherein the mechanical force transmitting means comprises a rotating shaft, a push rod, or pull wires.
7. A surgical tool system as in claim 1, wherein the end effector comprises a grasper, scissors, a retractor, a needle holder, or a camera.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for determining a native language of a text string associated with a media asset, the method comprising:
undergoing one or more N-gram analyses at a word level to determine a plurality of probabilities of occurrence, each of which correspond to a probability of occurrence of the text string in a particular language, wherein the probability of occurrence of the text string in the particular language is based partly on a type of text string associated with the media asset; and
determining that the native language of the text string is a language that is associated with the highest probability of occurrence out of the plurality of probabilities of occurrence.
2. The method of claim 1 wherein the one or more N-gram analyses at a word level comprises:
for each group of a number N of words in the text string, retrieving a plurality of probabilities, each of which corresponds to a particular language and represents the probability of occurrence of that group of N words in that particular language; and
for each language, calculating a total sum of the retrieved probabilities.
3. The method of claim 2 wherein determining the native language of the text string comprises determining that the native language is a language having the highest calculated total sum.
4. The method of claim 1 wherein the one or more N-gram analyses at a word level comprises a unigram analysis wherein, for each word in the text string, a plurality of probabilities are retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that word in that particular language.
5. The method of claim 1 wherein the one or more N-gram analyses at a word level comprises a bigram analysis wherein, for each group of two adjacent words in the text string, a plurality of probabilities are retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that group of words in that particular language.
6. The method of claim 1 wherein the one or more N-gram analyses at a word level comprises a trigram analysis wherein, for each group of three adjacent words in the text string, a plurality of probabilities is retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that group of words in that particular language.
7. The method of claim 1 wherein the one or more N-gram analyses at a word level comprises any combination of a unigram analysis, a bigram analysis and a trigram analysis, wherein total probability sums are calculated under each such analysis and are weighted differently.
8. The method of claim 1 further comprising separating the text string into distinct words.
9. The method of claim 1 further comprising determining whether each word in the text string is in vocabulary by consulting a table that includes a list of words that are known in all known languages.
10. The method of claim 9 wherein, for each word that is not in vocabulary, undergoing one or more N-gram analyses at a character level to determine a plurality of probabilities of occurrence, each of which corresponding to a probability of occurrence of the word in a particular language.
11. The method of claim 11 wherein the probability of occurrence of the word in the particular language is based partly on the type of text string associated with the media asset.
12. The method of claim 10 wherein the one or more N-gram analyses at a character level comprises:
for each group of a number N of characters in the word that is not in vocabulary, retrieving a plurality of probabilities, each of which corresponds to a particular language and represents the probability of occurrence of that group of N characters in that particular language; and
for each language, calculating a total sum of the retrieved probabilities.
13. The method of claim 10 wherein the one or more N-gram analyses at a character level comprises a unigram analysis wherein, for each character in the word that is not in vocabulary, a plurality of probabilities are retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that character in that particular language.
14. The method of claim 10 wherein the one or more N-gram analyses at a character level comprises a bigram analysis wherein, for each group of two adjacent characters in the word that is not in vocabulary, a plurality of probabilities are retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that group of characters in that particular language.
15. The method of claim 10 wherein the one or more N-gram analyses at a character level comprises a trigram analysis wherein, for each group of three adjacent characters in the word that is not in vocabulary, a plurality of probabilities are retrieved, each of which corresponds to a particular language and represents the probability of occurrence of that group of characters in that particular language.
16. The method of claim 10 wherein the one or more N-gram analyses at a character level comprises any combination of a unigram analysis, a bigram analysis and a trigram analysis, wherein total probability sums are calculated under each such analysis and are weighted differently.