EXHIBIT B

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USOO831 1805B2 

(12) United States Patent (10) Patent No.: US 8,311,805 B2 

Parikh (45) Date of Patent: Nov. 13, 2012 

(54) AUTOMATIC DYNAMIC CONTEXTUAL 7,644,102 B2 * 1/2010 Gaussier et al. ....... TO7/999.104 

DATA ENTRY COMPLETON SYSTEM 7,650,348 B2 * 1/2010 Lowles et al. .......... TO7/999.101 

7,657.423 B1* 2/2010 Harik et al. ....................... TO4/9 

7,679,534 B2 * 3/2010 Kay et al. ....... ... 341 (22 (76) Inventor: Prashant Parikh, New York, NY (US) 8,036,878 B2 * 10/2011 Assadollahi .................... TO4/10 - 2004/0093.557 A1* 5, 2004 Kawatani ....... T15,500 

(*) Notice: Subject to any disclaimer, the term of this 2004/0153963 A1* 8/2004 Simpson et al. ........... 715/500.1 

patent is extended or adjusted under 35 2005/0060448 A1 3/2005 Gutowitz ........................ 71O/72 

U.S.C. 154(b) by 1245 days. OTHER PUBLICATIONS 

(21) Appl. No.: 11/422,939 Matiaseketal. “Exploiting Long Distance Collocational Relations in 

PredictiveTyping”. Proc. of EACL-03 Workshop on Language Mod 

(22) Filed: Jun. 8, 2006 eling for Text Entry Methods. 2003.* 

O O Witten et al. “The Zero-frequency problem: estimating the probabili 

(65) Prior Publication Data ties of novel events in adaptive text compression'. IEEE Transactions 

US 2006/0217953 A1 Sep. 28, 2006 on Information Theory, vol. 37, No. 4. (1991), p. 1085-1094.* 

k . Related U.S. Application Data cited by examiner 

(63) Continuation-in-part of application No. 11/040,470, Primary Examiner — Jesse Pullias 

filed ed. On on Jan. Jan. 21, Zl, 2005 74). A ttorney, ey, Ag ent, or Firm Fi — Wei Weltzman Law Law Offi CeS, 

LLC 

(51) Int. Cl. 

G06F 7/27 (2006.01) (57) ABSTRACT 

G06F 7/20 (2006.01) A method, performed in a character entry system, for inter G06F 5/00 (2006.01) relating character strings so that an incomplete input charac 

(52) U.S. Cl. ........ grgr. 704/9: 704/1: 704/10 ter string can be completed by selection of a presented char 

(58) Field of Classification Search .................. 715/256, acter string involves computing relationship scores for 

715/261, 271; 707/737 individual character strings in the system from documents 

See application file for complete search history. present in the character entry system, in response to inputting 

of a string of individual characters that exceeds a specified (56) References Cited 

U.S. PATENT DOCUMENTS 

threshold, identifying at least one selectable character string from among contextual associations that can complete the 

input character string in context based upon an overall rank 

S. A : g E. R al . . . . . . . . . . . . . . . 36."i. ing score computed as a function of at least two other scores, s--- (- C. C. a. . - 0 7,039,631 B1* 5/2006 Finger, II .......................... 707/3 and providing the identified at least one selectable character 

7,111.248 B2 * 9/2006 Mulvey et al. 715812 string to a user for selection. 

7,149,695 B1* 12/2006 Bellegarda ... 704/275 

7,218,249 B2 * 5/2007 Chadha ........................... 341 (23 15 Claims, 6 Drawing Sheets 

Matrix 

500 Y 504 506 508 510 512 514 516 |- |- - - - slot Finance Summary Sugar Two One Chapter Chili Spoon 

5O2 Finance 12 2 3 5 O O 

Summary 3 6 10 O O 

Sugar 7 9 O 3 14 

Two 3 5 8 11 

One 6 8 19 

O O 

 

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U.S. Patent Nov. 13, 2012 Sheet 1 of 6 US 8,311,805 B2 

110 

Other input 

Documents 

Character 14O 160 Input 

Su ested a 

9. r Cont 

Completion S A r O 170 ( SSociations 15 

Completed / FIG. 1 

Input 18O 

 

 

 

 

 

 

 

 

 

 

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U.S. Patent 

Offline 

or Online 

Computation 

FIG. 2a 

Nov. 13, 2012 Sheet 2 of 6 US 8,311,805 B2 

Create list of pertinent documents on 

the device. 1 200 

Create list of unique words from 

documents. 

(optionally) Remove stop words from 

Word list. 

For each document, Count number of 

occurrences of each Word in Word list. 

hu 205 

h 210 

U215 

Store in a matrix of documents vs. 

Words. 

Calculate the similarity value for all 

possible pairs of documents using 

matrix. 225 

Compare similarity values to the 

threshold value. 1U230 

Discard document pairs whose 

similarity value falls below specified 

threshold value. us 

Form groups of documents, using remaining document pairs, such that 

similarity value of all possible pairs in 

each group is above the threshold 

value. Words within each similar 

document group are contextually 

related. 

240 

Create lists of unique words from each 

group of similar documents. 1U 245 

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U.S. Patent Nov. 13, 2012 Sheet 3 of 6 US 8,311,805 B2 

250 

Accept characters from the user until the threshold? 

number of required characters are entered. 

ldentify relevant document groups from entered 55 

characters. 

Online 

Computation 260 - Using the identified document groups, choose words Y\U 

that are contextually associated with and match the 

characters entered. 

Offer the chosen contextually associated words to 

the user for selection to complete the entry. 

The user accepts or rejects the offered contextually associated words and the process repeats with the 

beginning of the next word entry. 

270 

FIG. 2b 

 

 

 

 

 

 

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U.S. Patent Nov. 13, 2012 Sheet 4 of 6 US 8,311,805 B2 

3O2 

312 314 316 

Matrix 506 508 510 512 516 500 Y 594 - 514 

Finance Summary Sugar Chapter Chili 

502 Finance 12 1 2 5 

Summary 

Sugar 

TWO emrm 

One 

Chapter 

Chili 

Spoon 

Matrix 

600 Na 

602 - Word List it 1 finance, report, summary, ... 

F G 6 Word List i2 chili, one, salt, spoon, sugar, two, three, ... 604/, Word List i3 book, chapter, summary, two, three, ... - 606 Word List if A apple, cider, orange, ... 608 Word List i5 Orange, paper, ... 

 

 

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U.S. Patent Nov. 13, 2012 Sheet 5 of 6 US 8,311,805 B2 

Create list of pertinent documents on the device. r 400 

ra-i- is a ri-...- ...---- far Create list of unique words from O documents. - 41 

(optionally) Remove stop words 

from Word list. r 420 

Offline or Count frequency of co 

Online OCCurrence, Within a unit, across 430 Computation documents for all possible pairs r 

of Words from list. 

to frequency matrix. results into a ruA40 

Use matrix to identify word pairs that are contextually associated 

based on their frequency of co- 1U 50 

OCCUSC6. 

FIG. 4a 

 

 

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U.S. Patent Nov. 13, 2012 Sheet 6 of 6 US 8,311,805 B2 

Accept characters from the user until the threshold 

number of required characters are entered. 

ldentify relevant words in the matrix from entered 

characters. 

Online 

Computation 

Using identified words in matrix, choose words that 

are contextually associated. 

Offer the chosen contextually associated words to 

the user for selection to complete the entry. 

ru 

48O 

FIG. 4b. 

 

 

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US 8,311,805 B2 

1. 

AUTOMATIC DYNAMIC CONTEXTUAL 

DATA ENTRY COMPLETON SYSTEM 

CROSS REFERENCE TO RELATED 

APPLICATIONS 

This application is a continuation-in-part of and claims the 

benefit of priority of U.S. patent application Ser. No. 11/040, 

470 filed Jan. 21, 2005, the entirety of which is incorporated herein by reference as if fully set forth herein. 

FIELD OF THE INVENTION 

The present invention relates to information processing and, more particularly, computer, cellphone, personal digital 

assistant, or other similar device-based text entry. 

BACKGROUND OF THE INVENTION 

In modern life, there are a number of devices, notably digital computers and multifunctional handheld units that 

involve data entry, typically text, including for example cel 

lular phones and other devices like organizers and handheld 

computers. For all of these, one important use is the entry of 

linguistic items like words, phrases, and sentences. For 

example, a user may create an unstructured text document or 

might formulate an email message or a short text message to 

be sent as an SMS message on a cellphone. In such cases, text 

entry may occur through use of a keyboard or stylus for some 

handheld computers or cellphones, etc. However, data entry 

can be difficult when the keyboard is relatively small as it is on 

a handheld cell phone, organizer or computer, or uses indi 

vidual keys for entry of multiple letters, text, especially when 

a large number of characters must be entered. Similarly, with 

devices employing a stylus for text entry, entry of text can be 

slow and burdensome. 

Automated word completion programs have eased the bur 

den somewhat. Such automated word completion programs have appeared recently in a variety of applications in a variety 

of devices. These programs are typically based on either 

predefined word Suggestion lists (e.g. a dictionary) or are 

culled from the user's own most recently typed terms, the 

latter often called MRU (i.e. “Most Recently Used') pro 

grams. For example, the former type of program is based on 

a pre-given word Suggestion list based on a dictionary aug 

mented with information about which words are more fre 

quently used. If a user types the characters “Su” in a docu 

ment, then it might Suggest 'super as the appropriate word 

completion based on the fact that it belongs to the pre-given word Suggestion list and has a high frequency of use in gen 

eral English. On the other hand, the latter type of program would suggest a word completion based on the user's own 

recently used words (e.g. 'Supreme' may be suggested to a 

lawyer who has recently input “Supreme Court'). Such pro 

grams are often found in web browsers for example and will 

Suggest the most recently used “uniform resource locator” or 

URL (e.g. www.google.com when the user types "www.g.) 

as characters are input. 

A third type of program is able to detect that the user is in 

a particular type of field (e.g. the closing of a letter) and will 

Suggest word completions (e.g. “Sincerely when the user 

types “Si') based on a more limited “contextual” list. An 

extension of this is to maintain many separate word Sugges tion lists and allow the user to choose an appropriate list for 

each document the user creates. Other variants allow users to 

actually insert entries manually into word Suggestion lists 

(e.g. a name and address) or to maintain frequencies of word 

10 

15 

25 

30 

35 

40 

45 

50 

55 

60 

65 

2 

usage by a user and thus, rather than offering the most 

recently used word, offer the user's most frequently used 

words. 

SUMMARY OF THE INVENTION 

While the methods delineated above have many useful 

features, there is still a lack of a true context based system that 

is dynamic and automatic and thus, there is still much room 

for improvement when it comes to data entry in Such devices. 

Systems that maintain separate word lists and allows the user 

to choose an appropriate list are contextual to some degree, 

but still have the drawback of requiring the user to make a list 

selection each time, something that can become annoying for 

a user who typically creates several documents within the 

course of a single day. Moreover, separate word Suggestion lists are still inefficient because they are not automatically generated but instead depend on the user's guidance and 

input. 

The present invention combines certain features from 

existing techniques but goes significantly beyond them in 

creating a family of techniques that are automatic, dynamic, 

and context based as explained in greater detail herein. 

The present invention involves a method, performed in a 

character entry system. The method is used for interrelating character strings so that incomplete input character strings 

can be completed by a selection of a presented character 

string. The approach involves computing contextual associa 

tions between multiple character Strings based upon co-oc 

currence of character Strings relative to each other in docu 

ments present in the character entry system, identifying at 

least one selectable character string from among the com 

puted contextual associations that can complete the incom 

plete input character string in context (performed in response 

to inputting of a specified threshold of individual characters), and providing the identified at least one selectable character 

string to a user for selection. 

The advantages and features described herein are a few of 

the many advantages and features available from representa tive embodiments and are presented only to assist in under 

standing the invention. It should be understood that they are 

not to be considered limitations on the invention as defined by the claims, or limitations on equivalents to the claims. For 

instance, Some of these advantages or features are mutually exclusive or contradictory, in that they cannot be simulta 

neously present in a single embodiment. Similarly, some 

advantages are applicable to one aspect of the invention, and 

inapplicable to others. Thus, the elaborated features and 

advantages should not be considered dispositive in determin 

ing equivalence. Additional features and advantages of the 

invention will become apparent in the following description, 

from the drawings, and from the claims. 

BRIEF DESCRIPTION OF THE FIGURES 

FIG. 1 illustrates, in simplified form, a top-level flowchart 

for the automatic completion of character input using contex 

tual word associations; 

FIG. 2a illustrates a simplified flowchart for computing contextual associations in one example implementation of the 

invention; 

FIG.2b illustrates a simplified flowchart for the selection 

of contextual associations in an example implementation of 

the invention; 

FIG. 3 illustrates an example documents versus words 

matrix used to compute contextual associations with an 

example implementation of the invention; 

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3 

FIG. 4a illustrates a simplified flowchart for computing 

contextual associations in an alternative example implemen 

tation of the invention; 

FIG. 4b illustrates a simplified flowchart for the selection 

of contextual associations in an alternative example imple- 5 

mentation of the invention; 

FIG. 5 illustrates an example matrix of pairs of words used 

to compute contextual associations in the alternative example implementation of the invention; and 

FIG. 6 illustrates an example set of word lists for a word 10 

completion example involving the alternative example imple 

mentation. 

DETAILED DESCRIPTION OF THE INVENTION 

15 

The present invention can be used with a variety of elec 

tronic devices. The minimum requirements for any Such 

device are some means for accepting textual input from a user, 

one or more processor(s) that execute stored program instruc 

tions to process the input, storage for the data and the program 20 

instructions and a display or other output device of some sort 

to make output visible or available to the user. Representative, non-exhaustive, example input devices can include, but are 

not limited to, a keyboard, a handwriting recognition system 

that makes use of a stylus, a touchpad, a telephone keypad, a 25 

pointing device like a mouse, joystick, trackball or multi 

directional pivoting Switch or other analogous or related input devices. The storage preferably includes non-volatile 

memory, and can also include Volatile semiconductor-based 

memory, electro-magnetic media, optical media or other 30 

types of rewriteable storage used with computer devices. If a 

display is used, the display may be small and capable of 

displaying only text or much larger and capable of displaying 

monochrome or color images in addition to text. If another 

output device is used, like a text to speech converter, appro- 35 

priate implementing equipment will be included. Although described, for purposes of clarity, with reference to keyboard 

type entry, it is to be understood that the present invention is 

independent of the particular mode of, or device used for, text 

data entry. 40 

At the outset, it should be noted that, for the purposes of 

this invention, a "document” as used herein is intended to be 

a very general term covering one or more characters, whether 

alone or in conjunction with numerals, pictures or other 

items. A document’s length can vary from a single “word to 45 

any number of words and it can contain many types of data 

other than words (e.g. numbers, images, Sounds etc.). Thus, ordinary documents such as pages of text are documents, but 

so are spreadsheets, image files, sound files, emails, SMS text 

messages etc. 50 

As noted above, a “word,” for the purposes of this inven 

tion, can be considered to be more than a string of alphabetic characters, it may include numeric and other symbols as well. 

Broadly, the invention provides contextual completion of 

character strings, where a character string includes not only 55 

alphabetic words but any other discrete collection of charac 

ters, symbols, or stroke based pictographs or ideograms, for 

example, those used in languages like Chinese, Korean and 

Japanese, and thus can benefit from use of the present inven 

tion. Thus, although for simplicity the term “word is used in 60 

the following discussion, it should be understood to encom 

pass any discrete collection of characters, symbols or other 

stroke based representations of communicative concepts, thoughts or ideas. Thus, the present invention, although described with reference to English, is independent of any 65 

particular language. It can be used for phonetic, pictographic 

or ideographic languages when the characters, pictograms or 

4 

ideograms used therein (or “stroke' components thereof) are 

considered “words' and thereby are intended to be incom 

passed by the terms “text' and “textual.” In some cases, an 

entire pictogram or ideogram will be usable as a “word as 

described herein with entry of a component of the pictogram 

or ideogram, Such as a defined stroke, being analogous to 

entry of a letter in English. Likewise, for simplicity in the 

following examples, the terms “typing or “typed are used to 

describe data entry. However, those terms should be broadly 

read to encompass any and all methods of data entry, whether 

involving entry through use of a keyboard, a pointing or 

selection device, a stylus or other handwriting recognition 

system, etc. They are not in any way intended to be limited 

only to methods that make use of a typewriter-like keyboard. 

Examples of devices that can use and benefit from incor 

poration of the invention therein range from large computer 

networks, where an implementation of the invention may be 

part of or an application on the network, to Small portable 

hand held devices of more limited or specialized function 

Such as cell phones, text messaging devices and pagers. Implementations incorporating the invention can be used to 

assist users in interacting with large databases by helping in 

the entry of search terms or in data entry. Other implementa tions incorporating the invention are particularly useful for 

portable devices, in which the input device is limited by size 

and difficult to work with, because the automatic completion of character string entries provides greater benefits in Such 

devices. Still other implementations incorporating the inven 

tion are particularly useful for devices used by those with 

physical handicaps. In addition to the methods of character 

input already mentioned, devices intended for use by handi 

capped individuals may rely on Some type of pointing device 

to select individual characters for input. The pointing device 

may be controlled by movement of the eyes, head, hands, feet 

or other body part depending on the abilities of the particular individual. The present invention may also be used with 

“text that is implemented in braille or other tactile represen tations for individuals with impaired vision. 

In overview, in connection with the invention, words from 

one or more documents are associated, in either a fully or 

partially automated way, based on context. Context is derived 

from the co-occurrence of individual words in documents. In 

addition, the associations can be pre-computed and static or 

dynamic So they can thereby evolve and improve with con 

tinued use. 

For example, in an implementation of the invention, an 

association between “finance' and "Summary may be gen erated but not one between “finance' and “sugar; in this case, if a user has typed in the word “finance' followed by the 

characters "su, then, based on the association, the invention 

will suggest 'summary as the appropriate word completion 

rather than “sugar.” Here, the word “finance' has provided the 

context that suggests the appropriate completion; if instead 

the user had typed “two spoons of and then the characters 

“Su” and if an association had been generated between, “spoon' and “sugar rather than “spoon' and “summary” 

then the invention would suggest "Sugar as the contextually appropriate completion. As more words are entered in the 

document, the contextual associations become richer. 

The invention permits the use of different techniques for 

actually creating the associations. As a result, for purposes of 

understanding, two fully automated example techniques are 

described below with the understanding that semi-automatic 

implementation techniques are considered to be literally the 

same as the fully automated ones. The automatic or manual 

nature of a technique is, in most respects, independent of the 

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5 

invention because it relates more to the ease of processing large amounts amount of text, not the technique itself. 

The general approach is illustrated, in simplified overview, in FIG. 1 with respect to a single document. The approach begins with a device Such as a personal digital assistant, cell 

phone, computeror other device (100,110, 120 or 130) which 

has documents (140) stored in its memory. These documents 

are used to create associations (150) between pairs of words 

or character strings within the document and use these asso 

ciations to suggest word or character string completions (170) 

to the user entering text (160) in a document. The associations 

among the words or strings may be static or dynamic. With 

implementations incorporating a more dynamic approach, as 

the user adds to a document or creates more documents on the 

device, the associations are recomputed or Suitably aug 

mented. This will alter the set of associations by either adding 

new associations, deleting existing associations or both. 

Thus, with implementations of the automatic contextual word 

completion system having this 'dynamic aspect, the system 

evolves as the user adds to or creates new documents and thus 

generally improves with use. Extensions to these implemen 

tations further allow the device to impliedly track the user's 

evolving interests. 

Associations between words can be computed in a variety of ways and, as non-limiting examples, two alternative auto 

matic methods of doing so are described. 

In the first method, the first step is to assess the similarity of 

words within one document or from one document to other 

documents that may exist on the user's device. In this method, contextual associations are arrived at by grouping documents 

based on similarity and creating lists of words that are com 

monto each group. There are many known methods to assess 

document similarity including the Jaccard, Dice or cosine 

coefficients and the K-vec methods. For purposes of expla nation, one such example similarity assessment method, 

based on treating documents as vectors in a multidimensional 

space, is used, it being understood that, depending on the 

particular implementation, other similarity assessment meth 

ods can be used in addition to, or instead of those used in the 

examples described herein for practical reasons. 

This example method is outlined in the flowcharts in FIGS. 

2a and 2b. The method starts by creating a list of all the 

pertinent documents (200) on the device. From this list of 

pertinent documents a list of unique words is created (205). 

An optional step, is to remove stop words from the word list 

(210). Stop words are described in greater detail below but 

include words like “the “at” and “in” For each word in the 

word list, the number of times it occurs in each document is 

counted (215) and this number is stored in a matrix of docu 

ments vs. words (220). This matrix is used to calculate a 

similarity value (225) for each possible pair of documents in 

the document list. The similarity value for each document pair is compared to a threshold value (230) and those document 

pairs whose similarity value falls below the specified thresh 

old value are discarded (235). The remaining document pairs 

are used to group documents such that the similarity value of 

each possible pair in each group is above a specified threshold 

value (240). Lists of unique words from each group of similar 

documents are created (245). Words within each of these lists 

are contextually related. The steps of the example method to 

this point may be carried out independently of user text entry 

or, in implementations where the dynamic aspects of the 

invention are utilized, carried out simultaneously with user 

text entry, so that the contextual associations are updated as 

the user enters more words into the device. 

Once at least an initial set of contextual associations exists, it can be used at Some point thereafter. The approach to use is 

10 

15 

25 

30 

35 

40 

45 

50 

55 

60 

65 

6 

as follows. The device accepts character input from the user 

until a specified threshold number of characters has been 

entered (250). Using the entered characters, relevant word 

lists are identified (255). Due to the processing, the words 

within these identified lists are deemed contextually related 

and thus, words in the identified lists having a corresponding initial character string matching the entered characters are 

chosen (260) to be offered for selection by the user to com 

plete the character entry (265). The above referenced process can be fully understood by 

way of the following simplified example. To assess the simi 

larity or dissimilarity of documents, one way of thinking of a 

document that contains one or more words is as a bag or 

multiset of words. A bag or multiset is like an ordinary set in 

mathematics, a collection, except that it can contain multiple 

occurrences of the same element. For example, book, cape, pencil, book is a bag containing four words of which the 

word “book' appears twice. The order of occurrence of ele 

ments in a bag does not matter, and could equally be written 

as book, book, pencil, cape. Also, any bag can be converted 

to a set just by dropping multiple occurrences of the same 

element. Thus, the example bag above, when converted to a 

set, would be book, cape, pencil. To create the bag or 

multiset, the contents of a document with the exception of 

numbers which are a special case are stripped of all internal 

structure (e.g. Syntactic structure, punctuation etc.) including 

all non-lexical items like images, sounds etc. The resulting stripped document would be a bag or multiset of words as 

described above which may also include numbers and in 

which some words may occur multiple times. For a user who 

has a device with a number of stored documents, each perti nent document is similarly stripped down to form bags and 

the mathematical union of these bags can be taken to form a 

larger bag. 

As a side note, optionally, a certain class of words, typically 

called “stop words.” are removed from such document-de 

rived bags. Stop words are words like “the “of” “in” etc. and 

are removable because they usually are not very informative 

about the content of the document. Stop words, if removed, 

can be removed from the bags either before or after a math 

ematical union of the bags is made, as the end result is the 

same. Typically stop words are identified in a list which can 

be used for the exclusion process. Since the stop word 

removal process is well known it is not described herein. In 

addition, in some implementations where a stop word list is 

used, the list may be editable so that additional words can be 

defined as “stop words. For example, otherwise non-trivial 

words that are trivial in the particular context because they 

occur too often in that context (e.g. words like “shares' in 

stock related government filings). 

By way of simplified example (FIG. 3), if the user has just 

two documents on a device: “d 1’ (306) made up of “an apple, apple cider and an orange' and “d2 (308) made up of “a 

paper apple' then, each corresponding bag is apple, cider, apple, orange and paper, apple. Their union is the larger bag apple, cider, apple, orange, paper, apple and a set for 

the bag would be apple, cider, orange, paper. 

A matrix (300) is then formed with for example, each 

element in the set of words derived from the documents on the 

user's device listed along the columns (302) of the matrix and 

each document itself (symbolized in Some way) along the 

rows (304) of the matrix. In the cell corresponding to the 

intersection of a document 'd' with a word “w, the number 

of times “w” occurs in “d” is entered (318). For the simple example above, as shown in FIG.3, for the cell corresponding 

to the intersection of the row for the first document “d 1’ and 

the column for the word “apple” a “2 (318) is entered since 

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7 

it occurs twice in document “d 1.” This occurrence frequency information is obtained from the document bags. If a word 

does not occur in aparticular documentatall, a Zero is entered 

in the corresponding cell. Note that depending upon the num 

ber of documents and the number of words, the size of the 

matrix can be exceedingly large. Moreover, there is no sig nificance to whether rows list documents and columns list 

words or vice versa—the contents of the rows and columns 

could be exchanged without affecting the invention. 

Once the matrix is created, each document is treated as a 

vector in a multidimensional Euclidean space, with the num 

ber of dimensions being the number of words or columns of 

the matrix. Thus, the simplified example of FIG. 3, each of 

documents d1 and d2 can be treated as a four dimensional 

vector since there are four elements in the corresponding set 

apple, cider, orange, paper. Notably, by using this 

approach, the words can also be listed in any order in the 

columns, for example, in order of first or last occurrence, randomly, alphabetically, by number of letters, stroke type, 

stroke count, etc., in forward/increasing or reverse? decreas 

ing order. 

The similarity between d1 and d2 is then estimated by measuring the cosine of the angle between vectors d1 and d2. 

The higher the cosine value, the greater the similarity, the 

lower the cosine, the greater the dissimilarity. If the value is 

the maximum possibility of “1,” the vectors are fully aligned in multidimensional space and the two are identical. On the 

other hand, if the cosine reaches its minimum of “0” the 

vectors or documents are completely dissimilar (i.e. they are 

orthogonal). For the absolute intermediate cosine value of 

“0.5, the documents are roughly as similar as they are dis 

similar. 

This method can thus be extended where more than two 

documents exist, by pairing the documents in all unique com 

binations and performing the same similarity analysis on all 

pairs of documents that are pertinent (i.e. under consider 

ation) from the device of the user. Note that it is likely that 

every “document' on the user's device may not be “pertinent' for purposes of this invention, for example, it will likely be 

desirable to differentiate between the user's documents and 

documents that may be present for other reasons (e.g. pro 

grams, system configuration file contents, system data, etc.) 

and thus irrelevant. For purposes of understanding the inven 

tion, it should be assumed that non-pertinent documents have 

been excluded in Some manner, whether manually or auto 

matically. 

As to all of the pairs, cosine values are computed and a 

threshold value is chosen. The threshold value represents a 

divider in which all cosine values above the threshold are 

accepted as indicating meaningfully similar document pairs 

and below which the document pairs are sufficiently dissimi 

lar so as to be considered not meaningfully related. In other 

words, a range of values is taken and on the basis of those 

values, an arbitrary division is formed to differentiate, and 

deem each document, meaningfully similar or dissimilar rela 

tive to each other document. 

In application, the threshold value mentioned above is 

initially a preset parameter, the value of which may be differ 

ent for different types or classes of target users. However, in 

Some implementations, the threshold can be adjusted, 

whether over time through use or because of changes in the 

quantity or contents of the pertinent documents, and either by 

program modification or through user action. In other imple mentations, it can be adjusted automatically or by implication 

as is discussed in greater detail below. 

In all cases, what is obtained, either through the vector 

space method described above or through other analysis 

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8 

approaches, such as Jaccard, Dice or cosine coefficients, the 

K-vec methods or some other method, is a judgment of simi 

larity and dissimilarity of document pairs in the pertinent 

document collection or set. 

This similarity judgment is then used to form groups of 

documents, each group of which contains only documents 

that are Sufficiently similar to one another when compared in 

a pair wise fashion. Note that the relationship of similarity is 

reflexive and symmetric, but it is not necessarily transitive. 

This means that the groups may not be disjoint i.e. the same 

document may belong to more than one group, particularly in 

implementations where a document need not be sufficiently similar to every other member of the group, but only some 

specified portion thereof. In other words, as a result of the 

grouping, two or more groups will be formed wherein each 

document is meaningfully similar to at least Some specified portion of the other documents in that group. In general, each 

group will have the following characteristics: each document 

in the group will be sufficiently similar (i.e. be above the 

threshold) with respect to at least some specified portion of 

the group (in practice, likely preset at well more than 50% if 

stop words are not removed), each group will be differen 

tiable from any other group by at least one member, and each 

group will not be a simple Subset of any other group. 

In addition, Some implementations may involve large num 

bers of nearly identical documents such that the common 

information is not meaningful because it is ubiquitous and the 

differentiating information may be too unique relative to each 

other to be of interest. One such example, would be certain 

filled out forms. In Such a case, a second very high threshold 

can be used as a "screen” whereby copies of documents 

exceeding this threshold are separately grouped, the math 

ematical intersection of the documents are used to form a 

“stop word’’-like list, the intersection-based stop word list is 

then used to strip the copies of those words, and then the 

copies are used in place of the actual documents in the overall 

grouping process. In this manner, the ubiquity can be handled 

without sacrificing the potential contextual relevance of the 

unique information to other pertinent documents. 

Once groups of similar documents have been made, lists 

are formed for each group containing all non-stop words in 

the union of the sets corresponding to each document in the 

group. As with the lists above, these lists may be arranged in 

any of a variety of possible orders (e.g. alphabetical, first/last 

occurrence, etc.). In addition, the lists can dynamically change, for example, by repeating the above processing (in 

whole or part) as time progresses and the user creates/modi 

fies/deletes documents. 

As a result, the lists created as described above are essen 

tially lists of all words associated with each other i.e. once the 

lists have been computed, the associations mentioned earlier 

have been computed as well. By way of simple example with 

respect to the two documents d1 and d2 introduced above, if 

a list contains the words apple, cider, orange} then it can be 

assumed that these words are associated with each other. The 

word “paper from d2 above is not on this list and so is not 

associated with “apple.” “cider” or "orange' through this list, 

but may be associated with apple through another list “d3” 

that contains apple, orange, knife, paper, plate. It may also 

happen that there also is another list “d4, based on another 

group of documents, that contains orange, paper, peel in 

which case "orange' and “paper” would be associated with 

each other through that list as well as through “d3. 

Thus, based upon the above, if a user were to type “apple' 

followed by the characters “ci, then the program instructions 

implementing this aspect could check the lists, find that 

“apple' and “cider are associated through a list, and thus 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 12 of 18
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9 

offer “cider to the user as a possible completion of the 

incomplete word. Similarly, if a user were to type orange 

followed by “p', then “paper” and “plate” would be related to 

"orange' through list “d3 and “paper and "peel through 

“d4. Depending upon the next letter entered, the “contextu 

ally related contents of one of the lists would be excluded or 

the one word in common “paper would be provided as the 

“contextually related completion word. 

In the first of the immediately preceding examples, a single 

word “apple' is used as the basis for choosing “cider” as the 

possible completion. Whether a single word, multiple words, 

sentences or even longer segments of text are used as the basis 

for choosing the possible word completion is dependent on 

the particular implementation. For simple devices, limita 

tions on memory and processing resources will likely dictate 

that no more than a few words of prior user input be used as 

the basis for choosing the word completion. In other imple 

mentations this could be a user selectable parameter, a preset 

parameter in the device or could be adjusted dynamically by the program. 

At this point it should be understood that document simi 

larity is based on what words, if any, are shared by each of the 

pertinent documents and in what frequencies when similar 

documents are grouped and used to form lists. Thus, by grouping "similar documents, an assumption is made from 

the similarity analysis that words that belong to the same 

broad subject matter are being collected i.e. 'similar docu 

ments are necessarily likely to contain words that pertain to 

the same Subject matter. Hence, the derived associations are 

likely to be more relevant to a user who is typing a document 

on that subject than other alphabetically similar or most 

recently used, but non-associated, words. 

While the formation of groups is based on the calculated 

similarity value between pairs of documents, the details of 

how the similarity value is used can vary depending on the 

particular implementation of the invention. For example, as 

defined above, all document members of a group must have a 

similarity value above the set threshold value when compared 

to some portion of the other member of the group. However, 

that “portion' can be more or less restrictive. The most 

restrictive definition requires the portion be all members of a 

group and leads to the largest number of groups and devel 

opment of word lists that tend to be shorter but the contextual 

associations among words in the list tend to be stronger. The 

least restrictive definition of a group is that any one document 

of the group need only have a similarity value above the 

threshold value when compared with any one other member 

of the group. Although it will form the fewest groups, this 

definition will, for practical purposes, never be used. In prac tice, if a pre-set threshold is specified, it will likely be set so as 

to have a reasonable number of groups while having mean 

ingfully usable relationship lists. As currently contemplated, 

an initial threshold within the range of 50% to 90% would be 

used as a reasonable empirical starting threshold. Notwith 

standing the foregoing, the threshold can be initially set to a 

value outside that range or can, in Some implementations, ultimately end up outside that range either through evolution 

or affirmative changing of the setting. Regardless of how 

restrictive the definition of a group is, whenever a new docu 

ment is found to have a similarity value that is below the set 

threshold when compared to all of the existing documents on 

the device, that document becomes the first member of a new 

group. When less restrictive definitions of a group are used, 

groups, and their associated word lists, become larger and the 

strength of the contextual associations between words in the 

list becomes weaker. Thus, the threshold will clearly be a 

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parameter that, even with its initial value, will vary with the 

particular implementation of the invention. 

It will now be apparent that the threshold value for simi 

larity plays an important role in the formation of groups and 

thus the lists of contextually associated words. The ability to 

distinguish between closely related documents will also 

increase as the threshold value is set higher. Therefore a high threshold value may be appropriate for an attorney who spe 

cializes in real estate matters and who creates documents, 

Such as sales contracts or leases, which have a high degree of 

similarity, while a low threshold value may be appropriate for 

an editorial writer who produces documents on a wide variety of general interest topics. 

The threshold value may be set by the designer of the 

device depending on the intended use, selectable by the user 

or set dynamically by the program implementing the inven 

tion. One alternative implementation method which, in effect, dynamically sets the appropriate threshold value involves 

specifying the maximum number of groups that can be cre 

ated. By doing so, the system will be required to start at a 

relatively high threshold value and, if the resulting number of 

groups exceeds the specified maximum, it will adjust the 

threshold downward until the maximum number is not 

exceeded. If no more than five groups are specified then the 

threshold value will be lower than if no more than 20 groups 

are allowed. The program implementing the invention can be 

further optionally configured to, as documents are added/ 

changed/deleted, monitor the number of groups and adjust the 

similarity threshold value so that the number of groups is 

continually within the specified limit. Additionally, the limit 

can be made variable, for example based upon the number of 

documents in the system. In this manner, once a greater 

threshold number of documents is exceeded, a new maximum 

can become effective so as to allow for more groups and 

maintain a meaningful granularity. 

Another optional additional method that may be used by the program implementing the invention, is to dynamically 

set the similarity threshold value by monitoring the perfor 

mance of the word completion system. For optimal perfor 

mance of an automatic word completion system, the system 

must strike a balance between the case of rarely offering a 

word completion but the completion offered is always appro priate, and the case of always offering a word completion but 

the completion offered is often inappropriate. By monitoring how often the system offers a possible word completion to the 

user and how often the user accepts the offered word comple tion, the program can determine if the strength of the contex 

tual associations is too high or too low. If the user is frequently having to discard Suggested word completions, the program 

can increase the strength of the contextual associations by raising the threshold value or, as discussed above, making the 

definition of a group more restrictive or both. 

Yet another optional use of the similarity threshold value is 

to rank possible word completions so that the most likely 

word completion is offered to the user first. The ranking is 

accomplished by computing the word associations using dif 

ferent similarity threshold values. In implementations of the 

invention where the user is offered multiple possible word 

completions, words that are contextually associated with the 

partial input based on use of a high threshold value would be 

offered at the top of the list, while those words that are only contextually associated based on the use of a low threshold 

value, would be offered at the bottom of the list. 

To further illustrate how different documents may be 

grouped, consider a collection of four documents, a draft of 

the Declaration of Independence, the signed version of the 

Declaration of Independence, the United States Constitution, 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 13 of 18
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and an article reviewing the foundation of United States con 

stitutional law. In the case of a user who has documents 

covering widely different topics such as the history of base 

ball, art and literature a relatively low similarity threshold 

may be set so that there are only four groups of documents. 

The Declaration of Independence, Constitution, and consti 

tutional law article would likely all be in one group as they are 

clearly related documents but would not be related to docu 

ments on baseball, art or literature. In the case of another user 

who happens to be an attorney and who has only legal docu 

ments on his device the similarity threshold value would have 

to be set higher so that different classes of legal documents 

could be distinguished. If the threshold value were set too low, then the fact that words such as “liberty' are present in all four 

of the documents in our example collection, would be enough for all four documents to be placed in the same group. With a 

higher threshold value, words like “creator, which only 

appears in the Declaration of Independence, and “Senate.” 

which only appears in the Constitution and the constitutional 

law article, would result in these documents being placed in 

two different groups, one on the Declaration of Independence 

and one on the Constitution. 

A second example method for computing contextual asso 

ciations between words, suitable for use with the present invention, is outlined in the flowcharts of FIGS. 4a and 4b. As 

in the first example method, the second method begins by creating a list of pertinent documents on the device (400) and 

creating a list of unique words from these documents (410). Again, an optional step is the removal of stop words from the 

word list (420). For all possible pairs of words from the list, 

the frequency of co-occurrence withina unit of text is counted 

(430) and this result entered into a matrix (440). The particu lar “unit' that is chosen will be a parameter to be decided 

based upon various factors present for the particular imple mentation. Generally, if the documents being considered are 

fairly long (such as a thesis, book or report), then a paragraph is typically considered as the appropriate unit; if the docu 

ments being considered are fairly short (e.g. like an SMS 

message on a cell phone or an e-mail), the whole document 

may be considered as a unit. Other “unit options are, for 

example, the sentence, sections of a document, chapters, 

pages columns, etc. The unit parameter may be initially fixed, 

permanently fixed, modifiable or dynamic. In fact, in some 

implementations, the unit may even be allowed to change 

based on the length of the document and the portion involved. 

For example, for patent documents, the appropriate “unit for 

the abstract might be the paragraph, for the claims it might be 

the line or sentence, and for the detailed description it might be, for example, a section, column, paragraph or sentence. 

The rows and columns of the matrix are lined with words 

from the word list. As before, the interchange of rows and 

columns changes nothing with respect to the invention. The 

matrix containing the co-occurrence data is used to identify word pairs that are contextually associated, based on their 

frequency of co-occurrence. The steps of the method to this 

point can be performed independently of user text entry or, in 

implementations where the dynamic aspect of the invention 

are realized, these steps can be performed simultaneously 

with user text entry so that the contextual word associations 

are updated as the user enters new text into the device. 

Once the contextual word associations have been deter 

mined, the device accepts character input from the user until 

a threshold number of characters are entered (460). Words in 

the matrix that match the entered characters are identified 

(470) and the identified words that are contextually related are 

chosen (480) and offered for selection by the user to complete the character entry (490). 

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12 

Note that, as described above, the matrices in FIGS. 3 and 

5 have been filled with direct co-occurrence information. 

However, this is not required because there are many other 

variants of this approach that can be used to the same, better, 

or in some cases less, effect (Oust as there are many different 

ways of measuring document similarity) depending upon the 

particular implementation and documents involved. Thus, the 

raw information need not be used as is but may, for example, be normalized in Some way, ranked, and so on. 

Optionally, once the co-occurrence matrix is prepared, it 

can be reorganized so it is ordered, for example, according to 

which co-occurrences are higher relative to each letter of the 

alphabet. 

In an example system implementing this method of the 

invention, and in reference to the matrix (500) in FIG.5, when 

a user types “finance' followed by the characters “su, the 

system would check the co-occurrence matrix in the row 

corresponding to “finance' (502) to see which words starting with “Su” have the highest co-occurrence score. Thus, if 

'Summary' has a higher score than "sugar, the system would 

offer “summary” rather than “sugar as the appropriate completion word. 

In yet another implementation, variants of the above two 

methods can be run together as a cross-check or to get more 

Suggestions. However, limitations of speed and memory may render this dual method approach impossible, impractical or 

overly costly for Some implementations. 

It is to be understood that, while the two approaches have 

been described in terms of bags and sets, because they are 

conceptually the most appropriate mathematical description, 

the actual implementations of these data structures can vary 

(e.g. they can be lists rather than bags or sets), the particular data structure used to accomplish the result being unimpor 

tant to the invention. 

Optionally, in Some implementations the words in the lists 

are further processed to reduce them to stemmed form. That 

is, they may not be the full words but rather words with their 

endings Suitably modified, for example, by one of the many available stemming algorithms. This would enable the pro 

gram to handle words like “finance.” “financing and “finan 

cial' or “remain.” “remainder and “remaining in a uniform 

manner so that the user will be offered the same Suggestions independent of the particular variant of the contextual words 

he has typed. However, Stemming is optional. There may be 

advantages in not stemming words in some implementations, 

because the system can differentiate among them if the words 

are not stemmed and thereby offer one variant on one occa 

sion and another on another occasion. 

The number of initial characters that are needed in order for 

the system to make a suggestion to complete the word is also 

a parameter that can be preset or set by the user depending on 

the implementation. As with completion algorithms of the 

prior art, in most implementations, this is 2 or 3 characters. 

However, word Suggestions may start from the very first 

character. A second parameter that needs to be defined for a 

particular implementation is how long the full word needs to 

be in order for the system to even make a Suggestion. As 

contemplated, the minimum word length is 5 characters or 

strokes although minimum lengths of 2 or 3 characters (or 

strokes) can be used. That is, if the characters “Su” were 

typed, the system may not suggest 'sure' even if it happens to 

be the best association because it is too short a word. On the 

other hand, 'Summary and "sugar are acceptable candi 

dates as they meet the length requirement. This minimum 

length is also a parameter that will vary with different imple mentations and possibly different users. Again, the minimum 

length may be as short as one character so that any and all 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 14 of 18
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Suggestions (up to the “n” best Suggestions) may be offered. 

Once the associations have been computed the resulting information is used to Suggest a word completion. Having described component aspects in simplified and 

theoretical form, particular example implementations are 

now discussed with reference to the example word lists of 

FIG. 6 (600). 

Assume groups of documents to be used have been created, words that appearin each group collected and alphabetized to 

form the series of lists of FIG. 6. 

Example 1 

In one simplified example implementation, a user types 

“finance' followed by the characters “su.” The system checks 

all the word lists for the word “finance.” It would find that it 

just occurs in word list #1 (602). Next, it checks all words in 

that list (or lists, if there is more than one where “finance' 

occurs) that begin with “Su” and it finds the word “summary.” 

As a result, the system offers to complete the user's word with 

'Summary. The user can opt to accept that Suggestion or not 

by the appropriate action, in this case pressing an appropriate 

key (e.g. the “Enter” key). Note, that as a result of the above 

processing, "Sugar is not on this list and thus it is judged not 

to be a contextually relevant suggestion. 

Example 2 

In another simplified example implementation based upon 

FIG. 6, the user types “two spoons of and then the characters 

“su.” Again, the system looks for the words “two and 

“spoon' (the plural ending can be assumed to be stemmed in 

real time if stemming is used) on the word lists. Because, in 

this example, the word “of” is on the stop word list it is 

ignored. The system finds “two in word lists 2 (604) and 3 

(606) and finds “spoon” in word list 2 (604). As a result, it 

looks for words beginning with “Su” in all common lists first, i.e. in this example, word list 2 (604). It finds “sugar in word 

list 2 (604) and offers it as a suggestion to the user, for 

acceptance or rejection. In this case, although word list 3 

(606) contains the word “summary' which could have been a 

possible suggestion, it was eliminated because “summary is 

associated with “two through word list3 (606) but it is not 

associated with 'spoon' on any list. 

Example 3 

Inafurther simplified example implementation based upon 

FIG. 6, a user types in “one followed by the characters “ch.” 

Now “one” occurs in word lists 2 (604) and 3 (606). There is 

no other information to find a common list or to eliminate any list containing “one' as was done with “summary” in the 

previous example. In this case, both lists are considered and 

searched for words starting with “ch.” There are two: “chili” 

in list 2 (604) and “chapter in list3 (606). Depending on the 

implementation, both Suggestions are offered to the user for 

acceptance or rejection or the system can wait until the user 

types a third character. Note that, if the former occurs, option ally, the system can operate to ignore further character entry 

for that word or represent a selection based upon the next 

character entered. This can occur only once or interactively, the former allowing for entry of words not in any list without 

annoying prompts, the latter allowing for discerning among 

multiple words with similar starting letters like “heat.” 

“heavy,” “heather,” “heaven,” hearing” and “health' which all 

share an initial “hea' or different words having a common 

root like “finance.” “financing,” “financial.” and “financially.” 

5 

10 

15 

25 

30 

35 

40 

45 

50 

55 

60 

65 

14 

In the example, a third letter is waited for, so when the char 

acters “cha' are typed in, “chili' is eliminated as a possible completion and just “chapter is offered. 

Even when there are multiple words that have been typed as 

in Example 2, if there are no common lists or if there is more 

than one listin common, similar options can be employed, i.e. 

either all of the suggestions are offered or wait until additional 

characters are entered. If this is done interactively, eventually the system will most likely eliminate possible options until 

just one or a few can be offered to the user. As with other 

aspects described herein this can be preset or set/modified by the user, depending upon the particular implementation 

involved. Some users find it annoying to have too many Sug gestions while others find it acceptable. Of course, it is also 

possible that the words already typed do not belong to any 

word lists in which case, in one example implementation, no 

Suggestions are offered. Alternatively, in Such cases, a prior 

art approach Such as providing a word from a standard online 

dictionary or a word from a most recently used list can serve 

as a fallback. These again are implementation-specific deci 

sions. 

While the above simplified examples reflect the kinds of 

situations that can arise, by no means do they exhaust all the 

possible permutations and combinations of situations that 

might occur in practice in a more commercially Suitable 

implementation. For Such systems additional optional exten 

sions can be employed to make the approach faster, more 

robust, or otherwise more suitable. 

One such optimal extension uses frequency information 

(i.e. the number of times the word occurs in the set of docu 

ments under consideration) stored with or otherwise associ 

ated with the words in word lists so that when there is more 

than one choice available to offer to a user, frequency ranking 

can be used to specify the offering. This is now described with 

respect to a simplified example matrix presumed generated 

from a group of documents using a technique described above 

illustrated in FIG. 5. 

The matrix of FIG.3 is similar to that of FIG.5 except that 

only half of the matrix is filled out because the information in 

the lower diagonal half would be symmetric to the informa 

tion in the upper half and it was created from a slightly different group of documents. In the example of FIG. 5, the 

diagonal entries are left blank but it should be understood that 

they can alternatively optionally be filled with plain fre 

quency information (e.g. the number of times a word occurs 

in a document or on the device). 

Example 4 

In an example system incorporating this simplified 

example implementation which uses the co-occurrence 

matrix of FIG. 5, a user types “finance followed by the char 

acters “su.” The system looks up “finance' in the rows (602) and finds the co-occurrence scores of the words starting with 

“Su” in the columns. These are “summary' and “sugar and 

their scores are 12 and 1 respectively (identified at the inter 

section of the two in the matrix (500)). Since “summary' has 

the higher score, it is offered as a Suggestion to the user. 

Example 5 

In another example system incorporating this simplified 

example co-occurrence matrix the user types “two spoons of 

and then the characters “su.” Now the word “two” (508) has 

scores of 3 and 7 respectively for “summary” and “sugar and 

“spoon” (516) has scores of 0 and 14 for “summary” and 

"Sugar respectively. Using any of a number of known pos 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 15 of 18
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sible ways for combining scores to produce a unique or at 

least a ranked outcome a preferred or selected result is 

obtained and the corresponding word presented. Clearly, in 

this example, any reasonable formula (e.g. the Sum of the 

scores) should produce “sugar as the preferred outcome and 5 

offer it as a possible completion to the user or alternatively 

offer both suggestions with “sugar optionally ranked first. 

Example 6 10 

In a further simplified example implementation involving 

the co-occurrence matrix of FIG. 5, presume a user has typed “one' followed by “ch.” The two possible completions as 

seen from the table above are “chapter” (512) with a score of 

6 and “chili” (514) with a score of 8. In this implementation, 

based upon the scores, the higher score may be chosen or, 

since the scores are relatively close to each other, a ranked list 

may be offered. If the two scores had been identical the order 

of presentation can be based upon, for example, the alpha- 20 

betical option, the most recently used option or some other 

Such variant can be used to specify presentation order. Alter 

natively the system can be set up for cases where the scores 

are the same to wait, without offering any suggestion, until 

the user has entered one more character(s) (e.g. "cha') in 25 

which case, in this example, “chapter” would be the unam 

biguous choice. 

In examining the examples above, it is clear that there will 

often be cases where there will be more than one possible completion to offer the user. In some implementations, the 30 

system will not offer the user a possible completion until the 

user has entered sufficient characters so that an unambiguous choice can be made. In other implementations, the system will rank the possible completions to be offered to the user 

based on the contextual associations that have already been 35 

computed. For example, words with a higher co-occurrence 

score, words from more frequently or recently accessed lists, 

or words from lists that have the greatest similarity, can be 

offered first. Still further, in some implementations, the rank 

ings can be dynamically set in that they change as the user 40 

enters more characters (whether in that word or during the 

course of document creation). One representative example of how ranking of multiple 

possible completions can be achieved is now described. Since 

multiple possible completions are likely to be the norm rather 45 

than the exception in many commercial implementations, a 

ranking method is sometimes desirable. 

Assume two words have been typed in a document and a 

third word is being entered. Assume further that the co-oc 

currence score of each prior word with possible completions 50 

based on the characters already entered is available from the 

co-occurrence matrix. Then the “relationship score' is 

defined as a monotonic function of these co-occurrence 

scores—for example, a simple sum of the co-occurrence 

scores of candidate completions with the prior words. 55 

Next, a function “f” is defined and stands for the “overall score as follows: 

f=Overall score- if relationship score-1 

and if frequency score 1 

then overall score relationship scorexfrequency score 60 

else overall score relationship score--frequency score 

Here, for example, relationship score the sum of the co 

occurrence scores of the candidate completions for the word 

being entered with the prior words in the text. Optionally. In 

some implementations, a limited part of the context will be 65 

used to make the computations practical, e.g. the prior ten 

words may be used. 

15 

16 

Given the example above, if the two words of the document 

have already been typed, and a third word is being entered, in 

general, there will be several candidate completions. If one 

presumes that the candidate completion is some word 'X'. 

then the co-occurrence matrix is examined for the entry cor 

responding to X to find the co-occurrence score for X with the 

first two words of the message. In this example, presume that 

the scores are respectively 8 and 3. Thus, the relationship 

score will be 8+3=11. Of course, in this example, the com 

plete number of words can be used. However, with longer 

documents, it may be desirable as noted above, to limit the 

number to some Subset, for example, ten words in one 

example implementation. Depending upon the particular implementation, the fre 

quency score will, for example, be given by the number of 

times a word has been used by a user in all the documents on 

the device. Alternatively, in Some implementations, a stan 

dard frequency of the word, for example as obtained from a 

corpus or standard usage, can be used instead of the user's 

own document-derived frequency. In yet other implementa 

tions, a function of the two types of frequency, the user based 

frequency and the corpus based frequency (e.g. the sum of the 

two) can also be used. 

Then ranking is done, for example, by ordering according 

to increasing or decreasing overall score or by increasing or 

decreasing values of “f. 

In some cases, two or more candidate completions can have 

a tie in their overall scores. In Such cases, one of many ways 

to deal with this tie circumstance can be to order the comple tions by the following ranking hierarchy: exact match->stop 

words-related wordd-unrelated word, where "exact match' 

means the length of the completion length of characters 

entered so far; “related word” means word with non-zero 

co-occurrence score; and “unrelated word” means word with a co-occurrence score of 0. 

Of course, it will be appreciated that such a function will 

have multiple possible variants, only one of which has been 

described for concreteness, others being straightforwardly obtainable from an understanding of the prior example. Simi 

larly, other ranking schemes can be used that are based on 

Such a function. 

A few such variants are now described. First, as mentioned 

above, any monotonic function (like a Sum) of the co-occur 

rence scores may be used to arrive at a relationship score. 

Similarly, any monotonic function of the relationship score 

and frequency score may be used (like the combination of 

product and sum used to define fabove) to arrive at an overall 

score. Finally, when there is tie, alternative rankings of can 

didate completions are possible on the basis of the cases 

described above (e.g. exact match, stopword, related word, 

unrelated word etc.) and other similar cases. 

More general computational schemes may also be 

employed. For example, the functions described above need 

not be monotionic, although the results with Such general functions may have limited practical value. Finally, a single function instead of two separate functions may also be con 

sidered, taking as arguments the co-occurrencescores of prior words and the various types of frequency scores described. 

The ranking methods described above pertain to the co 

occurrence method of computing associations between 

words. When the vector space and document grouping method is considered, a similar principle can be applied. Instead of the relationship score being a function of co-oc 

currence scores, the relationship score can be defined as some 

Suitable function of the candidate completion's group mem 

bership: does it belong to multiple groups?, what is its fre 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 16 of 18
US 8,311,805 B2 

17 

quency within each group'?, and even some function of its 

various frequencies in multiple groups are the kinds of inputs 

that may be considered. 

In addition to the contextual and automatic aspects which 

have been illustrated So far, there is also an optional dynamic 

aspect that can be included. Where Such an aspect is used, the 

user continues to create more documents of different types, the system re-computes the associations of words for 

example, through the methods described above. This allows 

the system to keep evolving and improving as it has more and or new data to work with. In the case where a co-occurrence 

matrix is created by pooling all of the unique words from all 

of the pertinent documents, the option of updating the co 

occurrence matrix in real time, even as the user is creating a 

new document, is available. 

Notably, the above examples have implicitly dealt with 

unstructured documents (like emails, SMS text messages, word processed documents, etc.) and not with structured 

documents like forms, letters and spreadsheets. Advanta 

geously, such documents can also be included in the class of 

documents useable with the invention. Moreover, the struc 

ture itself can additionally provide usable contextual clues. 

For example, in a letter, the closing usually contains words 

like “Very Truly Yours' or “Sincerely;” in such a situation, as 

a special case related to that used in the prior art, the system could suggest special words like 'sincerely from a special 

list because of the location in the document. Similarly, certain 

words may always appear capitalized (e.g. like the user's 

name and address) and these special words can once again be 

handled in a special way. In some implementations the word 

lists themselves can be further extended so as to contain, as 

needed or desired, a separate field for storing special infor 

mation about the word Such as capitalization, formatting 

(bold, italics, etc.), hyphenation, inclusion of special charac 

ters, accents on certain characters etc. Since the storing of 

Such information, perse, is known and in use in, for example, certain word processors and it is readily implementable based 

upon that knowledge, for brevity it is not described herein. 

As to treatment of numbers, in any given document there 

may be special numbers like the current date and year that can 

be offered as completions in the context of a letter or such 

numbers can be treated as “stop words.” 

Advantageously the above techniques can further be 

extended to allow for phrase completion. One way of doing this is to find phrases and so-called collocations by using standard frequency-based techniques on a phrase, as opposed 

to a word basis and then apply the methods described above to 

these phrases. In addition, an advantage of the techniques disclosed herein is that they can be combined with existing prior art techniques like Most Recently Used (e.g. “MRU’) techniques. For example, one way to use a combination of the 

present invention and the prior art techniques is to narrow 

down the possible Suggestion list using one of the above 

methods and then choose one or rank the remainder by an 

MRU technique. Another is to use regular dictionaries in the 

absence of any suggestion the system might make. Finally, 

the order of words on a word list generated by the vector space method can be allowed to change dynamically based on the 

use the user makes of Suggestions he receives. 

Note that when a user acquires a device like a computer, cellphone or personal digital assistant for the first time, there 

are not going to be any user-created documents on the device 

to which the methods described above can be applied. To 

allow for this, until there is some history of document cre 

ation, one or more of several options exist. One of the con 

ventional prior art techniques could be used until a sufficient 

number of documents exist. Alternatively, manually created 

10 

15 

25 

30 

35 

40 

45 

50 

55 

60 

65 

18 

word lists or co-occurrence matrices of ageneric type, i.e. that 

a multiplicity of users are likely to find useful, could be used; 

or more specialized word lists based on different types of 

Vocablaries or uses (e.g. for bankers, lawyers, businessmen, 

doctors, domestic uses etc.) could be created by the provider 

or others. In general. Such approaches would be temporary 

until the user has his or her own document-based history at 

which point the existing lists or co-occurrence matrices can 

either be augmented, modified or completely dropped in 

favor of new word lists and matrices generated by the user's 

own documents. These same approaches can also be used in 

the situation where there are documents on the device but the 

user is starting a new document and there are insufficient 

words entered to establish associations. 

An optional extension of the methods which uses the text 

being generated as dynamic data will now be described. This 

method can be used, in some implementations, to augment 

any of the approaches described above. 

Consider a user who is typing a somewhat long document 

(for example, more than three paragraphs, although this is 

again a variable parameter that can differ in particular imple mentations). Suppose they have already typed a few pages 

(say two pages) and now they are on the third page. The data 

entered so far can be used in any of two ways. One, as 

described above, where it provides the context to the next 

word being typed. Another, as dynamic data, in which the first 

two pages are used as data from which to create an auxiliary 

co-occurrence matrix of the type discussed above dynami cally (i.e. “on the fly’). This matrix can then be used, as before 

in conjunction with either the word lists based on document 

groups of the first method described above or with the co occurrence matrix of the second method described above. 

This approach can provide additional advantages because text 

in the document the user is currently working on will likely be 

more relevant to the word completions that can be offered to 

the user than those in the lists. This dynamically created 

matrix of co-occurrences is either continually updated on the 

fly, or periodically as the user continues to type—for example after each paragraph or page is completed. Alternatively, 

instead of using a co-occurrence matrix, a plain frequency matrix containing the number of occurrences of each word in 

the document being typed could be used. 

In instances where it is desirable that the results derived 

from this dynamically-created data be combined with results 

derived from the offline data, the intersection of the results 

can be taken and further, it can be optionally ranked in some 

way. Alternatively or in case of an empty intersection, the 

union can be taken or one set of results given greater priority. 

When a full keyboard is not available on the device (say a 

cellphone) there are currently methods (e.g. T9) which allow 

the user to enter, for example, the numbers 7 and 8 which 

correspond to the multiple groups of letters p, q, r, S and t, u, 

V respectively. A method like T9 offers the user an alphabeti cally ranked list of acceptable combinations (e.g. "pu' and 

“Su” are acceptable, “pt' is not) based on dictionary entries. In 

Such a situation, the computations described in the foregoing example involving any of the possible approaches would still 

occur, but not just to, for example, “Su” but also to, for 

example, "pu. Presumably, if the user were typing something like “finance st/pu' then, just as completions like “sugar would not be acceptable, completions like “pushing” would 

also not be acceptable under the same logic. If it turned out 

that that completion was, in fact, relevant under any of the 

methods above then multiple possible completions would be 

offered and these would be dealt with as before (e.g. by a 

ranked list). 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 17 of 18
US 8,311,805 B2 

19 

Finally, it is to be understood that various different variants 

of the invention, including representative embodiments and 

extensions have been presented to assist in understanding the 

invention. It should be understood that such implementations are not to be considered limitations on either the invention or 

equivalents except to the extent they are expressly in the 

claims. It should therefore be understood that, for the conve 

nience of the reader, the above description has only focused 

on a representative sample of all possible embodiments, a 

sample that teaches the principles of the invention. The 

description has not attempted to exhaustively enumerate all 

possible permutations, combinations or variations of the 

invention, since others will necessarily arise out of the use of 

particular hardware or software or through specific types of 

applications in which the invention can be used. That alter 

nate embodiments may not have been presented for a specific portion of the invention, or that further undescribed alternate 

embodiments may be available for a portion of the invention, is not to be considered a disclaimer of those alternate embodi 

ments to the extent those undescribed embodiments also 

incorporate the minimum essential aspects of the invention, 

as claimed, or an equivalent thereof. 

What is claimed is: 

1. A method, performed in a character entry system, for 

interrelating character strings so that an incomplete input character string can be completed by selection of a presented character string, the method comprising: computing relationship scores for individual character 

strings in the system from documents stored in memory of the character entry system, the relationship scores 

consisting of a function consisting of co-occurrence 

scores between pairs of distinct character strings stored 

in a single matrix created from the character strings in 

the stored documents; in response to inputting of a string of individual characters 

that exceeds a specified threshold, identifying at least 

one selectable character String from among contextual 

associations that can complete the input character String in contextbased upon an overall ranking score computed 

as a function of a relationship score and at least one other 

score; and 

providing the identified at least one selectable character 

string to a user for selection. 

2. The method of claim 1, wherein each relationship score 

represents the contextual association between an individual 

character string and another character String based upon co 

occurrence of character strings relative to each other. 

5 

10 

15 

25 

30 

35 

40 

45 

20 

3. The method of claim 2, further comprising: computing the overall ranking score as a monotonic func 

tion of the relationship score and a frequency score. 

4. The method of claim3, wherein the overall ranking score 

is a product of the relationship score and the frequency score, 

unless either relationship score or frequency score have a 

value of Zero or one, in which case the overall ranking score 

will be a sum of the relationship score and the frequency SCO. 

5. The method of claim 3, further comprising: computing the relationship score as a monotonic function 

of co-occurrence scores. 

6. The method of claim 5 wherein: 

the relationship score is a Sum of the co-occurrence scores. 

7. The method of claim3 wherein the at least one selectable 

character string comprises at least two strings, each having an 

overall score, and wherein the overall score for each is the 

same the method comprises: ranking the strings according to a hierarchy. 

8. The method of claim 7 wherein the hierarchy is based 

upon at least one of 

exact matches, stopwords, related words, unrelated words. 

9. The method of claim8, wherein the hierarchy comprises, in order of precedence, exact matches followed by stopwords 

followed by related words followed by unrelated words. 

10. The method of claim 1, wherein, each relationship 

score is a function of membership of the individual character 

strings in a candidate completion’s group. 

11. The method of claim 10 wherein the function com 

prises a value based upon membership in multiple groups. 

12. The method of claim 11 wherein the value is based 

upon a frequency within each of the multiple groups to which 

the at least one selectable character string belongs. 

13. The method of claim 10 wherein the at least one select 

able character string comprises at least two strings, each 

having an overall score, and wherein the overall score for each 

is the same the method comprises: ranking the strings according to a hierarchy. 

14. The method of claim 13 wherein the hierarchy is based 

upon at least one of 

exact matches, stopwords, related words, unrelated words. 

15. The method of claim 14 wherein the hierarchy com 

prises, in order of precedence, exact matches followed by 

stopwords followed by related words followed by unrelated 

words. 

Case 1:20-cv-23179-MGC Document 1-2 Entered on FLSD Docket 07/31/2020 Page 18 of 18