Trials@uspto.gov
`571-272-7822
`
`Paper No. 11
`Entered: December 21, 2015
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`GOOGLEINC., NEST LABS, INC., and
`DROPCAM,INC.,
`Petitioners,
`
`Vv.
`
`e.DIGITAL CORPORATION,
`Patent Owner.
`
`Case IPR2015-01473
`Patent 8,311,523 Bl
`
`Before KEVIN F. TURNER, HYUN J. JUNG, and BARBARA A. PARVIS,
`Administrative Patent Judges.
`
`JUNG, Administrative Patent Judge.
`
`DECISION
`Institution of Inter Partes Review
`37 C.F-R. § 42.108
`
`
`571-272-7822
`
`Paper No. 11
`Entered: December 21, 2015
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`GOOGLEINC., NEST LABS, INC., and
`DROPCAM,INC.,
`Petitioners,
`
`Vv.
`
`e.DIGITAL CORPORATION,
`Patent Owner.
`
`Case IPR2015-01473
`Patent 8,311,523 Bl
`
`Before KEVIN F. TURNER, HYUN J. JUNG, and BARBARA A. PARVIS,
`Administrative Patent Judges.
`
`JUNG, Administrative Patent Judge.
`
`DECISION
`Institution of Inter Partes Review
`37 C.F-R. § 42.108
`
`
`
`IPR2015-01473
`Patent 8,311,523 Bl
`
`.
`
`INTRODUCTION
`
`Google Inc., Nest Labs, Inc., and Dropcam,Inc. (“Petitioners”) filed a
`
`Petition (Paper2, “Pet.”), requesting institution of an inter partes review of
`
`claims 1, 3, 4, 6, 8-10, 19, 21, 23, 25, and 26 (“the challenged claims”) of
`
`U.S. Patent No. 8,311,523 B1 (Ex. 1001, “the ’523 patent”). e.Digital
`
`Corporation (“Patent Owner’) timely filed a Preliminary Response (Paper8,
`“Prelim. Resp.”). We have jurisdiction under 35 U.S.C. § 314, which
`
`providesthat an inter partes review may notbe instituted “unless .
`
`.
`
`. there is
`
`a reasonable likelihood that the petitioner would prevail with respect to at
`
`least 1 of the claims challengedin the petition.”
`
`Uponconsideration of the Petition and Preliminary Response, and for
`
`the reasons explained below, we determine that Petitioners have shownthat
`there is a reasonablelikelihood that they would prevail with respectto at
`
`least one of the challenged claims, and weinstitute an inter partes review of
`
`claims 1, 3, 4, 6, 8-10, 19, 21, 23, 25, and 26 of the ’523 patent.
`
`A. Related Proceedings
`Theparties indicate that the 523 patent is involved in e. Digital Corp.
`
`v. Dropcam, Inc., Case No. 3:14-cv-04922-JST (N.D. Cal.), e. Digital Corp.
`
`v. Dropcam, Inc., Case No. 3:14-cv-01579 (S.D. Cal.), e. Digital Corp. v.
`
`ShenZhen Gospell Smarthome Electronic Co., Ltd., Case No. 3:15-cv-
`
`- 00691-JST (N.D.Cal.), and e. Digital Corp. v. ArcSoft, Inc., Case No. 3:15-
`cv-00056-BEN-DHB (S.D. Cal.). Pet. 58; Paper 6, 2 (labeled “Paper No.
`4”),
`
`
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`IPR2015-01473
`Patent 8,311,523 Bl
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`B. The ’523 Patent (Ex. 1001)
`
`T
`
`lates to “the classification of a person’s current
`
`actions such that selected callers can automatically or manually gauge the
`
`intrusiveness of a communication request.” Ex. 1001, 1:17—20. Figure 1 of
`
`the °524 patent is reproduced below.
`
`FIG. 1
`
`—
`
`io
`
`Electronic Device
`
`Location
`Sensor
`
`Inertial
`Sensor
`
`Optical
`Sensor
`
`Acoustic
`Sensor
`
`Location
`Processor
`
`Optical
`Piocessni
`
`SLOUSIC
`Processor
`
`
`
`Transceiver
`
`Calculating
`Logic
`
`Training
`
`170
`
`Figure 1 is a block diagram of an electronic device. Jd. at 8:66-67.
`Mobile device 100 includes location sensor110,inertial sensor 120, optical
`
`sensor 130, and acoustic sensor 140.
`
`/d. at 9:22—27, 35-46, 11:22—26.
`
`Mobile device 100 monitors location, acceleration, orientation, audio, and
`
`optical samples using sensors 110, 120, 130, 140. Jd. at 9:19-21. For
`
`example, acoustic sensor 140 can generate acoustic measurementdata, and
`
`optical sensor 130 can generate simple light level measurement data. Jd. at
`
`11:52-54, 62-64,
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`
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`Location sensor 110 is coupled to location processor 115; inertial
`
`sensor 126 is coupled to motion processor i125; acoustic sensor 140is
`
`coupled to acoustic processor 145; and optical sensor 130 is coupled to
`
`optical processor 135. /d. at 13:33-34, 42-43, 14:3-5, 9-10. Calculating
`
`logic 150 receives data from processors 115, 125, 135, 145 and uses the data
`
`to classify a user’s activity from predefined, identifiable user activities.
`
`/d.
`
`at 14:31-35, 55-58. Calculating logic 150 can identify the user’s social
`
`activity “by monitoring for different social signatures, and applies a
`
`corresponding social template to determine howto treat an incoming
`
`communication request.” Jd. at 14:59-62. Thesocial signature can be a
`
`combination of sensors, with each sensor detecting a certain value range. Id.
`at 15:56-66 (Table 1). “Each social signature is indicative of a different
`
`type of activity.” Jd. at 15:38-39. When enough events indicative of a
`
`particular user social activity are detected, calculating logic 150 identifies
`
`the activity as being performed bythe user.
`
`/d. at 14:63-66.
`
`Calculating logic 150 compares the processed data from sensors 110,
`
`120, 130, 140 with social templates 165 stored in memory 160.
`
`/d. at 14:31-
`
`37. From the identified social signature, calculating logic 150 selects a
`
`social template that determines how much informationis provided to others.
`
`Id. at 15:45-49. For example, the social template may allow specific friends
`
`to knowthat the user is drinking coffee, may allow co-workers to know that
`
`the user is in a personal meeting, and mayallow others to know that the user
`
`is busy and not to be disturbed.
`
`/d. at 15:24-28.
`
`Calculating logic 150 can provide information to a requesting caller
`according to a hierarchical socialclassification. Jd. at 14:39-41. “Examples
`
`of hierarchical social classification that can be identified include high level
`
`
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`IPR2015-01473
`Patent 8,311,523 B1
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`available, busy, do not disturb,” and each classification can have further,
`
`}m
`
`ore accurate classifications that can be made available to moreselect social
`
`groups. Jd. at 14:44-48. “Eachset of hierarchical social classificationsis
`
`stored in a separate social template.” Jd. at 14:53-54.
`
`To set up a social template, social training program 167 in memory
`
`160 is activated, and a social signature is associated with a particular social
`template.
`/d. at 17:31-35. Specifically, data sensed by sensors 110, 120,
`
`130, 140 are correlated with a new social template, and the user enters how
`
`muchinformation is to be provided to different categories of potential
`
`callers. Id. at 17:35-41.
`
`Another embodimentis shownin Figure 2 of the ’523 patent, whichis
`
`reproduced below.
`
`/d. at 18:1—5.
`
`FIG. 2
`
`Location
`Sensor,
`and
`Processor
`
`Motion
`Sensor,
`and
`Processer
`
`Optical
`Sensor,
`and
`Processor
`
`Acoustic
`Sensor,
`and
`Processor
`
`Remote
`alculating
`Logic
`
`
`
`:
`
`280
`
`Calculating
`gic
`
`250
`
`260
`
`275
`
`Figure 2 is a block diagram of a social monitoring system.
`
`/d. at 9:1—
`
`3. While mobile device 100 can have the social templates and perform
`
`social training, in the embodimentof Figure 2, the social templates are
`
`stored externally and social training is performed externally. Id. Mobile
`
`
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`device 200 monitors location sensor and processor 210, motion sensor and
`
`processor 220, optical sensor and processor 230, and acoustic sensor and
`
`processor 240. Jd. at 18:5—10. Sensors and processors 210, 220, 230, 240
`
`perform generally the sameas sensors 110, 120, 130, 140 and processors
`
`115, 125, 135, 145. Jd. at 18. Id. at 18:10-20. Calculating logic 250
`
`receives data from sensors and processors 210, 220, 230, 240 and transmits
`
`the data to server 270 via network 260.
`/d. at 18:20-25. The data received
`at server 270 are compared with social templates stored in memorythatis
`included in remote calculating logic 275 of server 270. Jd. at 18:29-31. The
`
`assignmentandtraining of social signatures according to social signatures is
`
`performed externally at server 270, instead of within mobile device 200. Jd.
`
`at 18:34—37.
`
`C. Illustrative Claim
`
`The ’523 patent has 29 claims. Of the challenged claims, claims 1
`
`and 19 are independent; claims 3, 4, 6, and 8—10 depend from claim 1; and
`
`claims 21, 23, 25, and 26 depend from claim 19. Claim 1 is reproduced
`
`below:
`
`A server in communication with a communication
`1.
`device via a network and which automatically provides
`differing levels of information according to a predetermined
`social hierarchy, the server comprising:
`templates, each social
`a memory which stores social
`template corresponding to a unique social signature comprising
`a first sensor value range and a second sensorvalue range other
`than the first sensor value range and each social template being
`selectable to provide, for each level of the predetermined social
`hierarchy, a corresponding differing amount of information to
`each memberofthe predetermined social hierarchy;
`a processor which receives from the communication
`sensor data received from a
`sensor
`set of the
`
`device
`
`
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`communication device which detects sensor data including a
`first detected sensor value comprising optical
`information
`detected by an optical sensor of the sensor set which detects an
`amountoflight of an environment of the communication device
`and a second detected sensor value comprising acoustic
`information detected by an acoustic sensor of the sensor set
`which detects a sound level of the environment of the
`communication device, creates a detected social signature from
`the received sensor data, determines which of the social
`signatures of the social templates has a greatest correspondence
`with the created social signature through comparisonof thefirst
`and second detected sensor values and the first and second
`sensor value ranges of each stored social template, retrieves
`from the memory the determined one social template having the
`greatest correspondence and having the detected amount of
`light within the first sensor value range and the detected sound
`level within the second sensor value range, and providesto at
`least one memberof the predetermined social hierarchy only as
`much information as allowed based on the retrieved social
`template; and
`a transceiver which receives the sensor data from the
`sensor set in the communication device, and provides under the
`control of the processor to at least one of the members of the
`predetermined social hierarchy only as much information as,
`allowed based on the retrieved social template.
`
`Claim 19 recites a “method of automatically providing differing levels
`
`of information according to a predetermined social hierarchy within a
`
`server.”
`
`D. Challenges
`Petitioners challenge, under 35 U.S.C. § 103:
`(1) claims 1, 3, 4, 8-10, 19, 21, 23, 25, and 26 as unpatentable over
`
`U.S. Patent Application Publication No. 2009/0300525 A1 to Jolliff,
`
`published Dec. 3, 2009 (Ex. 1005, “Joiliff’) and U.S. Patent Application
`
`
`
`IPR2015-01473
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`Publication No. 2009/0094179 A1 to Jager, published Apr. 9, 2009 (Ex.
`
`(2) claim 6 as unpatentable overJolliff, Jager, and Bo Luo and
`
`DongwonLee, “On Protecting Private Information in Social Networks: A
`
`Proposal,” 2009 IEEE 25th International Conference on Data Engineering,
`
`published Apr. 3, 2009 (Ex. 1017, “Luo”);
`
`(3) claims 1, 3, 4, 8-10, 19, 21, 23, 25, and 26 as unpatentable over
`
`International Publication No. WO 2009/043020 A2 to Miluzzo, published
`
`Apr. 2, 2009 (Ex. 1007, “Miluzzo”) and U.S. Patent Application Publication
`No. 2006/0004680 to Robarts, published Jan. 5, 2006 (Ex. 1008, “Robarts”);
`and
`.
`
`|
`
`(4) claim 6 as unpatentable over Miluzzo, Robarts, and Luo. Pet. 3-4,
`
`. 10-57.
`
`Il.
`
`ANALYSIS
`
`A. Claim Construction
`
`.
`
`In an inter partes review, claim terms in an unexpired patent are
`
`interpreted according to their broadest reasonable construction in light of the
`
`specification of the patent in which they appear. 37 C.F.R. § 42.100(b);
`
`Office Patent Trial Practice Guide, 77 Fed. Reg. 48,756, 48,766; Jn re
`
`Cuozzo Speed Techs., LLC, 793 F.3d 1268, 1275—79 (Fed. Cir. 2015). Only
`those terms in controversy need to be construed, and only to the extent
`necessary to resolve the controversy. Vivid Techs., Inc. v. Am. Sci. & Eng’g,
`
`Inc., 200 F.3d 795, 803 (Fed. Cir. 1999).
`
`
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`Petitioners propose interpreting “social template,”
`
`99 66
`
`“social hierarchy,”
`
`and “level of [aj social hierarchy.” Pet. 6-10. Patent Owner respondsthat
`claim constructionis “irrelevantatthis stage.” Prelim. Resp.7.
`For the purposesof this Decision, we determine an express
`
`construction of any term is not necessary.
`
`B. Obviousness of Claims 1, 3, 4, 8-10, 19, 21, 23, 25, and 26 over
`
`Miluzzo and Robarts
`.
`Petitioners contend that claims 1, 3, 4, 8-10, 19, 21, 23, 25, and 26 are
`rendered obvious by Miluzzo and Robarts with citations to the disclosures in
`these references and a Declaration of David Hilliard Williams (Ex. 1003,
`
`“the Williams Declaration”). Pet. 33-55.
`1. Miluzzo (Ex. 1007)
`
`Miluzzo teaches a “method for injecting sensed presenceinto social
`
`networking applications.” Ex. 1007, Abstract. In particular, sensor data
`
`associated with a user are received by a computer, the computer analyzes the
`
`sensor data to infer a presence status of the user, and then the presencestatus
`
`stores the data in a database and sendsthe presencestatus to a social
`
`‘networking server to update social networking applications based upon the
`
`user’s preferences. Jd. Figure 1 is reproduced below.
`
`
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`1/4
`
`©
`
`USER
`WW
`
`:
`
`118
`
`118
`
`SOCIAL NETWORKING
`
`SERVER 126
`
`| PRESENCE 122 |
`NETWORK 120
`L
`
`“A
`J
`
`PRESENCE SERVER
`116
`
`PRESENCE122
`
`PRESENCE 124
`
`
`
`
`
`FIG. 1
`
`Figure 1 illustrates a system for injecting sensed presenceinto social
`
`networking applications.
`
`/d. | 8. As shown in Figure 1, user 102 has cell
`
`phone 106, personal digital assistance (PDA) 108, and embeddedsensorunit
`
`10
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`
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`IPR2015-01473
`Patent 8,311,523 B1.
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`110. Jd. 413. Cell phone 106 includes a camera for sensing images around
`
`user 102 aiid a giobal positioning sensor (GPS) unit for determining the
`
`location of user 102. Jd. PDA 108 includes a temperature sensor for sensing
`
`the temperature near user 102. Jd. Embedded sensors 110 include one or
`more accelerometers for determining activity of user 102. Id. Embedded
`
`sensor unit 110 periodically provides sensor data to cell phone 106 via
`
`wireless link 111, which maytransmit using Bluetooth technology. Jd. {{
`
`17, 26. Additional sensors within devices carried by user 102 include a
`
`microphone,a light sensor, and a humidity sensor. Jd. {J 16, 81.
`
`Asalso shownin Figure 1, second user 104 has network computer
`112 that includes one or moresensors. Id. q 14. Sensed data are sent from
`notebook computer 112 to presence server 116 via network 120, which may .
`be the Internet or other wired or wireless networks. Jd. Jf 13, 14, 26, 69.
`
`Presence server 116 analyzes sensor data to infer activity of users 102 and
`
`104. Id. f§ 14, 69. The data may be processed by an analysis component
`
`within server 116, such as inference engine 212 which may include human
`activity-inferring algorithms. Jd. J 43, 45. For example, presence server
`
`116 uses sensor data to define presence status 122 of user 102 and presence
`
`status 124 of user 104. Jd. Characteristics of users 102 and 104, such as
`
`presence status 122 and 124, are sent to social network server 126 that
`
`supports one or more social networking applications 119, such as Facebook
`
`or MySpace. Id. JJ 21, 69. The presence information for the associated
`
`social networking applications may be updated with presence status 122,
`
`124. Id.
`
`Certain embodiments of system 100 use the “Virtual Walls model
`
`which provides different levels of disclosure based on context, enabling
`
`11
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`IPR2015-01473
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`access to the complete sensed/inferred data set, a subset of it, or no accessat
`Me @co2
`all»? 7
`au.
`2
`&
`» Yee.
`
`2. Robarts (Ex. 1008)
`
`Robarts teaches techniques for using user context modeling
`
`techniques to identify and provide appropriate computer actions based on
`
`current context. Ex. 1008, Abstract. A body-mounted computer receives
`
`input and forwardsit to characterization system 100. Jd. J 56.
`
`Characterization system 100 receives sensed user information via user
`sensors 126 and sensed environment information via environment sensors
`
`128. Id. Characterization system 100 processes the information to create a
`
`current model of the user context based on multiple attributes. Jd. J{ 56, 63.
`
`Additionally, a themeis identified that matches the current context.
`
`Id. § 157. A theme will match the current contextif specified attributes have
`
`current values that are the same as the possible values for the theme. /d.
`
`Robarts also teachesthat selection of a current theme from multiple
`
`matching themes may be performed in a variety of ways. Id. { 162. For
`
`example, a theme may begiven an associated degree of match and then the
`
`theme with the highest degree of match is selected. Jd.
`
`Robarts further teaches a theme data structure that has privacy,
`security, and permission information. Jd. { 203. Examplesof different
`
`privacy values include private, public, work, family, friends, acquaintances,
`
`people in the immediate vicinity, and everyone in my contactlist. Jd.
`
`3. Independent Claims I and 19
`
`Petitioners argue that Miluzzo teachesthe limitations of claim 1,
`
`except it does not describe explicitly how its inferences are derived and how
`
`it stores, organizes, and retrieves a user’s status and privacy settings. Pet.
`
`12
`
`
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`IPR2015-01473
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`33-34 (citing Ex. 1007 Abstract, F952, 53, 81), 36-37 (citing Ex. 1003 {f
`
`7-139; Ex. 1007 Abstract, fj Z1, 43, 45, 53, 69), 39-42 (citing Ex. 1003
`
`{{ 146-150; Ex. 1007 Abstract, J] 52, 53, 56, 69, 81), 45—46 (citing Ex.
`
`1003 {| 163-165; Ex. 1007 ¥ 14, 26, 52, 53, 69).
`
`Petitioners rely on Robarts for teaching themesthat includeattributes,
`
`which can be specified as a range and can be stored in memory,creating a
`current model from collected sensordata, identifying a theme based on
`matchingattributes, and selecting a theme with the highest degree of match.
`
`Pet. 34-35 (citing Ex. 1008 Abstract, [¥ 40, 43, 203), 36 (citing Ex. 1008 §
`
`212), 37-38 (citing Ex. 1008 Abstract, Jf 40, 92, 212, 224, Fig. 8), 39
`
`(citing Ex. 1008
`
`162), 42-45 (citing Ex. 1003 J§ 155-160; Ex. 1008 4 56,
`
`63, 92, 157, 158, 162, 203).
`
`Petitioners assert that a person of ordinary skill would have been
`
`motivated to seek out a reference, such as Robarts, that describes how to
`
`store, organize, and retrieve a user’s privacy settings, which Miluzzo does
`
`not describe explicitly and would have been motivated to seek out
`information for implementing an inference engine. Pet. 34—35 (citing Ex.
`1008 qf 40, 43, 203), 37 (citing Ex. 1003 7 140-144). Petitioners also
`assert that a person of ordinary skill would have understood that, when
`
`adapting the themes of Robarts for use in Miluzzo’s inference engine, the
`
`privacy settings would correspond to Miluzzo’s group memberpolicy or
`
`social hierarchy and matchingattributes would involve ranges. Pet. 39, 43
`
`(citing Ex. 1003 Ff 155-156), 45 (citing Ex. 1003 ¥ 162).
`Petitioners additionally contend that it would have been obvious to
`modify Miluzzo so that its privacy settings can be stored with sensor
`
`attributes in the theme data structures of Robarts, which include privacy
`
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`settings, and that a person of ordinary skill would have been motivated to
`iicorporate Robarts into Miiuzzo “to organize andretrieve the user’s ‘group
`
`membership policies’ because Robarts teachesthat ‘the themes each include
`
`related sets of attributes that reflect the context of the user.’” Jd. at 35
`
`(citing Ex. 1007 ¢ 53; Ex. 1008 ¥ 40, 203). Petitioners further contendthat
`
`it would have been obvious to modify Miluzzo’s inference engine to
`
`incorporate the theme and user context recognition of Robarts because both
`recognize a user’s context based on sensordata. Pet. 38. Petitioners further
`argue that the themes and user context recognition of Robarts is a substitute
`
`for Miluzzo’s inference engine, and the results would have been predictable.
`
`Pet. 39 (citing Ex. 1003 { 140-144).
`
`Petitioners note that independent claim 19 includes a step of
`“constructing a social signature” instead of “a processor which .
`.
`. creates a
`detected social signature.” Pet. 47. Petitioners argue that Miluzzo and
`
`Robarts also teach the limitations of claim 19 because “[a]s used in the
`
`claim, ‘constructing’ is the sameas ‘creating,’ and the art applies in the same
`way as described with respect to claim 1.” /d. (citing Ex. 1003
`167). At
`this stage of the proceeding, Petitioners’ arguments for claims 1 and 19 are
`
`reasonable and supported by record evidence.
`
`Patent Ownerrespondsthat “Miluzzo provides no description of a
`
`data structure that can be equated with the ‘social template.’” Prelim. Resp.
`
`48. The argumentis not persuasive becausePetitioners provide citations to
`Robarts and arguments that the proposed combination of Miluzzo and
`
`Robarts includes a “social template.” See Pet. 37-39 (citing Ex. 1003 {J
`
`140-144; Ex. 1008 Abstract, J] 40, 62, 224, Fig. 8).
`
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`Patent Owneralso arguesthat Petitioners do not “point to any portion
`
`of Robarts that specifies the collection of ‘attributes’ must be unique to any
`
`particular theme,” and Robarts does not require attributes to be unique.
`
`Prelim. Resp. 49 (citing Pet. 37-38; Ex. 1008 | 207). Patent Owner quotes a
`
`portion of paragraph 207 that states “themes can also specify other types of
`
`information, such as whether someorall of the information about the theme
`is available to other themes.” Jd. The quoted portion, however, does not
`address directly the attributes of a theme,and thus, on the current record, we
`are not persuaded.
`
`Patent Ownerfurther argues that Petitioners rely on the attributes,
`which are described in paragraph 92 of Robarts,as part of the “detected
`social signature” rather than the recited “social template.” Prelim. Resp. 50
`
`(citing Pet. 42). Petitioners, however, cite paragraph 40 of Robarts for
`
`teaching “‘themes’ (i.e., social templates)” that include “‘attributes’ (i.e.,
`
`unique social signatures)” and paragraphs 56 and 63 of Robarts for teaching
`
`the creation of “‘a current model’ (i.e., a detected social signature) of the
`
`user based on collected sensor data.” Pet. 37-38, 42. Petitioners cite
`
`paragraph 92 of Robarts for teaching that its attributes can be specified as a
`
`range. See Pet. 38, 43. On the current record, we are not persuaded.
`With reference to Figure 13 of Robarts, Patent Owner arguesthat the
`“uncertainty value” of Robarts is not a “sensor value range”and instead,“is
`
`used to determine the accuracyof a particular attribute contained within a
`
`theme.” Prelim. Resp. 50-52. Petitioners, however, cite Figure 8, not
`
`Figure 13, for teaching that the attributes of Robarts can include data from
`
`light and soundsensors, and Petitioners cite paragraph 92 of Robarts for
`
`teaching a “sensor value range.” See Pet. 38. Paragraph 92 describes “an
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`uncertainty value that represents a range of values aroundtheattribute value
`| that the attribute is likely to have.” Because Patent Owner’s arguments do
`not address Figure 8 or paragraph 92 of Robarts, we are not persuaded.
`
`On the present record, we are persuaded that Petitioners have a
`
`reasonable likelihood of prevailing in showing that claims 1 and 19 are
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`unpatentable over Miluzzo and Robarts.
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`4, Dependent Claims 3, 4, 8-10, 21, 23, 25, and 26
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`Petitioners argue that Miluzzo and Robarts eachteachthe limitations
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`of claim 3. Pet. 47 (citing Ex. 1007 J 53; Ex. 1008 4 203). Petitioners also
`
`argue that a person ofordinary skill in the art “would have recognizedthat,
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`because more than one subset of the sensed data can be created, a group
`policy can specify even more levels of disclosure” and “that providing
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`different levels of disclosure involves determining to which group a
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`receiving user belongs, then providing the amountof information as
`
`specified in the group membershippolicy.” /d. at 47-48 (citing Ex. 1003 9§
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`168-169).
`
`For claims 4 and 21, Petitioners assert that Miluzzo and Robarts teach
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`the limitations of these claims. Pet. 48-53 (citing Ex. 1007 {ff 45, 69; Ex.
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`1008 Abstract, JJ 20, 40, 194, 199, 214, 272-277, Figs. 12A—12H, 17).
`
`Petitioners also argue that a person of ordinary skill in the art would be
`
`motivated to seek out methodsofinferring presence status and reducing
`
`incorrect inferences, as taught by Robarts and would have understood that
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`“such ‘humanactivity inferring algorithms’ required training because they
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`may makeincorrect inferences based on sensed conditions.” Pet. 48-49
`
`(citing Ex. 1003 4 172, 173).
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`16
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`Petitioners assert that it would have been obvious“to incorporate
`Robarts ‘appropriateness verification’ with Miiuzzo’s inference engine to
`
`improve the inference engine’s ability to make better inferences regarding
`
`the sensed context” and the proposed modification is “using Robarts’ known
`
`technique of verifying the appropriateness of sensed user context to improve
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`the similar process described in Miluzzo.” Pet. 50 (citing Ex. 1003 {J 171-
`
`174). Petitioners also assert that it would have been obviousthat Robarts’s
`
`appropriatenessverification “would involve adjusting the ‘uncertainty value’
`
`(i.e., the range) for at least one attribute to incorporate the detected error”
`
`and the “uncertainty value” is modifiable by Robarts’s GUI. Pet. 51 (citing
`
`Ex. 1003 4 173; Ex. 1008 § 214). Petitioners further assert that it would
`
`have been obviousto “create a new theme based on current context
`
`information, if the wrong themeis presented” and creating a new themeis an
`
`obviousalternative. Pet. 52 (citing Ex. 1003 J 176-178; Ex. 1005 { 277).
`
`Forclaims 8 and 23, Petitioners contend that Miluzzo teachestheir
`
`limitations and argue that it would have been obviousthat “one disclosure
`
`level in Miluzzo would correspondto one social networking service.” Pet.
`
`53 (citing Ex. 1003 | 182; Ex. 1007 ff 21, 52, 69). For claims 9 and 25,
`
`Petitioners assert that Miluzzo teachestheir limitations and argue that a
`
`person of ordinary skill in the art “would recognize that the social
`networking service ‘Facebook’ contains microblogging features.” Pet. 53—
`
`54 (citing Ex. 1003 | 185-186; Ex. 1007 ff 52, 69, 88). For claims 10 and
`26, Petitioners argue that Robarts teachestheir limitations. Pet. 54-55
`
`(citing Ex. 1008 ff 114, 118,210).
`
`-
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`17
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`Atthis stage of the proceeding,Petitioners’ arguments regarding
`
`dependent claims 3, 4, 8-10, 21, 23, 25, and 26 are reasonable and supported
`
`by record evidence.
`
`Patent Ownerrespondsthat, for claims 4 and 21, Robarts does not
`
`teach explicitly that the appropriateness verification of Robarts “‘is triggered
`
`by a detection of an ‘error between the detected social signature and the
`
`social signature of the determined one social template having the greatest
`correspondence,’” as required by these claims. Prelim. Resp. 54 (citing Ex.
`1008 § 273). However, Petitioners rely on a portion of Robarts that
`
`describes, when a suggested rule is presented to the user for verification, the
`
`user can respond with “the system should ask the user for confirmation
`
`(because the suggestion was good,just not entirely appropriate).” Pet. 49
`
`(citing Ex. 1008 {] 273-276). At this stage of the proceeding, Patent
`
`Owner’s argumentis not persuasive.
`Patent Owneralso argues that Robarts does not teach updating a
`social signature with the detected error, as required by claims 4 and 21.
`
`Prelim. Resp. 55-57. Patent Ownerasserts that examples of feedback
`
`described in Robarts do not adjust the uncertainty value and instead add a
`
`new sensorvalue. Jd. at 56—57 (citing Ex. 1008 J] 279-280, 285-309).
`
`Petitioners, however, rely on paragraph 276 of Robarts that describes
`“snecify[ing] more information to the system to improve the appropriateness
`
`of the request” and Petitioners assert it would have been obviousthat the
`“more information” would involve adjustinga range of an uncertainty value.
`See Pet. 51. At this stage of the proceeding, Patent Owner’s argumentis not
`
`persuasive.
`
`18
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`IPR2015-01473
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`Patent Owneralso argues that Petitioners’ motivation to combine
`
`Miluzzo aid Robarts is unsupported (discussing Ex. 1003 § 172). Prelim.
`
`Resp. 54. Paragraph 172 of the Williams Declaration cites paragraphs 278—
`285 of Robarts, which reasonably support the contention of paragraph 172.
`On the current record, Patent Owner’s argumentis not persuasive.
`
`The Preliminary Response presents no specific arguments for claims
`
`3, 8-10, 23, 25, and 26. Thus, we are persuadedthat the present record
`showsa reasonable likelihood of Petitioners prevailing in the challenge of
`claims 3, 4, 8-10, 21, 23, 25, and 26 as unpatentable over Miluzzo and
`
`Robarts.
`
`C. Obviousness of Claim 6 over Miluzzo, Robarts, and Luo
`
`Petitioners contend that claim 6 is rendered obvious by Miluzzo,
`
`Robarts, and Luo with citations to the disclosures in these references and the
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`Williams Declaration (Ex. 1003). Pet. 55-57.
`
`1. Luo (Ex. 1017)
`
`Luoproposes “preliminary results on defining and tackling
`
`information aggregation attacks over online social networks.” Ex. 1017
`
`Abstract. It states that, “[flor instance, people trust LinkedIn as a
`
`professional/business network” and “assumethat their information would
`
`stay in the network.” Jd. § I. Luostates that “the information could be
`
`easily accessed from outside of the context due to wrong configuration, mal-
`
`functioning code, or user’s misunderstanding” and that “users of multiple
`
`social networks maynot want information from different contexts to mix up
`
`with each other.” Jd. Luo consequently “define[s] multilevel and
`
`discretionary models to manage private information.” Jd. § III. “In the
`
`multilevel model, private information is managedin hierarchically organized
`
`19
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`categories,” and “[i]nformation objects in higher levels are considered more
`
`private than objects in lowerieveis.” id.
`
`2. Claim 6
`
`Claim 6 depends from claim 1 andrecites “wherein, for at least one of
`
`the social templates, each level of the social hierarchy correspondsto a
`
`corresponding different social networking service, and the processor
`automatically provides different updates to each ofthe social networking
`services as allowed based onthe onesocial template.”
`
`Petitioners contendthat, although Miluzzo teaches creating buddy
`
`sub-lists from different social network services, Miluzzo and Robarts do not
`
`teach explicitly “how to define access policies for each imported buddy sub-
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`list from each social networking service.” Pet. 55 (citing Ex. 1007 § 52).
`
`Petitioners rely on Luoto teachthat different social networks are used for
`
`different purposes and contacts, that “users of multiple social networks may
`
`not want information from different contexts to mix up with each other,” and
`
`that “Luo describes a private information model in which different social
`
`network correspondsto a different privacy disclosure set.” /d. (citing Ex.
`
`1017 §§ I, I).
`
`Petitioners assert that a person of ordinary skill in the art “would have
`
`been motivated to seek out a teaching of how to define the accesspolicies
`
`while protecting the private user presence information” and “would have
`
`been motivated to seek out a per-social-network based privacy protection
`
`setting because Miluzzo teaches creating buddy sub-lists in defining access
`
`policies and that the sub-lists are imported from different social networks.”
`
`Pet. 55, 56.
`
`20
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`Petitioners contend that it would have been obvious“to modify the
`
`Miluzzo-Robvarts system such ihat each disciosure ievei ina theme...
`
`would correspondto a different social network, as taught by Luo...to
`
`prevent mixing up information from different social networks.” Pet. 55-56
`
`(citing Ex. 1017 § I). Petitioners also contend that incorporating Luo’s
`private information model with each privacy disclosure set corresponding to
`
`a different social network is a simple substitution of Miluzzo-Robarts’ theme
`
`with each disclosure level correspondingto a different group. Jd. at 56.
`
`Petitioners further contend that the substitution provides the predictable
`
`result of “a theme with each disclosure level correspondingto a different
`social network.” Jd. At this stage ofthe proceeding, Petitioners’ contentions
`regarding claim 6 are reasonable and supported by record evidence.
`
`Patent Ownerrefers to its arguments against Petitioners’ challenge of
`
`claim 6 based onJolliff, Jager, and Luo. Prelim. Resp. 53, 57. In its
`
`previous arguments, Patent Ownerrespondsthat Luo does not teach “social
`29 66
`
`templates,”
`
`“social hierarchies,” and a processorthat “automatically
`
`provides different updates to each of the social networkingservices as
`
`allowed based on the one social template.” Prelim. Resp. 35. Patent Owner
`
`argues that Luo “does not disclose a system that automatically provides
`
`differing levels of information to different social networks based on
`
`information obtained from sensors”and instead, “provides a vehicle for a
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`user to make informed choices about what information he or she manually
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`places on her social networks.” Jd.
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