'(cid:173)
`Q)
`tlD
`tlD w
`
`001
`
`Facebook Ex. 1002 Part 3
`
`

`
`The Problem: Finding Relevant and Important Material
`
`Finding relevant, important, and related objects:
`
`"will not return the desired textual object" (1 :44)
`
`"does not convey some important and necessary information to the researcher" (2: 13-.14)
`
`Examples:
`
`Find Mauldin (or Ishikawa) in relation to Egger
`
`Find Mauldin (or Ishikawa) starting with a Boolean search for topical material
`based on Egger
`
`How can. a computer determine the relatedness of Mauldin to Egger?
`
`Egger 5,544,3 52
`
`relation?
`
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`
`Mauldin 5,748,954
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`002
`
`Facebook Ex. 1002 Part 3
`
`

`
`Prior Art: Misses Important Material
`
`"search retrieves a significant amount of irrelevant objects" (1: 54-55)
`"will not return the desired textual object" (1 :44)
`
`53 7 5 ~ 752~025 T: ·Method ootnplJter; ptognun po.duc:Land system for. creating and displa}1ng a wegorization table
`538 5.75.1.286 (;:; ==~
`
`539 5.748,.16] . ~ =ai,O= :~.peroeptm!ly ~live and!!10ba!!y scaJable signal em(;Mding
`
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`541 5_745.55-5 'F Sy.stemandnu:pu:xl using personal identifi~n:nmnbers and associated prompts for. controlling:ummthpOzed lise
`
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`
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`
`Word search finds "Image steganography system" (and hundreds of
`other unrelated patents)
`
`Does not find Mauldin (5,748,954) - the "Lycos" patent
`/ "'
`
`Prior art: "Boolean" retrieval (or anything purely "semantical" or
`word-based)
`
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`3
`
`003
`
`Facebook Ex. 1002 Part 3
`
`

`
`The 1352 Solution: {{Proximity Indexing"
`
`" 'Proximity indexing' is a method of indexing that uses statistical techniques and
`empirically generated algorithms to organize and categorize information stored in
`databases .... for legal research by indexing objects based on their degree of
`relatedness- in terms of precedent and topic-
`to one another" (11 :51-60)
`
`"Textual objects may contain 'citations', which are explicit references to other
`textual objects" (11 :63-64)
`·
`
`"Any two textual objects may be related to each other through a myriad of
`patterns" (12:32-33)
`
`"The 'numerical factors' for all eighteen patterns are assigned various
`weights ... to generate a scalar .. . " (13:35-26)
`
`" .. . proximity matrix" (14:2)
`
`" ... and generate a coefficient of similarity" ( 14: 8)
`
`4
`
`004
`
`Facebook Ex. 1002 Part 3
`
`

`
`• Direct Relationships
`Patent Example •
`
`t' .... '*-'f'IIHII ..
`
`Egger 5,832,494
`'W8 lii!!!J!!UIIIII
`.....
`~=~::i~·-· i.~~~
`
`"contains ... citations" -- 12: 15
`
`"Citatio·n Vectors" -- 14:55
`
`"first numerical representation"
`-- cl. 26
`
`Egger 5,544,352
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`
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`
`Page 6,285,999
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`Mauldin 5,748,954
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`Turtle 5,418,948
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`
`5
`
`005
`
`Facebook Ex. 1002 Part 3
`
`

`
`The Claims Require Creating "a First Numerical Representation" of
`Existing Direct Relationships between Objects in the Database .
`
`• "initial ex tractor subroutine" ( 14:4 7-15: 1 7)
`
`• "Create Opinion Citation Vectors ... [b ]y comparing each full textual object in the data
`every other full textual object that occurred earlier in time" (14:55-57)
`
`base to
`
`--
`
`Ualted States Patent l ltl
`rep.
`
`, ........... 11.110
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`Claim 26 . .. wherein direct an~· indirect relationships
`exist between objects in the database .. ... creating a first
`numerical representation for each identified object in
`the database based upon the object's direct relationship
`with other objects in the database;
`
`(Claims 41 and 45 have comparable elements)
`
`6
`
`006
`
`Facebook Ex. 1002 Part 3
`
`

`
`Patent Example • Indirect Relationships
`
`•
`
`"myriad of patterns" (12:33)
`
`"analyzing the first numerical
`representations" ( cl. 26)
`
`"indirect relationships ( cl. 26)
`
`Turtle 5,41 8,
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`
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`
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`
`7
`
`007
`
`Facebook Ex. 1002 Part 3
`
`

`
`The Clai~s Require Using a.Numerical Representation of Each Object Based on
`Direct Relationships to Generate Another Based on Indirect Relationships
`
`26. . . . creating a flrst numerical representation for each identified object in the
`database based upon the object's direct relationship with other objects in the
`database;
`
`***
`analyzing the first numerical representations for indirect relationships
`existing between or among objects in the database;
`
`generating a second numerical representation of each object based on the
`analysis of the first numerical representation ...
`
`(Claim 41 has a comparable sequence of steps)
`
`8
`
`008
`
`Facebook Ex. 1002 Part 3
`
`

`
`Patent Example :Analyzing the for Indirect
`Relationships
`
`"<:tnalyzing the first numerical
`representations for indirect
`relationships ... " ( cl. 26)
`"generating a second numerical
`representation ... based on the
`analysis ... " ( cl. 26)
`
`Egger and Mauldin both
`cite Turtle (coupling, P#2}
`
`Turtle 5,418,948
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`Mauldin 5,748,954
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`
`Page cites Egger '494 and Mauldin
`(they are co-cited}; and
`Egger '494 cites Egger '352 (P#8}
`
`9
`
`009
`
`Facebook Ex. 1002 Part 3
`
`

`
`Analyzing for Indirect Relationships
`Many different indirect
`relationships ("myriad of
`patterns") ~ay exist between
`A and B (12:33; Figure 6)
`
`I. a~.~.~t
`
`lA ···~
`lL ... ~
`
`~
`
`For example, if A is Egger
`and B is Mauldin ....
`
`P#2 illustrates that Egger an
`Mauldin both cite Turtle
`
`P#8 illustrates Page cites
`Mauldin and Egger '494, and
`Egger '494 cites Egger
`
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`
`A.-'
`
`10
`
`010
`
`Facebook Ex. 1002 Part 3
`
`

`
`Patent Example : Generating a Second Numerical Representation
`of each Object Based on the Analysis
`
`"scalar ... arranged" (13:53-57)
`
`"proximity matrix" (14:2)
`
`"second numerical representation" ( cl. 26)
`
`Degree of relatedness of Egger to
`Mauldin, representing Egger,
`based on analysis of citations
`
`Egger 5,544,352
`•• ID!!!!,IUIII
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`representing
`Mauldin, based on
`analysis of citations
`
`11
`
`011
`
`Facebook Ex. 1002 Part 3
`
`

`
`Patent Example: Searching
`
`"searching the objects in the database .... " (cl. 26)
`
`"determines the degree of similarity between the retrieved textual objects and the
`selected textual object. ... " (5: 33-35)
`
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`
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`
`"ranks the importance of each of the full textual objects in the pool" (21: 31-32)
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`--· = ==:: = :=.: .. =--= ~===---~::..:~
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`..
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`== -=·· ..-.-.-....---... c-. .. -
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`~
`.. ~~
`n=.=~'lfi:N?.m<-ao':.''
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`
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`
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`
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`• .• •
`~-!.~
`
`ll.lll!!!!.lllllll
`..,.. ,.... .....,.. ~
`':" --:~....... ~!: ..
`
`-
`
`,.., -. n• .. •
`
`f!ll __ ._ _
`-~-­
`
`Uaked States P'lkftl ""
`~--
`-----------=~~~~~~~--~~~
`
`-
`!:'"~ .:='"';
`
`:::;;::-..:....-:.."'=--~
`
`• '352 Solution: Finds Mauldin
`
`• Prior Art: Does not find Mauldin
`
`- - - -- · ·- -
`.c--. ..........
`
`c .,
`
`I
`I
`
`-
`
`I
`
`"
`
`------~u.~i· ---~
`
`12
`
`012
`
`Facebook Ex. 1002 Part 3
`
`

`
`The Claims Require Using a Stored Sec.bnd Numerical Representation
`to Search for Objects
`
`26 ....
`
`analyzing the first numerical representations for indirect relationships existing
`between or among objects in the database;
`
`generating a second numerical representation of each object based on the analysis of
`the first numerical representation;
`
`·storing the second numerical representation for use in computerized searching; and
`
`searching the objects in the database using a computer and the stored second
`numerical representations, wherein the search identifies one or more of the
`objects in the database.
`
`013
`
`Facebook Ex. 1002 Part 3
`
`

`
`c
`0 ·-V)
`
`V)
`:::J
`u
`V) ·-0
`
`.......,
`L..
`<(
`\J
`Q)
`
`......., ·-u
`
`014
`
`Facebook Ex. 1002 Part 3
`
`

`
`Claim 26: "Objects in a Computer Database"
`
`United States Patent "''
`r_.
`
`1)41 JG'IWOG A~O """'._. l\.~ rOM &."«<DL"''C.
`CIAJOUSC .U.'D '*""""'~DATA
`
`c.,, ........ a....._,.,,.......,... ... c
`
`r.')I ~· ....... .... ~ ~C"
`
`~,..., ., ,,ll··'-0 ..,..1) --."'K'A&L.
`
`\1011 ., .. ., .... , _
`
`..,_
`
`... __
`
`~
`
`(:Jl .... ~ IUD
`J:JI tt.t
`tJII ..._ t-\' - - ··- - - - GIM.t'l"(cid:173)
`(1..'"1 CA_Q. -
`,...., ,..... l• tt. lttNDG. l~ U'JJ
`
`.,._..c..• .-;ut .. I UWU
`t ol'lif • C ... ~
`~c.· so""''t"'tl
`" - ' & - -...... .
`~-·:.- -... ·~ ,
`..,
`
`~~ - 1.-.-ot
`
`1111111••11!!.•••••o
`.. -(cid:173)
`, ..
`........ ~
`$,!
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`__ ... _,_ ... ,
`.. ...... .,. ... _ .. .._ ...
`____ .. __ ....__
`,
`""'
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`-···------.......
`'"' •• Sodf~ . . w.-..,.... ...
`.. .___,_ ........
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`-. ..........
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`fi.IIIJDIIIIIIII
`......... - •.. ~ P:-... "!~!.. ... ~~!- -
`•11 ,.,.. ""'-*'"'
`s.au .....
`·--~ .... ~--- -·-- - ... ·--·
`--
`·(cid:173)··-
`lh~---· -.......
`... _
`-
`··· -
`1M . . . . . , . . .,_,..,.,,f'lllll ...... --·-·-._.-"""*""' ..
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`•••IIIP•III
`--(cid:173)
`\Jiolkd--...
`.. .... "' ..... ..,_
`__ . .__....._ ___
`,._ __ .. ___ ...
`..,..., -----
`--~
`~
`,.. ~~~
`::--~.::.:-r=:r::;
`EJ
`- -=":"""---
`=-............. , .... - .~
`.... _ .....
`;::.~=-===--·
`----·-==
`==r= =--=
`-"_.,-.~-... " -
`.. ............
`IDIIU..,!!!IIDIII
`... ......
`-~-~ """"-
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`·---··- (cid:173)
`,.. _ - .. -..__
`-----··- (cid:173)
`·---···--·-
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`::::::.::.---=:: ... ,_
`,.._= .................
`........ ______ ....
`
`... ........ -.... .. ,_
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`--·-----
`-----------
`a.---.---·-.
`...... ...,_.
`-----.J
`.... -.-----.... ·-- --·-
`- -"-..=::-=::.": ~-:-~
`= = =:~·:=:-:::: ======:_-::
`= = ::--:.: ~..=.= 2:":".:"::=:::r::e::...-:=.
`===:=;.:!...;.::,.._= :
`: ==:-:.:.::.::.::
`;;;s ~::!.-.= = ::::.:::::-.:=::
`~-~~i;~ ~~~~~-==
`---- ... --~ -
`·---·---
`~~
`
`Egger:
`• objects in a database with existing citations
`
`• "method for numerically representing
`objects in a computer database" ( cl. 26)
`
`• "direct and indirect relationships exist
`between objects in the database" ( cl. 26)
`
`/
`
`• " ... for each identified object in the
`database based upon the object's direct
`relationship with other objects in the·
`database" ( cl. 26)
`
`,_ " ''.,
`tJtt rw.,,__ __ ~,....l9 • •.
`_ .,.,.
`,,..
`U._.~OOC\I~'TI
`~ ....,. ..._ .• ., ___ ,c
`..... .w .,.... ~-· -- --
`t111't•
`.,. .. , " ' - " - -- ·· 1MI•H rt
`\.Ut, rt) I.....U .-...- • • - - (cid:173)
`,,..._
`.,. ... , c--. •• - - - -
`. .,.
`, ....... .,.,.... ........
`..,., ... ., """"" ' -
`......."
`uov .. - · -
`· · - - -
`... It,..,... ..... ___ ..... ,.,....
`ona ... •.t:Ant;JII."'\
`.-,... .. .a . •.A1 ~"41 Hrflll'\rU~ ..... ,_
`.,_.,... 0... • ..c;w .. , ''"'''
`,___. .. _ .......... o-,. ........ a.-....
`"'-1' I"'-~ .......... ..... ,
`"""
`.... . ttn-. . "'\..rpf~--·-
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`~-- ICAII,.'W tf"'''
`~........C." 1CAI "
`
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`
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`,. ,... ~ UoM.1P
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`
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`
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`
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`
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`
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`
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`
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`
`:...~~
`
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`
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`
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`
`_
`
`<II .. _..,... __ ~ . . . .
`
`......
`1-----"'
`
`15
`
`015
`
`Facebook Ex. 1002 Part 3
`
`

`
`No Database Objects
`With Citations
`
`Fox:
`• Titles, abstracts, author & date
`• no references or citations
`
`1
`
`"The assembled CACM collections file is
`based on titles, and where available,
`abstracts of all articles published in the
`Communications of the A CM from the first
`issue of 195 8 through the last one in 1979."
`Fox at p. 66.
`
`Garner:
`• graphs and matrices
`based on citation
`index*
`• no database, no
`documents
`
`. T
`ELIZA-A Computer Program For the Study ofNaturallanguage Communication Between Man
`And Machine
`.W
`ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain
`kinds of natural language conversation between man and computer possible. Input sentences
`are analyzed on the basis of decomposition rules which are triggered by key words appearing
`in t he Input text. Responses are generated by reassembly rules associated with selected
`decomposition rules. the fundamental technical problems with which ELIZA is concerned are:
`(1)the identification of key words, (2) the discovery of minimal context, (3) the choice of
`appropriate transformations,(4) generation of responses in the absence of key words, and (S)
`the provision of an editing capability for ELIZA "scripts". A discussion of some psychological
`issues relevant to the ELIZA approach as well as of future developments concludes the paper.
`.B
`CACM January, 1966
`.A
`Weizenbaum, J .
`. N
`CA660108 JB March 3, 1978 4:00 PM
`
`* Sample citation index entry from Garfield, for
`illustration purposes only (not disclosed
`as being in a database in Garner)
`
`IS .
`
`•
`
`~
`
`IS. Aeua1, L., ilfll.r Uect ·el Epbe(cid:173)
`ploriow - Eoei~ 10: le7-229
`{ 1950).
`1•. NcA.rt'-"'• J. W., If el.1 Uriaa.rr
`~tioft ... Cclftkoo.lln'Oida Ia !)Ia.
`t.w Acidodl. 10, 507-SIZ ( 1950).
`...... "L: rcrtil1~ ia ~
`NaJa. 10: sat-Sta (1950).
`16. Crou-, $., •I .1. I lcliopada.Oe ,Lac•
`.. ..... fellowi ... .._._pa-y, 10:
`1~JU (1950).
`17. Coopn •. J. &., .. el. : Metabolic c=oo.
`IICqtll-., Spinal Cord ·~j...,, 10:
`.,._.70 (1950).
`Ja. HicKo, D .: A.clreaal Metaboll- ia
`8rondaial A.adlma, ao: ano-un
`( 1950) .
`l9. Jllilcr, J. W .: ~tv.i~A4ro:aal s,..
`-U.Iafaata, II : 116-IH (lUI).
`20. ~·•· H. W. 1 The Adnluh ia r.a(cid:173)
`pcri.cncJtl&l H)'PCnCMlo.., J 11 .,~
`2a. ( ltsl).
`.
`21. Hioeo. D., n e1. : ~piacphriM ucl
`ACTH ill Sroac:hial A.adlma. ll1
`22. :sct.aftc:obe ... c. A.., •••• : .,..u.,.
`395-407 ( 1951 ).
`
`~e- ( PJU') ucl
`.,.,_ ao<aJJed pituituy inlaiWUin,
`II : I:ZI!-1223 ( 1951) .
`n . 'Talboc, N. 8., •• el. : Urinary Wa~
`.soluble Conicoatcroicb, I I : 1223-
`1236 (1951).
`
`CiJeft.a I•~•• 6rt1r7
`IJJ2~17
`46 ... , . ,( Jl )
`N9-Ss&6( Jl )
`IIOS·,.76(A)
`112S-+4S2(a.)
`al 1,12~7!2(0)
`.0779(0)
`-726+(0)
`-7JS1(0)
`·?S85(0)
`.()866(0)
`.. 221(0)
`AuetOJ
`·"91(0)
`·9529(0)
`
`Table I . Index aam
`by Han. klr-. weer
`dromc" U. Cti11.
`( 1946)). Tloe cocte no
`in lhe W or14 U, ;.
`number ia art>ittarill
`the code oumber 101'
`681. T1>c 2S .nicJ.
`anic:lc are lialed, fo
`'"''i~al ~;,.,;.,. ..... ,
`~~~-· R, review ..,,
`ar\\de.
`
`I. Willia~ Jl . H .
`ln~n-elationa, 1
`2. Vc:nni~ !>. H
`coida, 7: 7..-101
`S. Forbes, ' ' •'·:
`Traumt and l
`( 1947).
`i . Talbot, •• •1. : i
`coni.-uroid .. :
`~ . Cutillo, :r.. B. c
`or lludimnnary
`( 1947).
`6. Fonham, P. ti
`Adrcnocorticoll'l
`( l948) .
`7. Pine,... C ., ' ' •
`£..:rccion, I : 2:
`• . LeCompu:, ... "'
`Cortex in LymJ
`1$1-162 (1949)
`9 . Wolrton, W . Q
`Cout, 9 : "97-~ 1
`10 . Strin, H . J.,
`oponse to
`( 194
`•
`u, 114. £ .:
`1\An.-y and L
`( 1949).
`Conn. J. W . : N
`(~p#lic.al lndu,
`
`0
`
`I
`
`0
`0 0
`
`I
`~ ID 0
`A- o. o
`0 0
`I 0 o.o
`
`.
`
`0
`
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`I
`0
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`0
`0
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`0 ()0
`I
`
`=
`
`0
`
`0
`
`0
`0
`
`c 0 0
`() 0 0
`
`0
`
`0
`
`0
`
`/
`
`/
`
`0
`
`Fig. 5. 3 -- Square of the Association Matrix
`
`16
`
`016
`
`Facebook Ex. 1002 Part 3
`
`

`
`Claim 26: "Creating a First Numerical Representation"
`
`Egger:
`
`• "initial extractor subroutine" (14:47-
`15: 17)
`
`• "Create Opinion Citation Vectors
`[b ]y comparing each full textual object in
`the data base to every other full textual
`object that occurred earlier in time"·
`(14:55-57)
`
`• "creating a frrst numerical
`representation for each identified object
`in the database based up<?n the object's
`direct relationship with other objects in
`the database" ( cl. 26)
`
`1m111111•••••••n••n
`IJSI:XII.)&<JllA
`5.su.J$l
`l'llall NW1121or.
`:111
`(<'I O.U fll l'llcal:
`" ... 6, I tM
`ClcloM. ~....,..., --n..elUCI)N.A
`..... lh>o_llrdi,.. ,,......ICAII. ,111001).
`..... . , l0c<t4p0....,- w. .. -
`t,_,.nl." .uc;d ... 11.,.,11
`"fct\lr • CaA. .,.,..__., ~ ~tor ~
`~IL.'"'SJCI"''tlt9h
`
`~~.--na.... a. llld.
`
`......... ~-ww-·.....,
`
`United States Pateat '"'
`t:arr
`
`J)IJ ll«1ltODASDAPPAI.An;$MtL'Cot'.l.ISC;.
`SIA IOU.~<: A. "'I O!Sf\A\'1.-.r. D.\TA
`
`,,,, , . ...,.. ·~--~ ~('.
`
`lttl 211,.....,~ b.Wfcr, ...,...._OC
`
`r:u A.ot.,.. ,...._~
`1!11""" _ , , _
`
`,...,___0.., • •v.r, """'
`Aa.ttaAC'T
`
`M-.. AI-. -
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`~lel:ah.ttAIWO Sitfinbft'r. •~rN:I.d
`·~iU.~~mat~~tfdu~
`u·U.W~oMt~-.lfa ........ •IIW
`
`,...
`
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`.......... ~.-fl'doiO..,.""'-""-·
`,_.. • o.- .._. ...,._. -
`.. ,
`11 .. 11
`-
`·t(;AII. ... (I-
`-
`........ "'-PI ..,.._.... .......... . lt)tN
`................ ,_.._~ .. a.-o
`~ lliDw•.t.'"' ICAI\. "'n (1Wh
`
`-----....-(cid:173)
`Ill~ !Ia " " " -' - .... ..-»
`__ ... "-_....,....,
`~:./=.~-:-· iD!~.:
`. ...-..... --""~
`.......... ...,..,. ........,. ***'
`... _"t .... __ _ ._
`........................ ~ .....
`- -u.o.w.,__, -.. ..
`• ~ . . " dlcla&aal nr a..-.
`~..a at'ab. 1'it c::srow '* .-.n diD ..
`......- ........... CSI'D>C). - -
`-
`• ~ .. o.--.·,~w. tltlc.e~
`da:LCW~ . . . ..,...., 'nwe.t-c.:. ~
`......... ~..,., ...,..tOUi l ~·-­
`... _ _._ ,_piQIOal......,.'lb<
`.......,......, .. _ . , . ._ ... CPD\1_
`CF11J1Hafct:..,.,....,.,._,.a .... ....._,
`
`~.......-tf,a.aa
`
`ac:w-. u .,.__
`
`17
`
`017
`
`Facebook Ex. 1002 Part 3
`
`

`
`Creating a First Numerical Representation..:.- Not Disclosed
`Fox:
`• Fox does not disclose creating source-cited pairs or in submatrix. See Response, pp. 15-21.
`• lSI collection ("source-cited pairs") : no In matrix, pairs show co-citations, not direct relationships. Fox
`Collections, pp. 46-47.
`• CACM collection: In a separate paper (Fox Collections), Fox states: Carol Fox and Jill Warner looked
`through printed copies of each article to locate the bibliography .... a list of the 'dids' representing articles
`referenced in the CACM collection was eventually obtained .... a relational form was produced: Raw_ data
`(citing, cited) which contained pairs of identifiers for the citing article and the one in the article's bibliography.
`Fox Collections, p. 14.
`
`Garner:
`• Garner discloses mathematical
`"notation" (p. 7) of functions, graphs,
`and matrices.
`• It does not disclose a database or
`creating the graphs or matrices from
`objects. See Response, pp. 30-32.
`
`1.k~
`
`~c,
`
`Flg. 3. 1-- Example of Clllng Function Txo
`
`Figure 18: File 1 on lSI Citation Tape - 88,294 R«or<b .
`
`Articles 1 and 2 each have ennies according to rorma•:
`
`Field
`Name
`
`No. or
`Ch~
`
`Journal
`Volume .
`Page
`Year
`Author
`Citation Ffeq
`
`20
`4
`4
`2
`18
`5
`
`· The pair has eut.ry:
`co-cil.a1.ioD (rtqneney- 4 characters
`
`2
`
`( / .
`
`I
`
`I
`
`I
`
`II= 1:
`
`• 1'1
`
`0
`
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`
`0
`
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`
`f
`" 0
`
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`
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`
`()
`
`!]
`0 I C'· I
`
`()
`
`()
`
`()
`
`.1
`
`3
`
`S'
`
`Request: Fox "discloses describing direct citations using 'source(cid:173)
`cited document number pairs' .... describes representing direct
`relationships in a link matrix ln. "Request, pp. 19-20
`
`0
`
`I
`
`0
`
`I
`
`()
`
`s-)
`
`fie. 3. 8 MAttia RopreunUI>OII " A Crapb
`
`18
`
`018
`
`Facebook Ex. 1002 Part 3
`
`

`
`"Analyzing the First Numerical Representations for Indirect Relationships" and
`"Generating" and "Storing" "a Second Numerical Representation"
`
`Ullllll'IJ )lain r.ttM 1•
`
`Egger:
`• "myriad of patterns"
`(12:33)
`
`• "numerical factors .. .
`assigned weights" (13:35)
`
`• "scalar is generated .. .
`arranged" (13 :53-57)
`
`• "arranged ... proximity
`matrix" (14:2)
`
`Egger 5,544,352
`--___ .,..
`~---4· ____ -<~.!!~~
`------.. -- -·----.. -·
`----- ----<• ... -
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`. •n
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`
`1• -
`-
`
`!!!!!1 . . 111
`-
`U'M.UJ
`
`-
`
`-
`
`Egger 5,832,494
`~.~ ....... " :. ::::-.. ~ ~=
`·•·1!!~111
`- =-----="".:=-· !?:.~ ... ~=.r:~·.:::
`
`: ::. :.:;.-:__ ~=-=~~
`
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`
`Page 6,285,999
`D lll,. . -m
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`. -_-:,::__ ~:~:.-·
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`
`;iii
`~
`
`Mauldin 5,748,954
`........... ,_ ... --- ........
`---...--(cid:173)
`-
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`
`Claim 26:
`
`"analyzing the first numerical representations for
`indirect relationships existing between or among
`objects in the database," and
`
`" generating a second numerical representation of
`each object based on the analysis of the ~rst
`numerical representation.', (cl. 26)
`
`19
`
`019
`
`Facebook Ex. 1002 Part 3
`
`

`
`Analyzing ... Generating ... Storing- ~ot Disclosed
`
`Fox:
`lSI Collection: There is no In data or submatrix for lSI. "Source-cited pairs" are co-cited pairs and thus cannot be
`a first numerical representation. See Response, p. 20; Fox Collections, pp. 46-47.
`
`CACM collection: The In, co-citation (cc) and coupling (be) are produced from the same hand-compiled data. The
`In matrix is not "analyzed": "Carol Fox and Jill Warner looked through printed copies of each article to locate the
`bibliography .... a relational form was produced: Raw_ data (citing, cited) which contained pairs of identifiers for
`the citing article and the one contained in the article's bibliography. Figure 1 shows th~ steps required .. . to
`produce from Raw data first normal form [Date 1982] versions of the desired relations for be. In, and cc
`subvectors." Fox Collections, p. 14; see Response, pp. 18-20.
`
`1137
`1141
`
`4
`4
`
`5
`5
`
`6
`6
`6
`
`" .. . versions of the
`desired relations for
`
`Gar ner : A 2 matrix and related graphs are
`disclosed as part of searching, not stored (i.e.,
`in an index) for use in searching. See
`Response, pp. 32-34.
`
`IJ~ I 0
`
`0
`
`I
`
`0
`
`I
`0
`() 0
`
`0
`0 0
`0
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`
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`
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`I
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`
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`
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`j; 0
`
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`0
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`
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`
`ELIZA-A Computer Program For the Study ofNatural Language
`Communication Between Man And Machine
`.w
`ELIZA is a program operating within the MAC time-sharing system
`at MIT which makes certain kinds of natvrallanguage
`conversation between man and computer possible. Input
`sentences are analyzed on the basis of decomposition rules which
`are triggered by key words appearing in the input text . ...
`
`Fig. 5. 3 -- Square of the Association Matrix
`
`20
`
`020
`
`Facebook Ex. 1002 Part 3
`
`

`
`Fox Thesis Detail- No Analysis, No Generating
`
`Fox:
`lSI Collection: There is no ln data or
`submatrix for lSI. "Source-cited pairs"
`are co-cited pairs and thus cannot be a
`first numerical representation. See
`Response, p. 20, Fox, p. 181:
`
`SMART methods. Some 90,000 source-cited document number pairs were processed to
`
`yield co-citation Yalues for all pairs of cited documents. The final concept type scheme was
`
`tbererore obtained as shown in Table 6.3.
`
`Table 6.3: lSI Concept Types
`
`Number
`0
`1
`2
`
`Designation
`tm
`au
`cc
`
`Description
`terms
`authors
`co-citations
`
`Since the lSI collection lacked other types of bibliographically based information, and
`
`the CACM collection (see Table
`
`6.4), a more exlensi\'e test oi the multiple concept type appro:1eb was planned.
`
`CACM collection: The ln, co-citation
`(cc) and coupling (be) are produced from
`the same hand-compiled data see
`Response, pp. 18-20, Fox, p. 182:
`
`Table 6.4: CACM Conce~l Types
`
`Number
`0
`1
`2
`
`Desi~:~tion
`tm
`au
`bi
`
`3
`
`4
`s
`
`6
`
`cr
`
`ce
`be
`
`In
`
`Descri2tion
`terms
`authors
`uil..liographic
`information
`Computing Review
`categorie3
`co-citations
`bibliographic
`coupling
`links
`
`Referenees between all CACM anicles were