(12) United States Patent
`Herz
`
`111111
`
`1111111111111111111111111111111111111111111111111111111111111
`US006460036Bl
`US 6,460,036 Bl
`*Oct. 1, 2002
`
`(10) Patent No.:
`(45) Date of Patent:
`
`(54) SYSTEM AND METHOD FOR PROVIDING
`CUSTOMIZED ELECTRONIC NEWSPAPERS
`AND TARGET ADVERTISEMENTS
`
`(75)
`
`Inventor: Frederick S. M. Herz, Davis, WV
`(US)
`
`(73) Assignee: Pinpoint Incorporated, TX (US)
`
`( *) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`This patent is subject to a terminal dis(cid:173)
`claimer.
`
`(21) Appl. No.: 08/985,732
`
`(22)
`
`Filed:
`
`Dec. 5, 1997
`
`(63)
`
`(60)
`
`(51)
`
`(52)
`
`(58)
`
`(56)
`
`Related U.S. Application Data
`
`Continuation-in-part of application No. 08/346,425, filed on
`Nov. 28, 1994, now Pat. No. 5,758,25.
`Provisional application No. 60/032,462, filed on Dec. 9,
`1996.
`
`Int. Cl? ......................... G06F 17/30; G06F 17/60;
`G06F 15/16; H04H 9/00
`U.S. Cl. ............................... 707/10; 707/2; 725/14;
`709/217; 705/14
`Field of Search ...................... 707/103, 10, 103 R,
`707/2, 9; 395/200.36; 348/7; 705/14; 725/105,
`14; 709/217
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`4,706,080 A
`5,038,211 A *
`5,223,924 A *
`5,245,656 A
`5,301,109 A
`5,321,833 A
`5,323,240 A *
`5,331,554 A
`
`11/1987
`8/1991
`6/1993
`9/1993
`4/1994
`6/1994
`6/1994
`7/1994
`
`Sincoskie .............. 340/825.02
`Hallenbeck ................. 348/460
`Strubbe ... ... .. ... ... ... ... .. ... 348/7
`Loeb et a!. . . . . . . . . . . . . . . . . . . . 380/23
`Landauer eta!. ...... 364/419.19
`Chang eta!. ............... 395/600
`Amano eta!. .............. 348/731
`Graham ................. 364/419.07
`
`5,331,556 A
`
`7/1994 Black, Jr. eta!. ....... 364/419.08
`
`(List continued on next page.)
`
`OTHER PUBLICATIONS
`
`Miller et al "News on-demand for multimedia networks",
`ACM Press 1993, pp. 383-392.*
`King et al "Competitive Intelligence, Software Robots and
`the Internet: The NewsAlert Prototype", IEEE 1995, pp.
`624-631.*
`
`(List continued on next page.)
`
`Primary Examiner-Safet Metjahic
`Assistant Examiner-Uyen Le
`(74) Attorney, Agent, or Firm-Melvin A Hunn
`
`(57)
`
`ABSTRACT
`
`This invention relates to customized electronic identification
`of desirable objects, such as news articles, in an electronic
`media environment, and in particular to a system that
`automatically constructs both a "target profile" for each
`target object in the electronic media based, for example, on
`the frequency with which each word appears in an article
`relative to its overall frequency of use in all articles, as well
`as a "target profile interest summary" for each user, which
`target profile interest summary describes the user's interest
`level in various types of target objects. The system then
`evaluates the target profiles against the users' target profile
`interest summaries to generate a user-customized rank
`ordered listing of target objects most likely to be of interest
`to each user so that the user can select from among these
`potentially relevant target objects, which were automatically
`selected by this system from the plethora of target objects
`that are profiled on the electronic media. Users' target profile
`interest summaries can be used to efficiently organize the
`distribution of information in a large scale system consisting
`of many users interconnected by means of a communication
`network. Additionally, a cryptographically-based pseud(cid:173)
`onym proxy server is provided to ensure the privacy of a
`user's target profile interest summary, by giving the user
`control over the ability of third parties to access this sum(cid:173)
`mary and to identify or contact the user.
`
`20 Claims, 13 Drawing Sheets
`
`Petitioner Apple Inc. - Exhibit 1013, p. 1
`
`

`

`US 6,460,036 Bl
`Page 2
`
`U.S. PATENT DOCUMENTS
`5,410,344 A * 4/1995 Graves eta!. ................. 348/1
`5,444,499 A * 8/1995 Saitoh ........................ 348/734
`5,534,911 A * 7/1996 Levitan ......................... 348/1
`5,617,565 A * 4/1997 Augenbraun eta!. .......... 707/4
`5,649,186 A * 7/1997 Ferguson ..................... 707/10
`5,689,648 A * 11/1997 Diaz eta!. .................... 705/26
`5,717,923 A * 2/1998 Dedrick ...................... 707/102
`5,724,521 A * 3/1998 Dedrick ....................... 705/26
`5,740,549 A * 4/1998 Reilly eta!. .................. 705/14
`5,761,662 A * 6/1998 Dasan ......................... 707/10
`5,768,521 A * 6/1998 Dedrick ...................... 709/224
`5,805,156 A * 9/1998 Richmond et a!. .......... 345/328
`5,812,776 A * 9/1998 Gifford ....................... 709/217
`5,848,396 A * 12/1998 Gerace ........................ 705/10
`5,933,811 A * 8/1999 Angles eta!. ................ 705/14
`5,945,988 A * 8/1999 Williams et a!. ............ 345/327
`5,991,735 A * 11/1999 Gerace ........................ 705/10
`6,088,722 A * 7/2000 Herz et a!. .................. 709/217
`
`OTHER PUBLICATIONS
`Chesnais et al "The Fishwrap Personalized News System",
`IEEE 1995, pp. 275-282.*
`Seno et al "Multimedia Information Broadcasting Service
`PRESENT", IEEE 1994, pp. 117-120.*
`Haas et al "Secure Access to Electronic Newspaper", IEEE/
`ICCC 1994, pp. 805-809.*
`"Scatter/Gather: A Cluster-based Approach to Browsing
`Large Document Collections" by Cutting et al., 15th Ann
`Int'l Sigir '92, ACM 318-329, 1992.
`"Evolving Agents For Personalized Information Filtering",
`Sheth et al., Proc. 9th IEEE Conference on AI for Applica(cid:173)
`tions, 1993.
`"A Secure And Privacy-Protecting Protocol For Transmit(cid:173)
`ting Personal Information Between Organizations" Chaum
`et al. pp. 118-167, no date.
`* cited by examiner
`
`Petitioner Apple Inc. - Exhibit 1013, p. 2
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 1 of 13
`
`US 6,460,036 Bl
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`Petitioner Apple Inc. - Exhibit 1013, p. 3
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 2 of 13
`
`US 6,460,036 Bl
`
`C")
`(f)
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`
`Petitioner Apple Inc. - Exhibit 1013, p. 4
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 3 of 13
`
`US 6,460,036 Bl
`
`FIG. 3
`
`D..__
`-------- s
`
`/A-----r
`
`p~ /
`,..... B - - - - - - - - C
`
`/
`q
`
`FIG. 4
`
`o __
`
`-------s
`
`Petitioner Apple Inc. - Exhibit 1013, p. 5
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 4 of 13
`
`US 6,460,036 Bl
`
`501 ------~
`
`f~ETRIEVE NEW DOCUMENT
`FROM DOCUMENT SOURCE
`
`DOCUMENT PROFILES
`
`[ CALCULATE
`,,
`
`CLUSTER DOCUMENTS INTO
`A HIERARCHICAL CLUSTER
`
`~,
`
`[ GENERATE LABELS
`
`FOR EACH CLUSTER
`
`505---------
`
`GENERATE MENUS FROM
`CLUSTER STRUCTURE
`AND LABELS
`
`~_____.I
`506 ~------T:oNITOR DOCUMENT ACTIVITY
`L AND ADJUST PROFILE
`
`FIG. 5
`
`Petitioner Apple Inc. - Exhibit 1013, p. 6
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 5 of 13
`
`US 6,460,036 Bl
`
`FIG. 6
`
`x2
`
`•
`a
`
`•
`g
`
`•
`h
`•
`i
`
`•
`k
`•
`j
`
`•
`b
`
`•
`c
`
`•
`d
`
`•
`e
`
`•
`f
`
`....
`x1
`
`(a, b, c, d, e, f, g, h, i, j, k, I )
`
`/ /(
`
`b
`
`a
`
`(c, f)
`
`d
`
`c
`
`f
`
`FIG. 7
`
`(!J, k)
`
`,1\
`
`e g
`
`k
`
`.......
`J
`
`(h, i)
`
`1\
`
`h
`
`Petitioner Apple Inc. - Exhibit 1013, p. 7
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 6 of 13
`
`US 6,460,036 Bl
`
`/
`
`I
`
`c
`
`a
`
`b
`
`d
`
`f
`
`e
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`
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`
`FIG. 8
`
`/ a
`
`b
`
`d
`
`c
`
`f
`
`e
`
`FIG. 9
`
`Petitioner Apple Inc. - Exhibit 1013, p. 8
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 7 of 13
`
`US 6,460,036 Bl
`
`1101--[
`
`USER LOG IN
`
`1102 --~~
`
`USER ACCESSES NEWS
`
`,
`
`1103~/-[
`
`COMPARE PROFILES AND
`SELECT ARTICLES
`
`1104 --- {
`
`PRESENT LIST TO USER
`
`~
`11 05 ~{ USER SELECTS ARTICLE
`~
`11 06 --~----J~>ERVER DELIVERS ARTICLE
`L
`
`TO USER
`
`~
`
`[
`11 07 _-------.
`
`MONITOR WHICH
`_A_RT-IC_L_ES-rA--R-E _REA_D _ __,
`
`~
`
`1108 _/ ____
`
`UPDATE USER
`PREFERENCE PROFILES
`
`FIG. 10
`
`Petitioner Apple Inc. - Exhibit 1013, p. 9
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 8 of 13
`
`US 6,460,036 Bl
`
`p
`
`A - - -
`
`~/ B q/
`
`FIG. 11
`
`1201
`ATTRIBUTES FOR ~
`TARGET OBJECT
`
`lr
`
`WEIGHT AS A FUNCTION " -
`OF THE USER
`
`1202
`
`FIG. 12 [ DETERMINE SET OF
`[ ASSIGN ATTRIBUTE
`[ COMPUTE WEIGHTED
`
`r
`
`SUM OF SELECTED
`NORMATIVE ATTRIBUTES
`OF TARGET OBJECT
`
`~----~
`
`1203
`
`r
`
`RETRIEVE SUMMARIZED
`WEIGHTED RELEVANCE
`FEEDBACK DATA
`
`-------- 1204
`
`,
`
`COMPUTE TOPICAL INTEREST
`OF TARGET OBJECT FOR
`SELECTED USER BASED ON
`RELEVANCE FEEDBACK
`FROM ALL USERS
`
`--~
`
`1205
`
`Petitioner Apple Inc. - Exhibit 1013, p. 10
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 9 of 13
`
`US 6,460,036 Bl
`
`FIG. 13A
`
`INITIALIZE LIST OF
`TARGET OBJECTS TO THE ~1 3A-OO
`EMPTY LIST
`
`~r
`
`INITIALIZE CURRENT TREE TO
`THE HEIRARCHICAL CLUSTER ~ 13A-01
`TREE OF ALL OBJECTS
`
`~r
`
`SCAN CURRENT TREE FOR
`TARGET OBJECTS SIMILAR
`TO P. USING PROCESS 13B
`
`,,
`
`---------------
`
`RETURN LIST OF
`TARGET OBJECTS ~ 1 ~3A-03
`
`Petitioner Apple Inc. - Exhibit 1013, p. 11
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 10 of 13
`
`US 6,460,036 Bl
`
`FIG. 138
`
`i = 1'
`n = NUMBER OF CHILD ~ 138-00
`SUBTREES OF CURRENT TREE
`
`[
`
`[
`
`RETRIEVE jth CHILD SUBTREE
`OF CURRENT TREE
`
`-----138-01
`
`CALCULATE d(P, pi)
`WHERE Pi IS
`PROFILE OF i1h
`CHILD SUBTREE
`- , - - - - - - '
`
`-138-02
`
`NO
`
`d(P, pi) <t
`?
`
`138-03
`
`138-04
`
`YES
`
`138-06
`\
`
`SCAN jth CHILD SUBTREE
`FOR TARGET OBJECTS SIMILAR
`TO P BY INVOKING PROCESS
`13B RECURSIVELY
`
`1
`
`138-05
`
`ADD TARGET OBJECT
`TO LIST OF
`TARGET OBJECTS
`
`i = i + 1
`
`~-138-07
`
`138-08
`
`NO
`
`(
`
`RETURN
`
`)
`
`Petitioner Apple Inc. - Exhibit 1013, p. 12
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 11 of 13
`
`US 6,460,036 Bl
`
`FIG. 14
`
`USER GENERATES
`A PSEUDONYM
`
`~
`
`~1400
`
`,
`
`PSEUDONYM IS ENCRYPTED
`
`- '--1401
`
`,
`
`USER SELECTS SERVICE
`PROVIDER IDENTIFIER
`
`-
`
`'--- 1402
`
`,,
`
`USER BLINDS PSEUDONYM
`& PROVIDER IDENTIFIER
`WITH RANDOM FACTOR
`--
`
`,,
`
`-~1403
`
`TRANSMIT SIGNED MESSAGE
`TO VALIDATING AGENCY
`SERVER
`
`~ ~1404
`
`~,
`
`VALIDATION SERVER RECEIVES --'---1405
`AND VERIFIES MESSAGE
`
`,,.
`
`VALIDATION SERVER SIGNS
`PSEUDONYM AND RETURNS
`TO USER
`
`~ '--1406
`
`~r
`
`USER IS IN RECEIPT OF
`VALIDATED PSEUDONYM
`
`------1407
`
`Petitioner Apple Inc. - Exhibit 1013, p. 13
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 12 of 13
`
`US 6,460,036 Bl
`
`FIG. 15
`
`CLIENT PROCESSOR FORMS
`ENCRYPTED MESSAGE WITH
`SIGNED VALIDATED PSEUDONYM
`
`MESSAGE IS ROUTED TO
`PROXY SERVER
`
`PROXY SERVER
`DECODES MESSAGE
`
`PROXY SERVER FORWARDS
`MESSAGE TO IDENTIFIED
`INFORMATION SERVER
`
`INFORMATION SERVER
`PROCESSES RECEIVED
`REQUEST
`
`INFORMATION SERVER
`TRANSMITS RESPONSE
`TO PROXY SERVER
`
`•
`•
`•
`+
`•
`t
`
`~ ----~-1500
`
`~
`
`~~1501
`
`~
`
`----~ 1502
`
`1--~~~1503
`
`-------1504
`1--
`
`~
`
`~--------1505
`
`PROXY SERVER CREATES
`RESPONSE MESSAGE
`TO USER
`
`------~-1506
`
`CLIENT PROCESSOR
`TABULATES USER INTEREST
`
`~---- 1507
`
`+
`•
`
`CLIENT PROCESSOR TRANSMITS
`MESSAGE TO PROXY SERVER ~~1508
`TO UPDATE PROFILE
`INTEREST SUMMARY
`
`Petitioner Apple Inc. - Exhibit 1013, p. 14
`
`

`

`U.S. Patent
`
`Oct. 1, 2002
`
`Sheet 13 of 13
`
`US 6,460,036 Bl
`
`FIG. 16
`
`USER ESTABLISHES
`COMMUNICATION CONNECTION
`WITH NETWORK VENDOR
`
`-~-1600
`
`•
`I USER ACTIVATES BROWSING PROGRAM ~-1601
`~
`
`USER INPUTS QUERY
`
`~1602
`
`[
`
`!
`•
`
`[NETWORK VENDOR FORWARDS
`QUERY TO IDENTIFIED GENERAL
`INFORMATION SERVER
`
`GENERAL INFORMATION SERVER
`MATCHES QUERY PROFILE AGAINST
`CLUSTER PROFILES TO LOCATE
`SPECIFIC INFORMATION SERVER TO
`SERVE THE RECEIVED QUERY
`
`~1603
`
`~1604
`
`[SPECIFIC INFORMATION SERVER
`DETERMINES DEGREE OF MATCH
`WITH SPECIFIC CLUSTER
`
`~1605
`
`~NETWORK VENDOR TRANSMITS
`OMPUTED DEGREE OF MATCH FOR
`CH INFORMATION SERVER TO USER
`
`~1606
`
`+
`•
`+
`~ER SELECTS IDENTIFIED CLUSTER ~-1607
`~
`
`CCLIENT PROCESSOR TRANSMITS
`ELECTION TO NETWORK VENDOR
`
`• G NETWORK VENDOR RETRIEVES
`
`DENTIFIED TARGET OBJECT AND
`RANSMITS TO CLIENT PROCESSOR
`
`-~ 1608
`
`~1609
`
`Petitioner Apple Inc. - Exhibit 1013, p. 15
`
`

`

`US 6,460,036 Bl
`
`1
`SYSTEM AND METHOD FOR PROVIDING
`CUSTOMIZED ELECTRONIC NEWSPAPERS
`AND TARGET ADVERTISEMENTS
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`This patent application was originally filed as provisional
`application Serial No. 60/032,462, filed on Dec. 9, 1996 and
`is a continuation-in-part of U.S. patent application Ser. No.
`08/346,425, filed Nov. 28, 1994 now U.S. Pat. No. 5,758,
`257, and titled "SYSTEM AND ME1HOD FOR SCHED(cid:173)
`ULING BROADCAST OF AND ACCESS TO VIDEO
`PROGRAMS AND 01HER DATA USING CUSTOMER
`PROFILES", which application is assigned to the same
`assignee as the present application.
`
`FIELD OF INVENTION
`
`5
`
`2
`also no existing system which automatically estimates the
`inherent quality of a n article or other target object to
`distinguish among a number of articles or target objects
`identified as of possible interest to a user.
`Therefore, in the field of information retrieval, there is a
`long-standing need for a system which enables users to
`navigate through the plethora of information. With commer(cid:173)
`cialization of communication networks, such as the Internet,
`the growth of available information has increased. Customi-
`10 zation of the information delivery process to the user's
`unique tastes and interests is the ultimate solution to this
`problem. However, the techniques which have been pro(cid:173)
`posed to date either only address the user's interests on a
`superficial level or provide greater depth and intelligence at
`15 the cost of unwanted demands on the user's time and energy.
`While many researchers have agreed that traditional meth(cid:173)
`ods have been lacking in this regard, no one to date has
`successfully addressed these problems in a holistic manner
`and provided a system that can fully learn and reflect the
`20 user's tastes and interests. This is particularly true in a
`practical commercial context, such as on-line services avail(cid:173)
`able on the Internet. There is a need for an information
`retrieval system, that is largely or entirely passive,
`unobtrusive, undemanding of the user, and yet both precise
`and comprehensive in its ability to learn and truly represent
`the user's tastes and interests. Present information retrieval
`systems require the user to specify the desired information
`retrieval behavior through cumbersome interfaces.
`Users may receive information on a computer network
`either by actively retrieving the information or by passively
`receiving information that is sent to them. Just as users of
`information retrieval systems face the problem of too much
`information, so do users who are targeted with electronic
`junk mail by individuals and organizations. An ideal system
`would protect the user from unsolicited advertising, both by
`automatically extracting only the most relevant messages
`received by electronic mail, and by preserving the confi(cid:173)
`dentiality of the user's preferences, which should not be
`freely available to others on the network.
`Researchers in the field of published article information
`retrieval have devoted considerable effort to finding efficient
`and accurate methods of allowing users to select articles of
`interest from a large set of articles. The most widely used
`methods of information retrieval are based on keyword
`45 matching: the user specifies a set of keywords which the user
`thinks are exclusively found in the desired articles and the
`information retrieval computer retrieves all articles which
`contain those keywords. Such methods are fast, but are
`notoriously unreliable, as users may not think of the right
`50 keywords, or the keywords may be used in unwanted articles
`in an irrelevant or unexpected context. As a result, the
`information retrieval computers retrieve many articles
`which are unwanted by the user. The logical combination of
`keywords and the use of wild-card search parameters help
`55 improve the accuracy of keyword searching but do not
`completely solve the problem of inaccurate search results.
`Starting in the 1960's, an alternate approach to information
`retrieval was developed: users were presented with an article
`and asked if it contained the information they wanted, or to
`60 quantify how close the information contained in the article
`was to what they wanted. Each article was described by a
`profile which comprised either a list of the words in the
`article or, in more advanced systems, a table of word
`frequencies in the article. Since a measure of similarity
`65 between articles is the distance between their profiles, the
`measured similarity of article profiles can be used in article
`retrieval. For example, a user searching for information on
`
`This invention relates to customized electronic identifi(cid:173)
`cation of desirable objects, such as news articles, in an
`electronic media environment, and in particular to a system
`that automatically constructs both a "target profile" for each
`target object in the electronic media based, for example, on
`the frequency with which each word appears in an article
`relative to its overall frequency of use in all articles, as well 25
`as a "target profile interest summary" for each user, which
`target profile interest summary describes the user's interest
`level in various types of tar get objects. The system then
`evaluates the target profiles against the users' target profile
`interest summaries to generate a user-customized rank 30
`ordered listing of target objects most likely to be of interest
`to each user so that the user can select from among these
`potentially relevant target objects, which were automatically
`selected by this system from the plethora of target objects
`that are profiled. on the electronic media. Users' target 35
`profile interest summaries can be used to efficiently organize
`the distribution of information in a large scale system
`consisting of many users interconnected by means of a
`communication network. Additionally, a cryptographically
`based proxy server is provided to ensure privacy of a user's 40
`target profile interest summary, by giving the user control
`over the ability of third parties to access this summary and
`to identify or contact the user.
`
`PROBLEM
`
`It is a problem in the field of electronic media to enable
`a user to access information of relevance and interest to the
`user without requiring the user to expend an excessive
`amount of time and energy searching for the information.
`Electronic media, such as on-line information sources, pro(cid:173)
`vide a vast amount of information to users, typically in the
`form of "articles," each of which comprises a publication
`item, or document that relates to a specific topic. The
`difficulty with electronic media is that the amount of infor(cid:173)
`mation available to the user is overwhelming and the article
`repository systems that are connected on-line are not orga(cid:173)
`nized in a manner that sufficiently simplifies access to only
`the articles-of interest to the user. Presently, a user either
`fails to access relevant articles because they are not easily
`identified or expends a significant amount of time and
`energy to conduct an exhaustive search of all articles to
`identify those most likely to be of interest to the user.
`Furthermore, even if the user conducts an exhaustive search,
`present information searching techniques do not necessarily
`accurately extract only the most relevant articles, but also
`present articles of marginal relevance due to the functional
`limitations of the information searching techniques. There is
`
`Petitioner Apple Inc. - Exhibit 1013, p. 16
`
`

`

`US 6,460,036 Bl
`
`3
`4
`a subject can write a short description of the desired infor(cid:173)
`key words for indexing by picking the words with the largest
`mation. The information retrieval computer generates an
`TF/IDF (where TF is term (word) frequency and IDF is the
`article profile for the request and then retrieves articles with
`inverse document frequency) and label piles by using the
`profiles similar to the profile generated for the request. These
`determined key words.
`requests can then be refined using "relevance feedback", 5
`Numerous patents address information retrieval methods,
`where the user actively or passively rates the articles
`but none develop records of a user's interest based on
`retrieved as to how close the information contained therein
`passive monitoring of which articles the user accesses. None
`is to what is desired. The information retrieval computer
`of the systems described in these patents pre sent computer
`then uses this relevance feedback information to refine the
`architectures to allow fast retrieval of articles distributed
`request profile and the process is repeated until the user 10
`across many computers. None of the systems described in
`either finds enough articles or tires of the search.
`these patents address issues of using such article retrieval
`A number of researchers have looked at methods for
`and matching methods for purposes of commerce or of
`selecting articles of most interest to users. An article titled
`matching users with common interests or developing records
`"Social Information filtering: algorithms for automating
`of users' interests. U.S. Pat. No. 5,321,833 issued to Chang
`'word of mouth"' was published at the CHi-95 Proceedings 15 et al. teaches a method in which users choose terms to use
`by Patti Maes et al and describes the Ringo information
`in an information retrieval query, and specify the relative
`retrieval system which recommends musical selections. The
`weightings of the different terms. The Chang system then
`Ringo system requires active feedback from the users(cid:173)
`calculates multiple levels of weighting criteria. U.S. Pat. No.
`users must manually specify how much they like or dislike
`5,301,109 issued to Landauer et al teaches a method for
`each musical selection. The Ringo system maintains a 20
`retrieving articles in a multiplicity of languages by con(cid:173)
`complete list of users ratings of music selections and makes
`structing "latent vectors" (SVD or PCA vectors) which
`recommendations by finding which selections were liked by
`represent correlations between the different words. U.S. Pat.
`multiple people. However, the Ringo system does not take
`No. 5,331,554 issued to Graham et al. discloses a method for
`advantage of any available descriptions of the music, such as
`retrieving segments of a manual by comparing a query with
`structured descriptions in a data base, or free text, such as 25
`nodes in a decision tree. U.S. Pat. No. 5,331,556 addresses
`that contained in music reviews. An article titled "Evolving
`techniques for deriving morphological part-of-speech infor(cid:173)
`agents for personalized information filtering", published at
`mation and thus to make :use of the similarities of different
`the Proc. 9th IEEE Conf on AI for Applications by Sheth and
`forms of the same word (e.g. "article" and "articles").
`Maes, described the use of agents for information filtering
`Therefore, there presently is no information retrieval and
`which use genetic algorithms to learn to categorize Usenet 30
`delivery system operable in an electronic media environ(cid:173)
`news articles. In this system, users must define news cat(cid:173)
`ment that enables a user to access information of relevance
`egories and the users actively indicate their opinion of the
`and interest to the user without requiring the user to expend
`selected articles. Their system uses a list of keywords to
`an excessive amount of time and energy.
`represent sets of articles and the records of users' interests
`are updated using genetic algorithms.
`A number of other research groups have looked at the
`automatic generation and labeling of clusters of articles for
`the purpose of browsing through the articles. A group at
`Xerox Pare published a paper titled "Scatter/gather: a
`cluster-based approach to browsing large article collections" 40
`at the 15 Ann. Int'l SIGIR '92,ACM 318-329 (Cutting et al.
`1992). This group developed a method they call "scatter/
`gather" for performing information retrieval searches. In this
`method, a collection of articles is "scattered" into a small
`number of clusters, the user then chooses one or more of 45
`these clusters based on short summaries of the cluster. The
`selected clusters are then "gathered" into a subcollection,
`and then the process is repeated. Each iteration of this
`process is expected to produce a small, more focused
`collection. The cluster "summaries" are generated by pick- 50
`ing those words which appear most frequently in the cluster
`and the titles of those articles closest to the center of the
`cluster. However, no feedback from users is collected or
`stored, so no performance improvement occurs over time.
`Apple's Advanced Technology Group has developed an
`interface based on the concept of a "pile of articles". This
`interface is described in an article titled '"A pile' metaphor
`for supporting casual organization of information in Human
`factors in computer systems" published in CHI '92 Conf.
`Proc. 627-634 by Mander, R. G. Salomon and Y. Wong.
`1992. Another article titled "Content awareness in a file
`system interface: implementing the 'pile' metaphor for orga(cid:173)
`nizing information" was published in 16 Ann. Int'l SIGIR
`'93, ACM 260--269 by Rose E. D. et al. The Apple interface
`uses word frequencies to automatically file articles by pick(cid:173)
`ing the pile most similar to the article being filed. This
`system functions to cluster articles into subpiles, determine
`
`The above-described problems are solved and a technical
`advance achieved in the field by the system for customized
`electronic identification of desirable objects in an electronic
`media environment, which system enables a user to access
`target objects of relevance and interest to the user without
`requiring the user to expend an excessive amount of time
`and energy. Profiles of the target objects are stored on
`electronic media and are accessible via a data communica(cid:173)
`tion network. In many applications, the target objects are
`informational In nature, and so may themselves be stored on
`electronic media and be accessible via a data communication
`network.
`Relevant definitions of terms for the purpose of this
`description include: (a.) an object available for access by the
`user, which may be either physical or electronic in nature, is
`termed a "target object", (b.) a digitally represented profile
`indicating t hat target object's attributes is termed a "target
`profile", (c.) the user looking for the target object is termed
`55 a "user", (d.) a profile holding that user's attributes, includ(cid:173)
`ing age/zip code/etc. is termed a "user profile", (e.) a
`summary of digital profiles of target objects that a user likes
`and/or dislikes, is termed the "target profile interest sum(cid:173)
`mary" of that user, (f) a profile consisting of a collection of
`60 attributes, such that a user likes target objects whose profiles
`are similar to this collection, of attributes, is termed a
`"search profile" or in some contexts a "query" or "query
`profile," (g.) a specific embodiment of the target profile
`interest summary which comprises a set of search profiles is
`65 termed the "search profile set" of a user, (h.) a collection of
`target objects with similar profiles, is termed a "cluster," (i.)
`an aggregate profile formed by averaging the attributes of all
`
`35
`
`SOLUTION
`
`Petitioner Apple Inc. - Exhibit 1013, p. 17
`
`

`

`US 6,460,036 Bl
`
`5
`tar get objects in a cluster, termed a "cluster profile," Q.) a
`real number determined by calculating the statistical vari(cid:173)
`ance of the profiles of all target objects in a cluster, is termed
`a "cluster variance," (k.) a real number determined by
`calculating the maximum distance between the profiles of 5
`any two target objects in a cluster, is termed a "cluster
`diameter."
`The system for electronic identification of desirable
`objects of the present invention automatically constructs
`both a target profile for each target object in the electronic
`media based, for example, on the frequency with which each
`word appears in an article relative to its overall frequency of
`use in all articles, as well as a "target profile interest
`summary" for each user, which target profile interest sum(cid:173)
`mary describes the user's interest level in various types of
`target objects. The system then evaluates the target profiles
`against the users' target profile interest summaries to gen(cid:173)
`erate a user-customized rank ordered listing of tar get objects
`most likely to be of interest to each user so that the user can
`select from among these potentially relevant target objects,
`which were automatically selected by this system from the
`plethora of target objects available on the electronic media.
`Because people have multiple interests, a target profile
`interest, summary for a single user must represent multiple
`areas of interest, for example, by consisting of a set of
`individual search profiles, each of which identifies one of the
`user's areas of interest. Each user is presented with those
`target objects whose profiles most closely match the user's
`interests as described by the user's target profile interest
`summary. Users' target profile interest summaries are auto(cid:173)
`matically updated on a continuing basis to reflect each user's
`changing interests. In addition, target objects can be grouped
`into clusters based on their similarity to each other, for
`example, based on similarity of their topics in the case where
`the target objects are published articles; and menus auto(cid:173)
`matically generated for each cluster of target objects to allow
`users to navigate throughout the clusters and manually
`locate target objects of interest. For reasons of confidenti(cid:173)
`ality and privacy, a particular user may not wish to make
`public all of the interests recorded in the user's target profile
`interest summary, particularly when these interests are deter(cid:173)
`mined by the user's purchasing patterns. The user may
`desire that all or part of the target profile interest summary
`be kept confidential, such as information relating to the
`user's political, religious, financial or purchasing behavior;
`indeed, confidentiality with respect to purchasing behavior
`is the user's legal right in many states. It is therefore
`necessary that data in a user's target profile interest summary
`be protected from unwanted disclosure except with the
`user's agreement. At the same time, the user's target profile
`interest summaries must be accessible to the relevant servers
`that perform the matching of target objects to the users, if the
`benefit of this matching is desired by both providers and
`consumers of the target objects. The disclosed system pro(cid:173)
`vides a solution to the privacy problem by using a proxy
`server which acts as an intermediary between the informa(cid:173)
`tion provider and the user. The proxy server dissociates the
`user's true identity from the pseudonym by the use of
`cryptographic techniques. The proxy server also permits
`users to control access to their target profile interest sum(cid:173)
`maries and/or user profiles, including provision of this
`information to marketers and advertisers if they so desire,
`possibly in exchange for cash or other considerations. Mar(cid:173)
`keters may purchase these profiles in order to target adver(cid:173)
`tisements to particular users, or they may purchase partial
`user profiles, which do not include enough information to
`identify the individual users in question, in order to carry out
`
`6
`standard kinds of demographic analysis and market research
`on the resulting database of partial user profiles.
`In the preferred embodiment of the invention, the system