Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 1 of 44
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`EXHIBIT F
`(Part 1 of 2)
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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 6 of 44
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`U.S. Patent
`
`l'eb. 22, 2000
`
`Sheet 4 of 13
`
`6,029,195
`
`501 ~-------
`
`RETRIEVE NEW DOCUMENT
`FROM DOCUMENT SOURCE
`
`p
`
`502 -----
`
`CALCULATE
`DOCUMENT PROFILES
`
`~ r
`
`503 ------- Cl1JSTER DOCUMENTS INTO
`
`A HIERARCHICAL CLUSTER
`
`,,
`
`504 ~ GENERA TE LABELS
`FOR EACH CLUSTER
`
`H
`
`GENERATE MENUS FROM
`505 ~ CLUSTER STRUCTURE
`r\ND LABELS
`
`H
`
`506 ------- MONITOR DOCUMENT ACTIVITY
`
`AND ADJUST PROFILE
`
`FIG. 5
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`TT0007180
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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 8 of 44
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`U.S. Patent
`
`l'eb. 22, 2000
`
`Sheet 6 of 13
`
`6,029,195
`
`a
`
`b
`
`d
`
`C
`
`f
`
`e
`
`g
`
`k
`
`h
`
`FIG. 8
`
`FIG. 9
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`TT0007182
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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 10 of 44
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`U.S. Patent
`
`l'eb. 22, 2000
`
`Sheet 8 of 13
`
`6,029,195
`
`p
`
`A - - -~/ B q/
`
`FIG. 11
`
`FIG. 12
`
`DETERMINE SET OF
`ATTRIBUTES FOR ~ 1201
`TARGET OBJECT
`
`u
`
`ASSIGN ATTRIBUTE
`WEIGHT AS A FUNCTION
`OF THE USER
`
`r"-1202
`
`u
`COMPUTE WEIGHTED
`SUM OF SELECTED
`NORMATIVE ATTRIBUTES
`OF TARGET OBJECT
`
`r---- 1203
`
`, J
`
`RETRIEVE SUMMARIZED
`WEIGHTED RELEVANCE
`FEEDBACK DATA
`
`------- 1204
`
`1 r
`
`COMPUTE TOPICAL INTEREST
`OF TARGET OBJECT FOR
`SELECTED USER BASED ON
`RELEVANCE FEEDBACK
`FROM ALL USERS
`
`1205
`
`----------
`
`TT0007184
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`

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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 11 of 44
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`U.S. Patent
`
`l'eb. 22, 2000
`
`Sheet 9 of 13
`
`6,029,195
`
`FIG. 13A
`
`INITIALIZE LIST OF
`TARGET OBJECTS TO THE ~1 3A-OO
`EMPTY LIST
`
`H
`
`INITIALIZE CURRENT TREE TO
`THE HEIRARCHICAL CLUSTER r---- 13A-O1
`TREE OF ALL OBJECTS
`
`H
`
`SCAN CURRENT TREE FOR
`TARGET OBJECTS SIMILAR
`TO P. USING PROCESS 138
`
`------...._ 13A-O2
`
`H
`
`RETURN LIST OF
`TARGET OBJECTS ~ 13A-O3
`
`TT0007185
`
`

`

`

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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 13 of 44
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`U.S. Patent
`
`:Feb. 22, 2000
`
`Sheet 11 of 13
`
`6,029,195
`
`FIG. 14
`
`USER GENERATES
`A PSEUDONYM
`
`f----._
`
`1400
`
`u
`
`PSEUDONYM IS ENCRYPTED
`
`------ 1401
`
`~·
`
`USER SELECTS SERVICE
`PROVIDER IDENTIFIER
`
`1402
`r----_
`
`1 r
`
`USER BLINDS PSEUDONYM
`& PROVIDER IDENTIFIER
`WITH RANDOM FACTOR
`
`------- 1403
`
`, '
`TRANSMIT SIGNED MESSAGE
`TO VALIDATING AGENCY
`SERVER
`
`I,--._ 1404
`
`1J
`
`VALIDATION SERVER RECEIVES
`AND VERIFIES MESSAGE
`
`i-----..___ 1405
`
`1 r
`
`VALIDATION SERVER SIGNS
`PSEUDONYM AND RETURNS
`TO USER
`
`1406
`
`---------
`
`~'
`
`USER IS IN RECEIPT OF
`VALIDATED PSEUDONYM
`
`-----
`
`1407
`
`TT0007187
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`

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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 14 of 44
`
`U.S. Patent
`
`Feb.22,2000
`
`Sheet 12 of 13
`
`6,029,195
`
`FIG. 15
`
`CLIENT PROCESSOR FORMS
`ENCRYPTED MESSAGE WITH
`SIGNED VALIDATED PSEUDONYM
`
`1500
`
`MESSAGE IS ROUTED TO
`PROXY SERVER
`
`1501
`
`PROXY SERVER
`DECODES MESSAGE
`
`1502
`
`PROXY SERVER FORWARDS
`MESSAGE TO IDENTIFIED
`INFORMATION SERVER
`
`INFORMATION SERVER
`PROCESSES RECEIVED
`REQUEST
`
`INFORMATION SERVER
`TRANSMITS RESPONSE
`TO PROXY SERVER
`
`PROXY SERVER CREATES
`RESPONSE MESSAGE
`TO USER
`
`1503
`
`1504
`
`1505
`
`1506
`
`CLIENT PROCESSOR
`TABULA TES USER INTEREST
`
`1507
`
`CLIENT PROCESSOR TRANSMITS
`MESSAGE TO PROXY SERVER
`TO UPDATE PROFILE
`INTEREST SUMMARY
`
`1508
`
`TT0007188
`
`

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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 15 of 44
`
`U.S. Patent
`
`Feb.22,2000
`
`Sheet 13 of 13
`
`6,029,195
`
`FIG. 16
`
`USER ESTABLISHES
`COMMUNICATION CONNECTION
`WITH NETWORK VENDOR
`
`I"--- 1600
`
`USER ACTIVATES BROWSING PROGRAM ------------ 1601
`
`•
`+
`i
`INFORMATION SERVER •
`•
`•
`•
`+
`•
`
`USER INPUTS QUERY
`
`NETWORK VENDOR FORWARDS
`QUERY TO IDENTIFIED GENERAL
`
`I'- 1602
`
`---...____ 1603
`
`GENERAL INFORMATION SERVER
`MATCHES QUERY PROFILE AGAINST
`CLUSTER PROFILES TO LOCATE ~ 1604
`SPECIFIC INFORMATION SERVER TO
`SERVE THE RECEIVED QUERY
`
`SPECIFIC INFORMATION SERVER
`DETERMINES DEGREE OF MATCH ~ 1605
`WITH SPECIFIC CLUSTER
`
`NETWORK VENDOR TRANSMITS
`COMPUTED DEGREE OF MATCH FOR ~ 1606
`EACH INFORMATION SERVER TO USER
`
`USER SELECTS IDENTIFIED CLUSTER
`
`1607
`
`-----------
`
`CLIENT PROCESSOR TRANSMITS ~
`1608
`SELECTION TO NETWORK VENDOR
`
`NETWORK VENDOR RETRIEVES
`IDENTIFIED TARGET OBJECT AND
`TRANSMITS TO CLIENT PROCESSOR
`
`~ 1609
`
`TT0007189
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`

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`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 16 of 44
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`6,029,195
`
`1
`SYSTEM FOR CUSTOMIZED ELECTRONIC
`IDENTIFICATION OF DESIRABLE OBJECTS
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`This patent application was originally filed as Provisional
`Patent Application Ser. No. 60/032,461 on Dec. 9, 1996 and
`is a continuation-in-part of U.S. patent application Ser. No.
`08/346,425, filed Nov. 29, 1994, now U.S. Pat. No. 5,758,
`257 and titled "SYSTEM AND METHOD FOR SCHED(cid:173)
`ULING BROADCAST OF AND ACCESS TO VIDEO
`PROGRAMS AND CY111ER D!XI'A USING CUSTOMER
`PROFILES", which application is assigned to the same
`assignee as the present application.
`
`FIELD OF INVENTION
`
`5
`
`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
`as a "target profile interest summary" for each user, which 25
`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 30
`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 35
`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 the privacy of a user's target
`profile interest summary, by giving the user control over the 40
`ability of third parties to access this summary and to identify
`or contact the user.
`
`2
`ent quality of an 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(cid:173)
`zation of the information delivery process to the user's
`10 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
`the cost of unwanted demands on the user's time and energy.
`15 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
`user's tastes and interests. This is particularly true in a
`20 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-
`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
`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
`keywords, or the keywords may be used in unwanted articles
`50 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
`improve the accuracy of keyword searching but do not
`55 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
`quantify how close the information contained in the article
`60 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
`between articles is the distance between their profiles, the
`65 measured similarity of article profiles can be used in article
`retrieval. For example, a user searching for information on
`a subject can write a short description of the desired infor-
`
`PROBLEM
`
`It is a problem in the field of electronic media to enable 45
`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 iden(cid:173)
`tified 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 informa(cid:173)
`tion searching techniques do not necessarily accurately
`extract only the most relevant articles, but also present
`articles of marginal relevance due to the functional limita(cid:173)
`tions of the information searching techniques. There is also
`no existing system which automatically estimates the inher-
`
`TT0007190
`
`

`

`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 17 of 44
`
`6,029,195
`
`4
`TF;1DF (where TF is term (word) frequency and IDF is the
`inverse document frequency) and label piles by using the
`determined key words.
`Numerous patents address information retrieval methods,
`but none develop records of a user's interest based on
`passive monitoring of which articles the user accesses. None
`of the systems described in these patents pre sent computer
`architectures to allow fast retrieval of articles distributed
`across many computers. None of the systems described in
`10 these patents address issues of using such article retrieval
`and matching methods for purposes of commerce or of
`matching users with common interests or developing records
`of users' interests. U.S. Pat. No. 5,321,833 issued to Chang
`et al. teaches a method in which users choose terms to use
`in an information retrieval query, and specify the relative
`weightings of the different terms. The Chang system then
`calculates multiple levels of weighting criteria. U.S. Pat. No.
`5,301,109 issued to Landauer et al. teaches a method for
`retrieving articles in a multiplicity of languages by con(cid:173)
`structing "latent vectors" (SYD or PCA vectors) which
`represent correlations between the different words. U.S. Pat.
`No. 5,331,554 issued to Graham et al. discloses a method for
`retrieving segments of a manual by comparing a query with
`nodes in a decision tree. U.S. Pat. No. 5,331,556 addresses
`techniques for deriving morphological part-of-speech infor(cid:173)
`mation and thus to make use of the similarities of different
`forms of the same word (e.g. "article" and "articles").
`Therefore, there presently is no information retrieval and
`delivery system operable in an electronic media environ-
`30 ment that enables 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.
`
`3
`mation. The information retrieval computer generates an
`article profile for the request and then retrieves articles with
`profiles similar to the profile generated for the request. These
`requests can then be refined using "relevance feedback",
`where the user actively or passively rates the articles 5
`retrieved as to how close the information contained therein
`is to what is desired. The information retrieval computer
`then uses this relevance feedback information to refine the
`request profile and the process is repeated until the user
`either finds enough articles or tires of the search.
`A number of researchers have looked at methods for
`selecting articles of most interest to users. An article titled
`"Social Information filtering: algorithms for automating
`'word of mouth"' was published at the CHi-95 Proceedings
`by Patti Maes et al and describes the Ringo information 15
`retrieval system which recommends musical selections. The
`Ringo system requires active feedback from the users(cid:173)
`users must manually specify how much they lil(e or dislike
`each musical selection. 'Ibe Ringo system maintains a
`complete list of users ratings of music selections and makes 20
`recommendations by finding which selections were liked by
`multiple people. However, the Ringo system does not take
`advantage of any available descriptions of the music, such as
`structured descriptions in a data base, or free text, such as
`that contained in music reviews. An article titled "Evolving 25
`agents for personalized information filtering", published at
`the Proc. 9th IEEE Conf. on AI for Applications by Sheth
`and Maes, described the use of agents for information
`filtering which use genetic algorithms to learn to categorize
`Usenet news articles. In this system, users must define news
`categories and the users actively indicate their opinion of the
`selected articles. Their system uses a list of keywords to
`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 35
`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"
`at the 15 Ann. Int'l SIGIR '92,ACM 318-329 (Cutting et al. 40
`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
`these clusters based on short summaries of the cluster. The 45
`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(cid:173)
`ing those words which appear most frequently in the cluster 50
`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
`key words for indexing by picking the words with the largest
`
`SOLUTION
`
`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 that target object's attributes is termed a "target
`profile", (c.) the user looking for the target object is termed
`a "user", (d.) a profile holding that user's attributes, includ-
`55 ing age/zip code/etc. is termed a "user profile", (e.) a
`summary of digital profiles of target objects that a user li](es
`and/or disli](es, is termed the "target profile interest sum(cid:173)
`mary" of that user, (f.) a protlle consisting of a collection of
`attributes, such that a user likes target objects whose profiles
`60 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 protlles is termed the
`"search protlle set" of a user, (h.) a collection of target
`65 objects with similar profiles, is termed a "cluster," (i.) an
`aggregate profile formed by averaging the attributes of all tar
`get objects in a cluster, termed a "cluster profile," G .) a real
`
`TT0007191
`
`

`

`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 18 of 44
`
`6,029,195
`
`5
`number determined by calculating the statistical variance of
`the profiles of all target objects in a cluster, is termed a
`"cluster variance," (k.) a real number determined by calcu(cid:173)
`lating the maximum distance between the profiles of 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 10
`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- 15
`erate 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 available on the electronic media. 20
`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 25
`target objects whose profiles most closely match the user's
`interests as described by the user's target profile interest
`summary. Users' target protlle 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 30
`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 35
`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 40
`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 45
`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 50
`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 55
`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, 60
`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 65
`standard kinds of demographic analysis and market research
`on the resulting database of partial user profiles. Pseudony-
`
`6
`mous control of an information server suggests how a
`special discount can be issued to a user's pseudonym and
`that such a digital credential is provided to the user as a
`result of his/her user profile making him/her eligible. The
`5 user may thus present this type of credential to the appro(cid:173)
`priate vendor to take advantage of the discount. This tech(cid:173)
`nique can be extended also to smart cards wherein the digital
`credential providing the discount is downloaded from the
`client to the smart card and upon presentation, the vendor
`may if desired, delete the credential upon redemption by the
`user. These discount credentials may similarly include any
`of the discount types (customized promotions) herein dis(cid:173)
`closed wherein each purchase may identified ( characterized)
`and credentialized by the vendor onto the user's smart card
`and/or the vendor' s system.
`In the preferred embodiment of the invention, the system
`for customized electronic identification of desirable objects
`uses a fundamental methodology for accurately and effi-
`ciently matching users and target objects by automatically
`calculating, using and updating profile information that
`describes both the users' interests and the target objects'
`characteristics. The target objects may be published articles,
`purchasable items, or even other people, and their properties
`are stored, and/or represented and/or denoted on the elec(cid:173)
`tronic media as (digital) data. Examples of target objects can
`include, but are not limited to: a newspaper story of potential
`interest, a movie to watch, an item to buy, e-mail to receive,
`or another person to correspond with. In one suggested
`application, the user is a sender of email (which may have
`originated from the user for or from another external source
`such as from outside of a large organization) and the target
`objects are users who might be considered most appropriate
`based upon previous messages which they have received,
`read and responded to. Accordingly, like other target objects,
`users (or user pseudonyms) in accordance with their user
`profiles (or portions of which they have disclosed) may be
`organized and browsed within an automatically generated
`menu tree, which is below described in detail. In all these
`cases, the information delivery process in the preferred
`embodiment is based on determining the similarity between
`a profile for the target object and the profiles of target objects
`for which the user (or a similar user) has provided positive
`feedback in the past. The individual data that describe a
`target object and constitute the target object's profile are
`herein termed "attributes" of the target object. Attributes
`may include, but are not limited to, the following: (1) long
`pieces of text (a newspaper story, a movie review, a product
`description or an advertisement), (2) short pieces of text
`(name of a movie's director, name of town from which an
`advertisement was placed, name of the language in which an
`article was written), (3) numeric measurements (price of a
`product, rating given to a movie, reading level of a book), ( 4)
`associations with other types of objects (list of actors in a
`movie, list of persons who have read a document). Any of
`these attributes, but especially the numeric ones, may cor(cid:173)
`relate with the quality of the target object, such as measures
`of its popularity (how often it is accessed) or of user
`satisfaction (number of complaints received).
`The preferred embodiment of the system for customized
`electronic identification of desirable objects operates in an
`electronic media environment for accessing these target
`objects, which may be news, electronic mail, other pub-
`lished documents, or product descriptions. The system in its
`broadest construction comprises three conceptual modules,
`which may be separate entities distributed across many
`implementing systems, or combined into a lesser subset of
`physical entities. The specific embodiment of this system
`
`TT0007192
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`

`

`Case 6:20-cv-00810-ADA Document 73-16 Filed 04/23/21 Page 19 of 44
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`6,029,195
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`7
`disclosed herein illustrates the use of a first module which
`automatically constructs a "target profile" for each target
`object in the electronic media based on various descriptive
`attributes of the target object. A second module uses interest
`feedback from users to construct a "target profile interest
`summary" for each user, for example in the form of a "search
`profile set" consisting of a plurality of search profiles, each
`of which corresponds to a single topic of high interest for the
`user. The system further includes a profile processing mod(cid:173)
`ule which estimates each user' s interest in various target
`objects by reference to the users' target profile interest
`summaries, for example by comparing the target profiles of
`these target objects against the search profiles in users'
`search profile sets, and generates for each user a customized
`rank-ordered listing of target objects most likely to be of
`interest to that user. Each user's target profile interest
`summary is automatically updated on a continuing basis to
`reflect the user's changing interests.
`Target objects may be of various sorts, and it is sometimes
`advantageous to use a single system that delivers and/or
`clusters target objects of several distinct sorts at once, in a
`unified framework. For example, users who exhibit a strong
`interest in certain novels mav also show an interest in certain
`movies, presumably of a si~ilar nature. A system in which
`some target objects are novels and other target objects are 25
`movies can discover such a correlation and exploit it in order
`to group particular novels with particular movies, e.g., for
`clustering purposes, or to recommend the movies to a user
`who has demonstrated interest in the novels. Similarly, if
`users who exhibit an interest in certain World Wide Web 30
`sites also exhibit an interest in certain products, the system
`can match the products with the sites and thereby recom(cid:173)
`mend to the marketers of those products that they place
`advertisements at those sites, e.g., in the form of hypertext
`links to their own sites. The presently described system
`explains the techniques for target advertising ( on a user by
`user basis) through both links from advertisements on a web
`page which tends to be visited by the most likely buyers of
`that particular product or service, and routing advertise(cid:173)
`ments to such users via email. (This assumes that be cause
`user visitorship is measured at the level of the web page,
`certain pages within the web site may be more appropriate
`for certain advertisements due to the slight differences in its
`visitorship. Text chat( or acoustic voice chat) using a text to
`speech conversion module may be used in conjunction with
`real time profiling of the real time user dialogues occurring
`within that chat session. Advertisements which are relevant
`nature of the content being discussed at present may provide
`temporary links to the appropriate product such that when
`the nature of the content changes the advertisements changes 50
`(may disappear) accordingly.
`The ability to measure the similarity of profiles describing
`target objects and a user's interests can be applied in two
`basic ways: filtering and browsing. Filtering is useful when
`large numbers of target objects are described in the elec- 55
`tronic medias pace. These target objects can for example be
`articles that are received or potentially received by a user,
`who only has time to read a small fraction of them. For
`example, one might potentially receive all items on the AP
`news wire service, all items posted to a number of news 60
`groups, all advertisements in a set of newspapers, or all
`unsolicited electronic mail, but few people have the time or
`inclination to read so many articles. A filtering system in the
`system for customized electronic identification of desirable
`objects automatically selects a set of articles that the user is 65
`likely to wish to read. The accuracy of this filtering system
`improves over time by noting which articles the user reads
`
`8
`and by generating a measurement of the depth to which the
`user reads each article. This information is then used to
`update the user's target profile interest summary. Browsing
`p