UNITED STATES PATENT AND TRADEMARK OFFICE
`
`______________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________
`
`
`APPLE, Inc.,
`
`Petitioner
`
`v.
`
`QUALCOMM INCORPORATED,
`
`Patent Owner
`______________
`
`Case IPR2018-01281
`
`U.S. Patent No. 8,768,865
`______________
`
`
`
`DECLARATION OF JOHN VILLASENOR
`
`
`
`
`
`
`1
`
`Apple Inc. v. Qualcomm Incorporated
`IPR2018-01281
`Qualcomm Ex. 2004
`Page 1 of 30
`
`
`
`______________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________
`
`
`APPLE, Inc.,
`
`Petitioner
`
`v.
`
`QUALCOMM INCORPORATED,
`
`Patent Owner
`______________
`
`Case IPR2018-01281
`
`U.S. Patent No. 8,768,865
`______________
`
`
`
`DECLARATION OF JOHN VILLASENOR
`
`
`
`
`
`
`1
`
`Apple Inc. v. Qualcomm Incorporated
`IPR2018-01281
`Qualcomm Ex. 2004
`Page 1 of 30
`
`
`
`A. Qualifications
`1. My name is John Villasenor. I am a professor at UCLA. I have been retained
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`by Qualcomm Incorporated to provide opinions in the inter partes review
`
`proceedings IPR2018-01281 (the ‘1281 proceeding) and IPR2018-01282 (the
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`‘1282 proceeding) challenging U.S. Patent No. 8,768,865 (the ‘865 Patent).
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`2. My work focuses on
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`innovative, high-performance communications,
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`networking, media processing, and computing technologies and their broader
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`implications. Since well before the respective priority dates of the ‘865 Patent, I
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`have performed research in areas including image processing, machine learning,
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`and delivering content to mobile devices. For example, I have also done research
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`in machine learning, with substantial experience developing algorithms that adapt
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`in response to changing characteristics in the environment as reflected, for
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`example, through data measured through sensors. In addition, I have performed
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`research in relation to mobile devices since the 1990s. This research included
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`multiple aspects of mobile devices, including wireless communications, sensing
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`information (such as orientation) on mobile devices, and methods for delivering
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`content to mobile devices, including considerations such as the selection of type of
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`content for transmission to the mobile device.
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`3.
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`I received my B.S. in Electrical Engineering from the University of Virginia
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`in 1985, and M.S. and Ph.D. in Electrical Engineering from Stanford University in
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`1986 and 1989, respectively.
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`4. While at Stanford, I concentrated my research on digital signal processing
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`and communications.
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`5.
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`Between 1990 and 1992, I worked for the Jet Propulsion Laboratory in
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`Pasadena, CA, where I helped to develop techniques for imaging and mapping the
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`earth from space. Since 1992, I have been on the faculty of the Electrical
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`Engineering Department of the University of California, Los Angeles (UCLA).
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`Between 1992 and 1996, I was an Assistant Professor; between 1996 and 1998, an
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`Associate Professor; and since 1998, I have been a full Professor.
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`6.
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`For several years starting in the late 1990s, I served as the Vice Chair of the
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`Electrical Engineering Department at UCLA. I also hold an appointment in the
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`Department of Public Policy within the UCLA School of Public Affairs. In
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`addition, I teach in the UCLA Anderson School of Management.
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`7.
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`Since joining the UCLA faculty in 1992, my research has addressed
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`software, algorithms, hardware, networking, protocols, and other aspects of
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`systems and devices that acquire, store, process, transmit, and display information.
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`8.
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`I am an inventor on approximately 20 issued and pending U.S. patents in
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`areas including signal processing, data compression, communications, and
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`cybersecurity. I have published over 150 articles in peer-reviewed journals and
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`academic conference proceedings.
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`9.
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`In addition to my work at UCLA, I am a nonresident senior fellow at the
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`Brookings Institution in Washington, D.C. Through Brookings I have examined a
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`wide range of topics at the technology/policy intersection including cybersecurity,
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`wireless mobile devices and systems, intellectual property, financial inclusion for
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`“unbanked” populations, digital media policy, “drones,” critical infrastructure
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`security, driverless cars, and digital currencies and emerging payment methods. I
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`have published articles and commentary related to technology policy in venues
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`including Billboard, the Brookings Institution, the Chronicle of Higher Education,
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`Fast Company, Forbes, the Huffington Post, the Los Angeles Times, Scientific
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`American, Slate, and the Washington Post.
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`10. My attached curriculum vita (Ex. 2006) details my over expertise and
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`experience in the field of computer graphics and image processing.
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`B. Materials reviewed
`11.
`I have reviewed each Petition submitted in each of the ‘1281 and ‘1282
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`proceedings, as well the Patent Owner Preliminary Response submitted in each of
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`the ‘1281 and ‘1282 proceedings.
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`12.
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`I have reviewed the ‘865 Patent that is included as Exhibit 1001 in each of
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`the ‘1281 and ‘1282 proceedings, U.S. Provisional Application No. 61/434,400,
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`4
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`which incorporated by reference by the ‘865 Patent, that is included as Exhibit
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`2001 in each of the ‘1281 and ‘1282 proceedings, and the prosecution history for
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`the ‘865 Patent, excerpts of which are included as Exhibit 1002 in each of the
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`‘1281 and ‘1282 proceedings.
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`13.
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`I have reviewed each declaration of Dr. Allen, which is Ex. 1003 in the
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`‘1281 proceeding and Ex. 1021 in the ‘1282 proceeding.
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`14.
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`I have reviewed each Institution Decision entered by the Panel in each of the
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`‘1281 and ‘1282 proceedings.
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`15.
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`I have reviewed the transcript of Dr. Allen’s deposition conducted as part of
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`the ‘1281 and ‘1282 proceedings.
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`16. A complete listing of the documents I reviewed is as follows:
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`Proceeding
`
`Pr/ Ex Title
`
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`1281 Proceeding
`
`1281 Proceeding
`1282 Proceeding
`1282 Proceeding
`1282 Proceeding
`1282 Proceeding
`1282 Proceeding
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`2 Petition
`6 Patent Owner Preliminary Response
`7 Institution Decision
`1001 U.S. Pat. No. 8,768,865
`1002 Excerpts of ‘865 Patent Prosecution History
`1003 Allen Declaration
`1005 Wang
`2001 U.S. Provisional Application No. 61/434,400
`(Incorporated by Reference by the ‘865 Patent)
`2003 Deposition of James Allen (April 25, 2019)
`2 Petition
`6 Patent Owner Preliminary Response
`7 Institution Decision
`1011 Louch (U.S. 8,676,224)
`1021 Allen Declaration
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`5
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`C. Legal Principals
`17.
`I understand in this IPR proceeding claim terms, the BRI standard applies,
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`which means claim terms generally are given their ordinary and customary
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`meaning, as would be understood by one of ordinary skill in the art in the context
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`of the entire disclosure.
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`18.
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`I understand that for a prior art reference to anticipate a claim, elements of a
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`claim must be disclosed within the reference either expressly or inherently. For a
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`claim element to be inherent in a reference, I understand that the claim element
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`must be necessarily present in the reference. I also understand that the reference
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`must clearly and unequivocally disclose the claimed invention or direct those
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`skilled in the art to the invention without any need for picking, choosing, and
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`combining various disclosures not directly related to each other by the teachings of
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`the cited reference.
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`D. Overview of the ‘865 Patent
`19. The ‘865 Patent generally concerns methods and apparatuses relating to
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`machine learning on a mobile device, such as a smartphone or tablet. The ‘865
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`Patent addresses the problem of making sense of large amounts of multi-
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`dimensional data, as may be generated and/or processed by a mobile device. Such
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`multi-dimensional data may be used to identify user behavior or actions. As the
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`number of sensors and/or other data sources in a mobile device increases, it
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`becomes more difficult to process and analyze sensor and device data in order to
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`identify relevant patterns. When faced with a large multi-dimensional dataset,
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`efficiency of the associated analysis becomes extremely important—particularly on
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`a mobile device with limited resources. The ‘865 Patent describes this problem:
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`A popular and rapidly growing market trend in sensor-enabled
`technology includes, for example, intelligent or smart mobile
`communication devices that may be capable of understanding what
`associated users are doing (e.g., user activities, intentions, goals, etc.)
`so as to assist, participate, or, at times, intervene in a more meaningful
`way. Integration of an ever-expanding variety or suite of embedded or
`associated sensors that continually capture, obtain, or process large
`volumes of incoming information streams may, however, present a
`number of challenges. These challenges may include, for example,
`multi-sensor parameter tracking, multi-modal information stream
`integration, increased signal pattern classification or recognition
`complexity, background processing bandwidth requirements, or the
`like, which may be at least partially attributed to a more dynamic
`environment created by user mobility. Accordingly, how to capture,
`integrate, or otherwise process multi-dimensional sensor information
`in an effective or efficient manner for a more satisfying user
`experience continues to be an area of development.
`
`Ex. 1001 at 1:42-60. The patent also notes:
`
`As alluded to previously, continually tracking or monitoring all or
`most varying parameters or variables that may be associated with a
`multi-dimensional stream of sensor
`information may be a
`computationally intensive, resource-consuming, at times intractable,
`or otherwise less than efficient or effective approach for pattern
`matching or recognition.
`Id. at 7:58-63.
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`7
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`20. The ‘865 Patent teaches an efficient multi-stage approach processing and
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`analyzing this multi-dimensional data, thereby saving device resources and
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`improving the performance of the mobile device. See, e.g., id. at 8:50-54 (“This
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`may make pattern matching more tractable or otherwise allow for a more effective
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`or efficient pattern recognition since a pattern matching process is performed in a
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`remaining or reduced set of variables.”). By using this multi-stage approach, the
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`mobile device avoids the shortcomings of the prior art requiring “continually
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`tracking or monitoring all or most varying parameters.” See, e.g., id. at 7:58-63.
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`21.
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` One exemplary embodiment is disclosed in Figure 4 of the ‘865 Patent:
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`8
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`In the context of the ‘865 Patent claims, a mobile device monitors its various
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`22.
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`sensor and application data sources and detects a condition or an event of interest.
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`See, e.g., id. at 7:40-45; 8:54-60; 9:7-11; 14:60-65. This can be considered a first
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`stage.
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`23. The mobile device also identifies a “first pattern” based on this detected
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`condition. See, e.g., id. at Fig. 4, 14:66-15:5. The “first pattern may comprise a
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`distinct signal-related pattern having one or more varying parameters or variables
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`of interest that may be representative or otherwise correspond to” the “detected
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`condition.” See id. at 15:1-5.
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`24. The mobile device then “fix[es] a subset of varying parameters associated
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`with said first pattern.” See id. at Fig. 4. As will be discussed below, this “fixing”
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`step sets the scope of analysis for subsequent pattern recognition. Specifically, the
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`mobile device may then “initiat[e] a process to attempt recognition of a second
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`pattern.” See id. This can be considered a second stage. By fixing varying
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`parameters of the first pattern, during that second stage, the mobile device may
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`attempt to recognize a second pattern that occurs when the fixed varying
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`parameters match the first pattern. See id. at 13:23-37.
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`25. Figure 5 of the ‘865 Patent illustrates an exemplary mobile device with a
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`number of sensors that may be monitored for patterns indicative of user behavior
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`or action:
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`10
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`26. Like most modern mobile devices, the device depicted in Figure 5 has
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`several sensors, including, in this example, one or more accelerometers 514, an
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`ambient light sensor 516, a proximity sensor 518, and other sensors 520 “such as a
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`gyroscope, magnetometer, microphone, camera, GPS, WiFi, Bluetooth™-enabled
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`devices, etc. to facilitate or otherwise support one or more processes associated
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`with operating environment 500.” See id. at 17:28-42. The ‘865 Patent explains
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`that “parameters,” “varying parameters,” or “variables” are “derived” from these
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`monitored sensors. See id. at 9:63-10:3. The patent uses these three terms
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`interchangeably. Below, for shortness, I may use only one term, but use of any
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`such one term should be considered a reference to all three terms. The ‘865 Patent
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`also teaches that other “variables” may be derived from non-sensor sources, such
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`as indications user behavior or action, including time of day, day of the week, a
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`state or action of an application, and actions taken on the mobile device (e.g.
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`silencing a ringer, muting a call, or sending a text message). See id. at 8:1-6,
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`14:60-65. The variables have values that change over time. See, e.g., id. at Figure
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`2.
`
`The ‘865 Patent describes examples of “variables” that are derived using different
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`levels of analysis, and one variable may be used to derive another variable. For
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`example, the patent describes “acceleration” as an example of a “variable.” Id. at
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`4:8-13. The patent also describes “motion state” as another variable and explains
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`11
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`“acceleration” may be used to determine values of the variable “motion state” such
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`as “driving,” explaining: “an acceleration vibration may, for example, indicate that
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`a user is driving or walking.” Id. at 7:30-32.
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`27. A person of ordinary skill in the art would understand that “fixing”
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`parameters, in the context of the ‘865 Patent, refers to setting the scope of analysis
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`to enable pattern recognition of additional patterns when there is a pattern in the
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`fixed parameters.
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`28. The ‘865 Patent describes the function of “fixing” on several occasions, each
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`consistent with the understanding described above. First, the ‘865 Patent explains:
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`At least one subset of variables of interest may be fixed, as discussed
`above, and one or more patterns in a second subset of variables may
`be identified, for example, if there is a pattern in the fixed subset of
`variables.
`
`Id. at 13:19-22.
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`29. Next, the ‘865 Patent explains:
`
`By way of example but not limitation, an application processor
`associated with a mobile device may observe what other variables
`have patterns if a motion state corresponds, for example, to "driving,"
`as one possible illustration.
`Id. at 13:23-26. The patent goes on to explain that what was described in the
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`quotation above is “fixing one variable associated with or corresponding to
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`‘driving.’” Id. at 13:36-37.
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`30. The ‘865 Patent references U.S. Provisional Application No. 61/434,400 and
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`states “the entire disclosure of which is hereby incorporated by reference.” Id. at
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`1:7-10. I understand that this means that the contents of that provisional
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`application are part of the ‘865 Patent disclosure. That provisional application
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`states:
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`■ Monitor variables individually for patterns
`■ Fix one subset of variables and identify patterns in a second subset
`of variables when there is a pattern in the fixed subset of variables
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`■ E.g., observe what other variables have patterns when motion
`state corresponds to “driving”
`Ex. 2001 at 2001.015.
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`31. The ‘865 Patent also uses the term “associating.” A person of ordinary skill
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`in the art would understand that in the computer science context, the word
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`“associating” refers to a basic building-block step that may be used in any number
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`of contexts to, along with other steps, achieve any number of goals. This is also
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`how the term is used in the ‘865 Patent. For example, the ‘865 Patent describes
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`“associating” as part of multiple different processes:
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` “An example of context labeling may include associating a specific
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`accelerometer pattern with the context ‘surfing,’ for example, by providing
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`other context, such as a camera view, location corresponding to a beach,
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`detecting ‘wetness’, or the like.” Ex. 1001 at 14:13-17 (emphasis added).
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`13
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` “In some instances, a subset may be fixed, for example, by associating
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`parameters or variables with a particular, distinct, or otherwise suitable
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`pattern to represent a certain detected condition or event, as one possible
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`example.” Id. at 15:9-12 (emphasis added)
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`A person of ordinary skill in the art would understand that “context labeling” and
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`“fixing” are not the same processes.
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`32. A person of ordinary skill in the art would understand that the ‘865 Patent
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`describes “associating” as a substep of “fixing,” that “associating” does not, on its
`
`own, accomplish “fixing.” As discussed above, the patent explains that by “fixing”
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`“motion state” to “driving,” “a mobile device may observe what other variables
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`have patterns if a motion state corresponds, for example, to ‘driving,’ as one
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`possible illustration.” Id. at 13:23-26, 13:36-37. But a mobile device would not
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`able to “observe what other variables have patterns if a motion state corresponds,
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`for example, to ‘driving,’” if all that had been done was “associating” “motion
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`state” with “driving.” Rather, that association must be used to set the scope of
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`analysis to enable pattern recognition of additional patterns when “motion state” =
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`“driving.”
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`33. The concept of fixing can be illustrated through annotation of Figure 2 of the
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`‘865 Patent. In the first annotation below, the scope of analysis is shown as the
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`area inside the red box.
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`14
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`‘865 Patent Figure 2 (Annotated to Show “Fixing”)
`34. The scope of analysis shown in the red box corresponds to where “Motion
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`State” = “Driving.” As discussed above, the ‘865 Patent uses “fixing” to describe
`
`this step of setting the scope of analysis. The substep of “associating” “Motion
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`State” with “Driving” is part of this process, but on its own does not accomplish
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`fixing.
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`35. The next annotation below shows a second pattern found within the red box
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`as a result of pattern recognition efforts after the “fixing” process. Specifically,
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`based on the “fixing … by associating …,” the system can then attempt to
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`recognize a second pattern when motion state corresponds to “driving.” Ex. 2001
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`at 2001.015. This example second pattern is “Motion State” = “Driving” +
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`“Change in Location” from “SSID_3” to “SSID_1.”
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`‘865 Patent Figure 2 (Annotated to Show “Recognize Second Pattern”)
`36. The patent also explains that by “fixing,” “a set of variables associated with
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`a multi-dimensional sensor information stream may be advantageously reduced.” I
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`note that “fixing” is not reducing variables, but a reduction in variable can result
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`from “fixing.” For example, within the scope of analysis set in the fixing step, the
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`variable “Brightness Level” remains unchanged. The system may be able to
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`disregard that variable as a result. This is shown in the annotation below.
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`16
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`‘865 Patent Figure 2 (Annotated to Show “Variable Disregarded”)
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`E. Claim Construction
`i.
`“Pattern”
`37. A person of ordinary skill in the art would understand the BRI of “pattern,”
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`as used in the ‘865 Patent, to be: “a collection of one or more pairs of varying
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`parameters and corresponding parameter values, as well as the relationship
`
`between each pair (where the relationship may be implicit).” The claims refer to
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`both a “first pattern” and a “second pattern.” I understand the Petitioner proposed
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`a construction of “a collection of one or more parameter values.” Pet. at 11-13.
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`This construction is incomplete because it does not explicitly state that the pattern
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`includes not only parameter values, but the linked parameter.
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`38. One example pattern in the patent is: “location X AND motion state Y.” Id.
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`at 13:8-13. Here, “location” and “motion state” are variables, with “X” and “Y”
`
`being the corresponding values. “AND” is a logical representation that indicates
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`that the pattern requires both pairs. Figure 3 of the patent illustrates another
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`example of a pattern:
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`
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`39.
`
`In this example, “context_type” refers to “variables” and “context_value”
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`refers to the linked variable value. In this example, while not written down, it
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`would be understood that logically each pair of variables and corresponding values
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`are required to make up the pattern. That is, the pattern may be represented as:
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`“SoundIntensity = Loud AND PeriodicMovement = Running.”
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`40. The patent also discusses “identifying” patterns. A person of ordinary skill
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`in the art would understand that identifying a pattern means identifying all of the
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`elements I discussed above that make up the pattern.
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`ii.
`“Fixing … by Associating …”
`41. Consistent with the descriptions of “fixing” and “associating” above, a
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`person of ordinary skill in the art would understand that “fixing a subset of varying
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`parameters associated with said first pattern by associating at least one parameter
`
`of said subset of varying parameters with said first pattern to represent said at least
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`one detected condition” means “setting the scope of pattern recognition analysis to
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`where a subset of varying parameters match parameter values associated with said
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`first pattern by associating at least one parameter of said subset of varying
`
`parameters with said first pattern to represent said at least one detected condition.”
`
`This term is found in each of the challenged independent claims (Claims 1, 21, or
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`46). As discussed above, the ‘865 Patent consistently describes the function of
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`“fixing” parameters as setting the scope of analysis to enable pattern recognition of
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`additional patterns when there is a pattern in the fixed parameters.
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`42.
`
`I understand that Petitioner has asserted that “[t]his phrase is broad enough
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`to encompass ‘associating at least one parameter of a subset of varying parameters
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`with the first pattern to represent at least one detected condition.’” I do not agree
`
`that a person of ordinary skill in the art would interpret the claim language in this
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`way, which takes the original language and removes “fixing a subset of varying
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`parameters associated with said first pattern,” for several reasons.
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`43. First, as I have discussed above, “associating at least one parameter of a
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`subset of varying parameters with the first pattern to represent at least one detected
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`condition,” on its own, does not accomplish what the specification repeatedly
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`describes “fixing” as accomplishing. Rather, “associating” is only a substep that
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`on its own does not accomplish “fixing.” Therefore, Petitioner’s proposed
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`construction is contrary to the description of “fixing” in the patent.
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`44. Second, Petitioner’s construction—“associating at least one parameter of a
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`subset of varying parameters with the first pattern” is indistinguishable from the
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`separately-recited step of “identifying a first pattern based, at least in part, on said
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`at least one detected condition.” As discussed above, “identifying a first pattern”
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`includes identifying each of the variables included in the first pattern and linking
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`those variables to corresponding values.
`
` But Petitioner’s construction—
`
`“associating at least one parameter of a subset of varying parameters with the first
`
`pattern”—also just covers that same “linking” activity and nothing more. That is,
`
`“associating at least one parameter of a subset of varying parameters with the first
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`pattern” is already accomplished as part of identifying the first pattern because
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`identifying the first pattern includes linking parameter values of the first pattern
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`with the parameters of the first pattern. A person of ordinary skill in the art would
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`not interpret the “fixing” step in a way that made it duplicative of the “identifying”
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`step.
`
`F.
`45.
`
`Level of ordinary skill in the art
`In my opinion, a person of ordinary skill in the art relevant to the ’865 Patent
`
`at the time of its invention would have had a Bachelor’s of science degree in
`
`electrical engineering, computer science, computer engineering, or a closely-
`
`related field, and at least 2 years of work or research experience in the field of
`
`machine learning or a closely related field. More work experience could
`
`compensate for less education, and vice versa. At the time of the earliest filing
`
`date to which the ‘865 Patent claims priority, I was at least a person of ordinary
`
`skill.
`
`46.
`
`I note that the Allen Declaration defines a person of ordinary skill in the art
`
`as having a Bachelor of Science degree in either computer science or electrical
`
`engineering, together with at least two years of study in an advanced degree
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`program in artificial intelligence, machine learning, or pattern recognition, or
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`comparable work experience.
`
`47. To the extent there are any differences in these two definitions of one of
`
`ordinary skill in the art, I do not believe that there is a meaningful change of
`
`outcome using one definition of a person of ordinary skill in the art or the other.
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`G. Opinions about Wang and Petitioner’s mapping to the challenged
`claims
`I have reviewed Wang et al, “A Framework of Energy Efficient Mobile
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`48.
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`Sensing for Automatic User State Recognition” (“Wang”). I have also reviewed
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`Petitioner’s mapping of “GROUND-1A” asserting that “Wang anticipates Claims
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`1-4, 15-17, 21-23, 28, 29, 46, 47.” I understand that Petitioner also asserted other
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`grounds alleging obviousness of certain claims based on Wang in view of
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`additional references. My opinions relate to Petitioner’s mapping of Wang to
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`challenged claims. I have not been asked to address those additional references. I
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`note the additional grounds relate solely to dependent claims. For the reasons
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`described below, Wang does not anticipate the challenged independent claims
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`(Claims 1, 21, or 46), or dependent Claims 4 or 23. The claims addressed in
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`GROUND-1B and GROUND-1C are not anticipated based on the fact that they
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`depend on claims I address.
`
`i.
`Overview of Wang
`49. Wang describes “a novel design framework for an Energy Efficient Mobile
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`Sensing System (EEMSS).” Ex. 1005 at 1. Wang describes a system that uses the
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`current state and detection of “state transitions” to recognize user states: “EEMSS
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`uses hierarchical sensor management strategy to recognize user states as well as to
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`detect state transitions.”
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`22
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`Page 22 of 30
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`50.
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`“The core component of EEMSS is a sensor management scheme which
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`defines user states and state transition rules by an XML styled state descriptor.” Id.
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`at 2. “In essence, the state descriptor consists of a set of state names, sensors to be
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`monitored, and conditions for state transition.” Id. at 3. Wang explains that the
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`“conditions for state transition” are used to determine when to transition from one
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`state to another. For example, Wang explains: “If the state transition criteria is has
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`been satisfied, the user will be considered as entering a new state (denoted by
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`“<NextState>” in the descriptor).” Id. at 4. Thus, Wang discloses EEMSS using
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`the fact that the device is in a given state in combination with “state transition
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`criteria” being satisfied to define a next state.
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`51. Wang also explains that EEMSS achieves “energy savings” by defining a
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`different set of sensors to be used depending on the current state. Id. at 3. Wang
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`describes this as the “sensors to be monitored” specified in the XML styled state
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`descriptor file. Id. The sensors to be monitored reflect what sensors need to be
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`monitored to detect a state transition to a next state. Thus, the selection of those
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`sensors is based on the defined next state or states in the system design. It does not
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`reflect what sensors were needed to detect the current state.
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`52. Wang also includes a Table 1. Wang states: “Table 1 illustrates the set of
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`user states to be recognized by EEMSS and three characteristic features that define
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`23
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`Page 23 of 30
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`each of these states. The three features are the location, motion and background
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`sound information.” Id. at 5. Table 1 is reproduced below:
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`
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`Id. at 6.
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`53. While Table 1 describes a way to “define” states, Wang does not disclose
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`using those definitions in the EEMSS system. That is, Wang defines states in two
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`different ways—based on the groups of “State Features” and linked values shown
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`in Table 1, but separately based on the current state and a state transition—but only
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`describes EEMSS using one the latter definition. For example, Table 1 defines
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`state “Vehicle” as “Location” = “Keep on Changing” and “Motion” = “Moving
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`Fast.” Id. at 6. But Wang does not describe EEMSS using that definition. Instead,
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`Wang explains that EEMSS defines “Vehicle” as the combination of being in the
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`state “Walking” and a state transition being detected: “If a significant amount of
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`increase is found on both user speed and recent distance of travel, a state transition
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`will happen and the user will be considered riding a vehicle.” Id. at 5. It is this
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`24
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`Page 24 of 30
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`information that is provided in the XML state descriptor file. See id. at 3. A
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`person of ordinary skill in the art would understand that the use of a current state
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`plus a state transition criteria to define a next state is an alternative to defining a
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`state based on a collection of state features and values.
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`54. Further, the EEMSS system is incompatible with defining a state based on a
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`collection of state features and values. As discussed above, Wang states that
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`EEMSS uses “an XML-format state descriptor as system input that contains all the
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`states to be automatically classified as well as sensor management rules for each
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`state.” Id. at 3. Wang also illustrates this arrangement in Figure 3, reproduced
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`below. As discussed above, the XML state descriptor file does not include “state
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`features” and values as listed in Table 1. Thus, it is not possible for EEMSS to use
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`that information.
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`Figure 3 of Wang (Highlighted)
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`25
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`Page 25 of 30
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`55.
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`I understand that Petitioner identifies the state features shown in Table 1 for
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`“walking” as the claimed “first pattern” and the state features shown in Table 1 for
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`“vehicle” and “working” as two alternative claimed “second patterns.” See Pet. at
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`18, 31, 35. In each situation, based on the definitions of those states, it is not
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`possible for the identified “first pattern” to overlap in time with the identified
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`“second pattern.” This is because Table 1 defines each of these three patterns
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`using the “State Feature” “Motion” having a different value—“Moving Slowly”
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`for “Walking,” “Moving Fast” for “Vehicle,” and “Still” for “Working.” Because
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`“Motion” cannot have multiple values simultaneously, these three states cannot
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`overlap in time.
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`56. The fact that the states Petitioner asserts are associated with the “first
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`pattern” and “second pattern” cannot overlap in time is also apparent from Figure 2
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`of Wang, which shows a “sequential flow chart.” As shown in Figure 2, below, to
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`reach the “Vehicle” state, a transition from the “Walking” state, i.e, “Walking” is
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`no longer true, is detected. Addressing Figure 2, Wang explains: “If a significant
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`amount of increase is found on both user speed and recent distance of travel, a state
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`transition will happen and the user will be considered riding a vehicle. id. at 5.
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`26
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`Page 26 of 30
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`Id. at 5.
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`57. As a result of this lack of overlap, even if EEMSS used patterns of state
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`features to detect states (which it does not), EEMSS could never detect what
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`Petitioner identifies as a “second pattern” in data where what Petitioner identifies
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`as the “first pattern” is true.
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`ii. Wang does not disclose “identifying a first pattern” or
`“associating … with said first pattern”
`independent challenged claim (Claims 1, 21, or 46) recites
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`58. Each
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`“identify[ing] a first pattern based, at least in part, on said at least one detected
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`condition.” Petitioner asserts that the claimed “first pattern” corresponds in Wang
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`to a collection of “state features” and corresponding values as recited in Table 1 of
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`Wang: “‘Location’ = ‘Keep on changing’
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