`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 1 of 25
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`EXHIBIT F
`EXHIBIT F
`
`
`
`(12) United States Patent
`Partoviet al.
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 8,572,169 B2
`Oct. 29, 2013
`
`US0085721.69B2
`
`(54) SYSTEM, APPARATUS AND METHOD FOR
`DISCOVERY OF MUSIC WITHNA SOCIAL
`NETWORK
`
`(75) Inventors: Hadi Partovi, Seattle, WA (US); Ali
`Y8
`SR in SNathaniel
`cott Brown, Seattle, WA (US);
`Yevgeny Zarakhovsky, Seattle, WA
`(US); Benjamin Botts Osheroff,
`Fairfax, CA (US); Firooz Partovi
`Hillsbo
`gh, CA (US)
`s
`1IISDOrOugh,
`(73) Assignee: Myspace, LLC, Beverly Hills, CA (US)
`(*) Notice:
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 1326 days.
`(21) Appl. No.: 11/511,684
`
`22) Filed:
`(22) File
`(65)
`
`Aug. 28, 2006
`lug. AS,
`Prior Publication Data
`US 2008/OO52371 A1
`Feb. 28, 2008
`(51) Int. Cl
`Go,F i5/16
`(52) U.S. Cl
`709/204709/226
`USPG
`s
`58) Field fo - - - - - ificati- - - - - -s - - - - - - - h- - - - - -
`assification easf2O4, 226: 707/4, 104.1
`(58) t
`See application file for complete search history.
`
`(2006.01)
`
`(56)
`
`References Cited
`U.S. PATENT DOCUMENTS
`
`1/2004 Terry et al.
`6,681,108 B1
`2004 Risk et al. .............. 379,142.06
`6,728,354 B1*
`s299; As
`2.93. R:
`2002/0160338 A1 10/2002 Yirmeyahu
`2003/0078889 A1
`4/2003 Lee et al.
`2004/0044727 A1
`3, 2004 Abdelaziz et al.
`
`106
`Use A's PC
`
`l
`
`S.S. A. '58. Ea TO7 104.1
`Icken et al. ..............
`2005/0038876 A1
`2/2005 Chaudhuri .................... TO9,219
`2005/0055372 A1
`3/2005 Springer, Jr. et al.
`2005/0154764 A1* 7/2005 Riegler et al. .............. TO7 104.1
`2006,0004914 A1
`1/2006 Kelly et al.
`2006/0020662 A1
`1/2006 Robinson ...................... TO9,203
`2006/0143236 A1* 6/2006 Wu ............................ TO7 104.1
`2006, O24794.0 A1 11, 2006 Zhu et al.
`2006/0271460 A1 11, 2006 Hanif
`2006/0282.303 A1 12/2006 Hale et al.
`2007.0011161 A1
`1/2007 Norton et al.
`2007, OO16469 A1
`1/2007 Bae et al.
`2007, O250715 A1 10, 2007 Cai et al.
`2007,0266446 A1 11/2007 Aaron
`2008/0004959 A1
`1/2008 Tunguz-Zawislak et al.
`(Continued)
`OTHER PUBLICATIONS
`Jaccard Index, from Wikipedia, free encyclopedia, pp. 1-3. Retrieved
`the Internet: <URL:http://en.wikipedia.org/wiki/Jaccard in
`ex>.
`
`Primary Examiner — Liangche A Wang
`(74) Attorney, Agent, or Firm — Gates & Cooper LLP
`(57)
`ABSTRACT
`A system, apparatus, and method to assist in the discovery of
`music or other content by members of a social network. The
`present invention permits network members to network
`together and share information about their music collections
`and listening habits with a processing platform and with each
`other. The processing platform processes music library con
`tent and playback related data to produce similarity and com
`patibility measures between artists (that is, an artist-to-artist
`measure) and between network members (that is, a network
`member-to-network member measure). By combining the
`content and playback related data with filtering mechanisms,
`recommendation techniques (which may be based on the
`calculated measures), and set membership concepts, the
`present invention provides network members of the network
`with tools to discover artists and/or songs that are likely to be
`of interest to them.
`
`38 Claims, 8 Drawing Sheets
`
`Social Music
`Discovery Backend
`102
`
`Web - Sewer
`Middle - Tier
`
`Social Music
`Discovery
`databases
`(backend)
`
`Serves Web
`
`pins'
`east W. airies
`browser view to
`all user's music
`browse friends'
`library data.
`music tastes co Ser
`user similarity.
`Stores friend
`Shuttles RPC
`frien
`calls between
`Pied relationships,
`Stores music
`backend
`recommendations
`
`220
`
`222
`
`210
`
`2
`- -
`
`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 2 of 25
`
`202
`
`208
`
`We bowser
`Media player
`Social
`204-
`ib
`Er i
`2O6- city,t Music
`satire
`laylists, playCounts
`Davey
`;
`Media files (MP3, WMA, etc)
`e
`-------------------...---...-...- ...a... a .-----. --> -s. ------ a-- - - - - - - - - - www.---i
`
`P2P
`nnection
`
`
`
`We browser
`Media player
`Client/server
`Social
`law controls, library mgmit
`corrector
`Music
`Music library Database
`Distry ŠišE.
`laylists, playcounts
`Mediates (MP3, WMAetol T J
`
`
`
`And so or for other
`users' PCs...
`
`
`
`US 8,572,169 B2
`Page 2
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2008, 0214148 A1
`2008, 0215348 A1
`
`9, 2008 Ramer et al.
`9, 2008 Guildimann et al.
`
`2008/0243593 A1 10, 2008 KO et al.
`2009/0070.184 A1
`3/2009 Svendsen ........................ 70.5/10
`2009,00831.17 A1* 3, 2009 Svendsen et al.
`70.5/10
`2009, O112701 A1
`4/2009 Turpin et al.
`* cited by examiner
`
`
`
`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 3 of 25
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`U.S. Patent
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`US 8,572,169 B2
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 4 of 25
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`U.S. Patent
`
`Oct. 29, 2013
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`Sheet 2 of 8
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`US 8,572,169 B2
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 6 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`Sheet 3 of 8
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`US 8,572,169 B2
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 7 of 25
`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 7 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 8 of 25
`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 8 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 9 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 10 of 25
`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 10 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 11 of 25
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`U.S. Patent
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`Oct. 29, 2013
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`US 8,572,169 B2
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`US 8,572,169 B2
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`1.
`SYSTEM, APPARATUS AND METHOD FOR
`DISCOVERY OF MUSIC WITHNA SOCIAL
`NETWORK
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`BACKGROUND OF THE INVENTION
`
`The present invention is directed to systems, apparatus and
`methods for the discovery of content within a social network,
`and more specifically, to a system to enable members of a
`Social network to discover music of interest contained in the
`music libraries of network members. The system utilizes an
`automated process to calculate similarity and/or compatibil
`ity measures based on members music listening habits and
`music libraries, and combines the measures with filtering and
`other techniques to produce a set of music discovery tools for
`network members.
`The advent and popularity of portable music players, for
`example, MP3 players, has provided users with the ability to
`access a large number of Songs in a convenient manner. The
`portability and relatively large storage capacity of Such
`devices has made it possible for users to have their entire
`music collection on a single, transportable device. At the
`same time it has created great interest in developing ways for
`users to learn of (i.e., discover) music that might be of interest
`to them so that they can add the music to their collection.
`Traditionally, the primary ways for a user to discover music
`have been via (1) the radio, and (2) word of mouth (i.e., via
`friends). However, in addition to such methods, there has
`been recent interest in developing other ways for users to
`learn of music that might be of interest to them.
`In an effort to assist users to discover music that might be
`of interest to them, several approaches have been pursued.
`These approaches generally rely on a user publishing a list of
`their music interests or current music being enjoyed (e.g., a
`playlist) and then enabling friends or other invitees to access
`the list. One example of such a playlist publishing model
`involves allowing a user to publish a playlist to a web site, and
`then enabling the list to be accessed by specific friends or
`acquaintances. Those accessing the list may review the play
`list and in some cases, be provided with a link to enable
`download of selected songs from the list. Apple Computer'TM,
`the creator of iTunesTM, provides a feature named iMix which
`permits a user to publish a playlist from an iTunesTM media
`library. The list can be sent to a friend, who may then browse
`the playlist and purchase & download individual songs from
`the list or purchase the entire playlist. Other similar offerings
`include those of WebJayTM (recently acquired by YahooTM)
`which provides a website for sharing playlists, and Music
`mobs (www.musicmobs.com; which assists a user to upload
`a playlist from iTunesTM to the Musicmobs website). How
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`ever, all of these approaches require that a user actively pub
`lish a playlist to a web site or web-service, and then optionally
`invite one or more friends via a message to access the pub
`lished playlist. When the friend accesses the published play
`list, the friend must then decide whether or not they like the
`music, and if so, decide whether to purchase one or more of
`the Songs.
`Another approach to assisting users to discover music that
`might be of interest is allowing a user to publish their personal
`musical tastes to a location accessible by others. The leading
`companies in this space are believed to include Last.FMTM
`and Musicmobs. Both companies are believed to provide a
`user with client-side software that automatically examines
`the user's media library, tracks what music is being played,
`and uploads that data to a website. It is understood that the
`uploaded data is processed to report certain of the user's
`music listening habits (e.g., most-listened-to-artists or most
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`Case 4:20-cv-07572-JSW Document 70-6 Filed 11/11/21 Page 12 of 25
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`listened-to-songs) for others to access. Such services may
`also allow a user to publish their music listening habits (as
`determined by these web services) as an embedded resource
`on another website (such as a web page on a social network,
`e.g., MySpaceTM).
`Although the above described approaches to music discov
`ery do permita user to share their music interests and listening
`habits with others, they do not provide a complete solution to
`the problem. The above approaches generally lack sufficient
`automation or other data processing assistance that can add
`value by assisting those viewing the data to determine which
`music would be of greatest interest. This is because the
`present approaches operate so that once a playlist or Summary
`of listening habits is published, those accessing the informa
`tion have to decide for themselves (with no further informa
`tion) whether the listed Songs are of interest. Since users may
`have a music collection numbering in the thousands of songs,
`without more information, it is very difficult for someone to
`select which, if any, of a friends music is of sufficient interest
`to warrant listening to or downloading. In this sense, present
`methods lack a way to assess the likelihood that music of
`interest to one user will be of interest to another. Further,
`present methods lackaway to quantify this likelihood, should
`that be of interest to a user.
`What is desired is a system, apparatus and method for
`enabling the efficient discovery of and access to music con
`tent, where Such system, apparatus, and method overcomes
`the noted disadvantages of present approaches.
`
`BRIEF SUMMARY OF THE INVENTION
`
`The present invention is directed to a system, apparatus,
`and method to assist in the discovery of music or other content
`by members of a social network. The present invention per
`mits users to network together and share information about
`their music collections and listening habits with a processing
`platform and with each other. The information includes data
`related to the content of the network members music libraries
`(such as the names of songs and artists contained in a network
`member's library) as well as data related to the use of that
`content (such as a play count for each Song, and the date, time,
`and frequency with which a song or artist is played by a
`network member). The processing platform provides the abil
`ity to process the music library content and playback related
`data to produce similarity and/or compatibility measures
`between artists (that is, an artist-to-artist measure) and
`between network members (that is, a member-to-member
`measure). By combining the content and playback related
`data with filtering mechanisms, recommendation techniques
`(which may be based on the calculated measures), and set
`membership concepts, the present invention provides mem
`bers of the network with tools to assist in the discovery of
`music. In one embodiment, based on the results of the data
`processing, the present invention is capable of generating
`recommendations of artists and/or songs to members of the
`network. In this way, the present invention assists members of
`the Social music network to discover music content that is
`more likely to be of interest to them.
`In addition to generating recommendations, the present
`invention is capable of generating other outputs that facilitate
`the discovery of music (and other content), where that music
`is expected to be of interest to a member of the network.
`Along with the recommendations, these outputs may broadly
`be termed “music discovery tools'. Specifically, one feature
`of the invention is that a network member can access (or in
`terms of the user interface, “see’ information regarding) the
`music library and music listening data of another network
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`US 8,572,169 B2
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`member, a friend who is a network member (or has been
`invited to join the network), or a group of members of the
`network. Various inventive filtering mechanisms may be
`applied to this data to produce results that are contextually
`relevant to the network member viewing the data. Examples
`of such filtering mechanisms include showing music not cur
`rently owned by the network member but contained in the
`music library of a relevant person or group, or showing the
`Songs played most by a person or group (i.e., the most popular
`songs) but not currently owned by the network member. Such
`filtering mechanisms may be applied independently of each
`other or sequentially (e.g., what Songs are played most by the
`group, followed by determining which of those a network
`member does not presently own). This allows a network
`member to filter and/or order the data to provide contextually
`relevant results that permit the network member to discover
`music content that is expected to be of interest to them.
`In one embodiment, the present invention is directed to a
`Social music network that includes a plurality of clientagents,
`where each client agent is associated with a respective net
`work member and configured to communicate and exchange
`data with a processing platform. Each client agent is further
`configured to provide one or more types of data regarding the
`respective network members music library to the processing
`platform where the data is processed to produce an output
`representing a music discovery tool that may be used by a
`member of the network to discover music of interest to that
`member.
`In another embodiment, the present invention is directed to
`a Social music network server that includes a communications
`interface configured to receive data from a plurality of social
`music clients, where each social music client is associated
`with a respective network member and configured to provide
`data regarding the respective network members music
`library to the server. The server further includes a processor to
`process the received data and generate an output representing
`a music discovery tool that may be used by a member of the
`network to discover music of interest to that member.
`In yet another embodiment, the present invention is
`directed to a method of generating a music discovery tool for
`a member of a social network that includes determining data
`regarding the music library contents or music library contents
`usage of a plurality of members of the Social network and
`processing the data to generate an output representing a music
`discovery tool that may be used by a member of the network
`to discover music of interest to that member.
`Other objects and advantages of the present invention will
`be apparent to one of ordinary skill in the art upon review of
`the detailed description of the present invention and the
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`included figures.
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`FIG. 5 is a flowchart showing an example implementation
`of a process to invite a friend to be granted access to a network
`member's Social music network, in accordance with the
`present invention;
`FIG. 6 is a flowchart showing an example of the primary
`steps in the generation and display of a compatibility measure
`between two members of the social music discovery network
`of the present invention;
`FIG. 7 is a flowchart showing an example of the primary
`steps in the generation and display of a first recommendation
`feature (or music discovery tool) between two members of the
`Social music discovery network of the present invention; and
`FIG. 8 is a flowchart showing an example of the primary
`steps in the generation and display of a second recommenda
`tion feature (or music discovery tool) between two members
`of the social music discovery network of the present inven
`tion.
`
`DETAILED DESCRIPTION OF THE INVENTION
`
`The present invention is directed to a system, apparatus,
`and method for the efficient discovery of music between
`members of a social music network. The inventive system
`permits members of a Social music network to discover music
`contained in other members music collections that may be of
`interest, with that interest Suggested by processing of content
`and playback related data, calculated compatibility and simi
`larity measures, and data filtering techniques. The system
`further includes the ability to generate artist and Song recom
`mendations to members of the network based on a variety of
`indicia of potential interest. In addition to generating recom
`mendations, the present invention is capable of producing
`other types of music discovery tools that facilitate the discov
`ery of music (and other content) expected to be of interest to
`a member of the network. Such tools may be obtained by
`application of various filtering mechanisms to the music
`library and music listening data of another network member,
`a friend who is a member of the network (or has been invited
`to become one), or a group of network members. An example
`of Such a filtering mechanism is to show a network member
`music owned by or actively listened to by another network
`member, friend or group but that is not currently owned by the
`network member. Another example of such a filtering mecha
`nism is to show the Songs owned by or played most by a
`person or group. Further, note that such filtering mechanisms
`may be applied independently of each other or sequentially
`(e.g., what Songs are played most by the group, followed by
`determining which of those the network member does not
`presently own).
`The inventive system provides an infrastructure and rel
`evant processes to enable multiple users to form a social
`network, with that network designed to permit the tracking
`and exchange of data concerning members musical interests
`and tastes. The data is obtained using social music discovery
`client Software installed on members computing and/or
`music playback devices. The client software tracks a network
`members music interests and tastes as evidenced by the
`artists and Songs contained in a network members music
`library, and by the music playback activities of the network
`member. The data monitored or collected by the client soft
`ware is processed by a processing platform of the system to
`produce, among other results, a measure of the similarity or
`compatibility of the musical tastes of a first network member
`and a second network member, or between a first artist and a
`second artist. The system further utilizes information about
`the content of a network members musical library and play
`back activities, combined (if desired) with the similarity/
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`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a functional block diagram of the primary com
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`ponents of a system for enabling efficient discovery of music
`content within a Social music network, in accordance with the
`present invention;
`FIG. 2 is a more detailed version of the block diagram of
`FIG. 1, showing the primary functional elements of the func
`tional blocks of that figure;
`FIG. 3 is a detailed functional block diagram of the social
`music discovery backend element shown in FIGS. 1 and 2:
`FIG. 4 is a flowchart showing an example of the primary
`steps in the account registration and client-installation pro
`cesses of the Social music discovery network of the present
`invention;
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`intersection of two sets), a measure of the lack of overlap of
`the data sets, or a measure of the similarity of the artists
`contained in the two network member's musical libraries.
`Using the music library contents and usage data, and if
`desired, the generated similarity and compatibility measures,
`appropriate filtering mechanisms and set membership con
`cepts, the present invention generates music discovery tools
`for a member of the network. These tools provide insight into
`the music contained in one network member's library (or a
`group of members’ libraries) that is expected to of greater
`than nominal interest to a second network member (or group
`of network members). After identifying such music (either in
`the form of a song oran artist), the inventive system is capable
`of generating a recommendation to the second network mem
`ber (or group of members), where the recommendation may
`include a sample of the song or artist. The inventive system
`may also produce other outputs that assist a network member
`(or group of members) to discover music that is expected to be
`of interest to them. Such outputs include a display of music
`not currently in the network members music library (or
`group's libraries) that satisfies one or more criteria. These
`criteria may include music popular with one or more network
`members that is expected to be of interest (e.g. by virtue of a
`similarity/compatibility measure calculation) or music that a
`network member doesn’t have but that is found in the libraries
`of members of a group, or of a friend or group of friends.
`As noted, the inventive system may also rely on evaluation
`of other Suitably defined metrics or analysis techniques that
`can be used to characterize the data. For example, Such met
`rics or analysis techniques include those utilized in Collabo
`rative Filtering or Collaborative Recommendation methods,
`and a variety of forms of statistical analysis. For example,
`Such methods include similarity measures that are used as the
`basis for generating recommendations. Such similarity mea
`Sures are used to quantify the relative similarity (or closeness)
`of two data sets, typically by expressing a characteristic of
`Such data sets as an n-dimensional vector or n-by-m dimen
`sional matrix. Examples of such similarity measures include
`those termed the Cosine-based Similarity, Correlation-based
`Similarity, and Adjusted Cosine Similarity methods.
`The result of evaluating the measure, metric or technique
`that is utilized will typically be a characterization of the music
`library and/or musical taste of the network member (or group)
`whose data is processed, or a characterization of a relation
`ship between the music library and/or musical taste of a pair
`of network members (such as the correlation, etc.). When
`provided a measure or metric for a first network member and
`a second network member, an indicator of the similarity of the
`two network member's musical libraries or of the compatibil
`ity of the two network member's musical tastes can be
`inferred. This indicator may be the result of comparing the
`two measures or metrics, processing the two measures or
`metrics in accordance with a heuristic to determine the
`"closeness’ of the two measures, requiring a certain mini
`mum measure of similarity or compatibility, etc.
`Further, a value of the characterizing parameter may be
`thresholded, scaled, or otherwise processed to determine its
`relevance and assess the similarity or compatibility of the
`network members musical tastes. Regardless of how
`obtained, the result may be taken as an indicia of the likeli
`hood that the musical tastes or interests of a first network
`member will be of interest to a second network member. If
`these indicia are of sufficient value, then the inventive system
`may be used to generate recommendations (either automati
`cally or via user action) to the second network member of an
`artist or song contained in the music library of the first net
`work member. As noted, the system may also generate other
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`compatibility measures, set membership concepts and/or col
`laborative filtering techniques to provide network members
`with insights into music that they do not currently have access
`to, but that may be of interest to them. It is further expected
`that the network member's level of interest will be more than
`nominal by virtue of the results of the data processing and
`other analysis applied to the library, playback, and similarity/
`compatibility measure dat