`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`LIVE NATION ENTERTAINMENT, INC.,
`Petitioner
`v.
`SONGKICK.COM B.V.,
`Patent Owner
`____________
`
`Case PGR2017-00038
`Patent 9,466,035
`____________
`
`
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`PATENT OWNER’S PRELIMINARY RESPONSE
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`37 C.F.R. § 42.207
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`PGR2017-00038
`Patent 9,466,035
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`Preliminary Response
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`TABLE OF CONTENTS
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`I.
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`INTRODUCTION .................................................................................. 1
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`II. FACTUAL BACKGROUND ................................................................. 2
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`A. Economic Context of Electronic Event-Ticket Sales .......................... 2
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`B. Developments in Anti-Bot Technology ............................................... 7
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`1. Compaq Computer’s U.S. Patent No. 6,195,698, filed in 1998 ....... 8
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`2. Reshef et al. U.S. Patent Publication No. 2005/0114705, first filed
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`in 1997
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`9
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`C. Recent Developments in Electronic Event-Ticket Sales ................... 11
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`1. McEwen U.S. Patent Publication No. 2016/0078370 .................... 11
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`2. Scarborough U.S. Patent Publ’n. No. 2015/0066546 ..................... 15
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`3. Shivakumar U.S. Patent Publ’n. No. 2015/0134371 ...................... 21
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`III. THE PATENTED INVENTION .......................................................... 23
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`A. Overview of the ’035 Patent .............................................................. 23
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`B. Claims of the ’035 Patent ................................................................... 26
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`IV. PROSECUTION HISTORY OF THE ’035 PATENT AND ’811
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`PATENT 27
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`A. Allowance of ’035 Patent .................................................................. 27
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`B. Similarity between Claims of ’035 Patent and ’811 Patent ............... 29
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`C. Third-Party Submission during Prosecution of ’811 Patent .............. 31
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`D. Examiner Interview during Prosecution of ’811 Patent .................... 32
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`E. Examiner’s Reasons for Allowing ’811 Patent ................................. 33
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`V. ALL GROUNDS SHOULD BE DENIED UNDER SECTION 325(d)
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`35
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`A. Section 325(d) Applies to Third-Party Submissions Previously
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`Presented to the Office in Related Applications .................................................. 36
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`B. Section 101 Ground Based on FairWarning Was Previously
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`Presented 37
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`C. Section 103 Ground Based on Scarborough Was Previously
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`Presented 41
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`VI. PETITION SHOULD BE DENIED UNDER SECTION 324(a) ......... 43
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`A. Claim Construction ............................................................................ 43
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`1. “receiver” ........................................................................................ 43
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`2. “login” ............................................................................................. 43
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`3. “tagging…” and “linking…” .......................................................... 44
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`4. “real-time” ...................................................................................... 44
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`5. “flag” ............................................................................................... 44
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`B. Ground 1: Patent Eligibility of Claims 1-19 under FairWarning .... 44
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`C. Ground 2: Obviousness of Claims 1, 4-9, 12-16, and 19 based on
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`Scarborough and McEwen ................................................................................... 47
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`1. Claims 1, 4-9, 12-16 Implicitly Require Two Partitions ................ 47
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`2. Scarborough Does Not Teach the ’035 Patent’s Two Partitions .... 48
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`3. McEwen Does Not Teach the ‘035 Patent’s Two Partitions .......... 52
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`4. A Combination of Scarborough and McEwen Does Not Teach the
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`’035 Patent’s Two Partitions ............................................................................ 54
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`D. Ground 3: Obviousness of Claims 2, 3, 10, 11, 17, and 18 based on
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`Scarborough, McEwen, and Shivakumar ............................................................ 55
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`VII. CONCLUSION .................................................................................. 56
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`Ex. No.
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`EXHIBIT LIST
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`
`
`Description
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`2001
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`2002
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`2003
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`2004
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`2005
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`2006
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`2007
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`2008
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`2009
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`U.S. Patent No. 9,639,811 (the “’811 patent”)
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`Prosecution History of U.S. Patent App. No. 15/099,750, which
`issued as the ’811 patent
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`U.S. Patent No. 8,578,500 (“the ’500 patent”)
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`Dean Budnick & Josh Baron, Ticket Masters: The Rise of the
`Concert Industry and How the Public Got Scalped (2011), p. 229.
`
`Federal Bureau of Investigation press release: Three Plead Guilty in
`“Wiseguys” Scheme to Purchase 1.5 Million Premium Tickets to
`Events Through Computer Hacking and Fraud (2012),
`https://archives.fbi.gov/archives/newark/press-
`releases/2010/nk111810.htm (last visited October 17, 2017).
`
`United States of America v. Kenneth Lowson, et al., 2:10-cr-00114-
`KSH, D.N.J, Indictment, filed February 23, 2010.
`
`Jason Koebler, The Man Who Broke Ticketmaster (Feb. 2017), Vice
`Magazine, available at
`https://motherboard.vice.com/en_us/article/mgxqb8/the-man-who-
`broke-ticketmaster (retrieved October 17, 2017).
`
`Kim Zetter, Wiseguys Plead Guilty in Ticketmaster Captcha Case
`(November 19, 2010), WIRED,
`https://www.wired.com/2010/11/wiseguys-plead-guilty/(retrieved
`October 17, 2017).
`Hayley Tuskayama, The Switch: The Surprising Tools Bots Use To
`Get Around Those Pesky Captchas (June 9, 2016), The Washington
`Post, available at https://www.washingtonpost.com/news/the-
`switch/wp/2016/06/09/the-surprising-tool-bots-use-to-get-around-
`those-pesky-captchas/?utm_term=.69e1ac980514 (retrieved October
`17, 2017
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`2010
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`2011
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`2012
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`2013
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`2014
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`2015
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`2016
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`Biz Carson, Meet the Startup That Kept Scalpers From Buying Up
`All the Tickets to Adele’s Sold-Out Show (December 20, 2015),
`BUSINESS INSIDER, http://www.businessinsider.com/songkick-
`helps-adele-sell-tickets-2015-12 (retrieved October 17, 2017).
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`Talk of the Town, December 16, 2015 blog entry,
`http://talktown.blog.myajc.com/2015/12/16/5-things-to-remember-
`when-buying-adele-tickets/, (retrieved October 17, 2017).
`
`Andrew Flanagan, Ticketmaster's President Delivered a Morale-
`Boosting Memo in the Wake of Adele Disappointments (December
`18, 2015), BILLBOARD,
`http://www.billboard.com/articles/business/6813819/ticketmaster-
`ceo-memo-adele (retrieved October 17, 2017).
`
`Adrienne Green, Adele Versus the Scalpers (December 21, 2015),
`The Atlantic,
`https://www.theatlantic.com/business/archive/2015/12/adele-
`scalpers/421362/ (retrieved October 17, 2017).
`
`Stuart Dredge, Despite Privacy Scare, Adele Smashes Secondary
`Ticket Market (December 2, 2015), MUSIC ALLY,
`http://musically.com/2015/12/02/despite-privacy-scare-adele-
`smashes-secondary-ticketing-market/ (retrieved October 17, 2017).
`
`Record of the Day press release: Music Industry Innovation Saves
`UK Adele Fans £4.2m (2015),
`https://www.recordoftheday.com/news-and-press/music-industry-
`innovation-saves-uk-adele-fans-42m (last visited October 17, 2017).
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`Record of the Day editorial: Adele and Songkick’s partnership
`appears to have produced remarkable success in reducing secondary
`ticketing, David Balfour suggests. We’re not remotely surprised
`therefore that some want to portray it as a failure (2015),
`https://www.recordoftheday.com/news-and-press/adele-songkick-
`produced-remarkable-success-or-failure (retrieved October 17,
`2017).
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`2017
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`CAPTCHA, Wikipedia, the free encyclopedia,
`https://en.wikipedia.org/wiki/CAPTCHA#cite_note-:0-3 (retrieved
`on October 17, 2017).
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`I.
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`INTRODUCTION
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`Songkick.com B.V. (“Patent Owner”) files this preliminary response to the
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`post-grant review petition (Paper 1) filed by Live Nation Entertainment, Inc.
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`(“Petitioner”) against U.S. Patent No. 9,466,035 (“the ’035 patent”). For reasons
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`explained herein, the petition should be denied under 35 U.S.C. § 325(d) as relying
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`on the “same or substantially the same prior art or arguments previously …
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`presented to the Office,” in a 41-page Third-Party Submission during prosecution
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`of the ’035 patent’s direct child application (Ex. 2002 pages 57-97), or
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`alternatively denied under 35 U.S.C. § 324(a) as failing to meet the threshold for
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`institution for substantially the same reasons the Examiner gave in an extensive,
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`12-page Notice of Allowability explaining in detail why the Third Party’s proposed
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`§ 101 rejection based on FairWarning and proposed § 103 rejection based on
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`Scarborough were not adopted. (Ex. 2002 pages 8-23.)
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`In its petition, Petitioner does not even acknowledge the previous Third-
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`Party Submission (which contains language carried-over verbatim into the
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`petition), much less does Petitioner specifically point out any errors in the
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`Examiner’s Notice of Allowability. Petitioner, therefore, has failed to meet its
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`“initial burden” with respect to previously-considered art and arguments under
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`§ 325(d), such that the Examiner’s findings may be “treated as undisputed fact,”
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`according to an expanded panel of the Board (that included the Chief Judge). See
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`Ziegmann v. Stephens, IPR2015-01860, Paper 13, at slip op. 10 (PTAB Sept. 6,
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`2017) (Ruschke, Boalick, Kim, DeFranco, Mayberry, JJ.) (“[W]hen a prior art
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`reference presented in a petition was already considered substantively by the
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`Examiner, the petitioner has the initial burden to identify such errors made by the
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`examiner with respect to that prior art reference. Any factual finding made by the
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`examiner and not contested by the petitioner may be treated as undisputed fact for
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`the purposes of evaluating petitioner’s assertions concerning examiner error under
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`Section 325(d).”) (citations omitted) (emphases added).
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`II. FACTUAL BACKGROUND
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`A. Economic Context of Electronic Event-Ticket Sales
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`Ticketweb sold the first ticket via the internet in 1995. The following year,
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`Ticketmaster sold their first ticket online. As Ticketmaster’s SVP of New Media,
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`Alan Citron, said of the time, “There was this joke that would go around the office
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`that you don’t want to find out that, for the first on-sale that occurs on the internet,
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`some kid named Skippy has gotten hold of all the tickets. There was always the
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`threat hanging in the air. So we had to make sure this thing could not be hacked.”
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`(Ex. 2004 para 2.)
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`Evidence of how brokers would go on to take advantage of Ticketmaster’s
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`online ticket sales can be found in the aftermath of a federal indictment filed
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`against Wiseguys Tickets in 2010. The Wiseguys’ business model was to
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`“wholesale” the tickets they procured to a vast network of brokers. The brokers
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`were willing to pay Wiseguys Tickets more than the face value of the tickets,
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`because the tickets could be resold to the public at even greater prices. (Ex. 2005
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`para 7.)
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`As the indictment notes, “Because the Event Agreements required Online
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`Ticket Vendors to sell tickets on a first-come, first-served basis, Online Ticket
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`Vendors invested millions of dollars in technology that created virtual ‘queues’ to
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`serve would-be Internet purchasers in the order they arrived. On these systems,
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`how quickly an on-line purchaser reached the virtual queue would determine which
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`tickets that user could purchase, or whether the user could purchase tickets at all.”
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`(Ex. 2006 §1:i.)
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`Early on, Wiseguys became aware that the webpage at which to purchase
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`tickets for a given event would load in staggered fashion based on geography,
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`meaning that the sale for an event would start for customers at slightly different
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`times depending on from where they were trying to connect. The difference in start
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`times was only one of several seconds, but those critical few seconds was all it
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`took to give Wiseguys an outsized advantage. The company rented 30 servers
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`spread across the country in order to exploit this loophole, guaranteeing that at
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`least a portion of their connections would consistently occur within the first
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`seconds of the onsale. (Ex. 2007 pages 3-7.)
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`By the early 2000s, Ticketmaster and other online ticketing providers were
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`regularly implementing CAPTCHA (“Completely Automated Public Turing test to
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`tell Computers and Humans Apart”) in an attempt to reduce the increasingly
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`aggressive efforts of scalpers. CAPTCHA typically asked the customer to correctly
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`transcribe the letters of a word displayed on screen in a distorted image before
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`being allowed to search for tickets. The belief was that while CAPTCHA would
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`make it slower for customers to purchase their tickets, it would prevent the
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`automated programs that were becoming increasingly common from speeding
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`through the purchase process vacuuming up large quantities of tickets before
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`customers had a chance. Automated programs, or bots, could be programmed to
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`follow all of the standard steps necessary to search for and purchase tickets much
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`faster than a human user ever could hope to, enabling scalpers to harvest huge
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`amounts of tickets with minimal investment. (Ex. 2008.)
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`However, as CAPTCHA technology advanced, so did scalpers’ technology.
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`Pretty soon scalpers began using a technology called Optical Character
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`Recognition (“OCR”) to overcome more complex instances of CAPTCHA. OCR
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`converts images of typed, handwritten or printed text into machine-encoded text.
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`This technology was then connected to bots that could request thousands of tickets
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`within a minute, regardless of how many tens of thousands or millions of distorted
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`images were in a ticketing company’s CAPTCHA library.
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`Human intervention also helped defeat CAPTCHA. For example, “the Bots
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`transmit in real-time images of the CAPTCHAs they encounter on Ticketmaster
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`and other sites to armies of ‘typers,’ human workers in foreign countries where
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`labor is less expensive. These typers—employed by companies such as Death by
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`CAPTCHA, Image Typerz, and DeCaptcher—read the CAPTCHAs in real-time
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`and type the security phrases into a text box for the Bot to use to bypass ticket
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`vendors’ defenses and use their sites.” (Ex. 2009)
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`And, while all bots are undesirable purchasers, not all undesirable
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`purchasers are bots, and therefore undesirable purchasers continued to be
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`problematic.
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`Patent Owner’s unique solution, which was conceived after its experience
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`working with scores of frustrated clients, including Paul McCartney, Ed Sheeran,
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`Red Hot Chili Peppers, Lady Antebellum, Eric Church, Jack Johnson and Keith
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`Urban, addressed both human and non-human undesirable purchasers.
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`Patent Owner launched its own solution in November of 2015 for Adele,
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`whose last album was the fastest selling album in recorded music history. After a
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`five-year hiatus from performing in concert and innumerable sales records broken,
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`Adele returned to the stage. It was clear that despite the large number of concert
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`dates to which she committed, there was no way she would be able to satiate
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`demand. At the same time, she wanted to keep ticket prices reasonable, in order to
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`not alienate her broad fan base. (Ex. 2010. ; Ex. 2011.)
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`And, demand there was. Ticketmaster’s President, North America, reported
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`that Adele’s ticket sales generated “easily an all-time record” in terms of traffic,
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`with “10 million+ fans rushing to our site”. (Ex. 2012.) As well, many millions
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`purchased tickets to her shows across Europe and in her home country of Britain,
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`where Patent Owner handled the majority of ticket sales for Adele. Due to Patent
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`Owner’s new technology, tickets to Adele’s tour went overwhelmingly to the fans
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`that had waited years for her to return to the stage, not to scalpers. The Atlantic
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`reported that, “as a result of [Patent Owner’s] efforts, more Adele fans were able to
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`buy their tickets at normal prices instead of the exorbitant fees scalpers (or “touts”
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`as British people call them) charge.” (Ex. 2013.) Trade publication, MusicAlly,
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`surveyed the amount of tickets on the re-sale market for Adele versus other high-
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`profile acts whose sales occurred in the same period, reporting that the “figures
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`were startling”: “the average number of secondary tickets per Coldplay gig was
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`2,939, compared to 1,548 for Rihanna and just 54 for Adele.” (Ex. 2014.) Chris
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`Carey, the CEO of Media Insight, a consulting firm, found that Patent Owner’s
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`“music industry innovation saves UK Adele Fans £4.2m.” (Ex. 2015.) Prestigious
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`UK music business daily, Record of the Day, wrote:
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`“We’d like to see some greater transparency/detail released by [Patent
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`Owner] around this ‘tout-finding’ process. This is not because we question the
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`company’s morals or methodologies. It’s more because [Patent Owner] has just
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`done more in one campaign to address the issue of touting than has been achieved
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`to date by any other party in any other sector. Further detail would be eagerly
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`received.” (Ex. 2016.)
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`Adele would go on to perform more than 100 shows, and add four farewell
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`shows at Wembley Stadium in her hometown of London, as a gift to fans. In all,
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`Patent Owner sold nearly 500,000 tickets to Adele’s fans, and saw approximately
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`only 2% of these tickets only appear on the re-sale market. Seeing the impact of
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`Patent Owner’s patented technology, other artists, including Katy Perry and Jack
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`Johnson began to use it for their ticketing.
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`B. Developments in Anti-Bot Technology
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`Internet users have long wanted to prevent computers from “reading” text. A
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`primitive technique was to replace a word with look-alike characters. HELLO
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`could become |‐|3|_|_() or )‐(3££0, as well as numerous other variants.
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`One of the earliest commercial uses of CAPTCHAs was in the Gausebeck-
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`Levchin test. In 2000, idrive.com began to use CAPTCHA in connection with a
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`signup page. In 2001, PayPal used such tests as part of a fraud prevention strategy
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`in which they asked humans to "retype distorted text that programs have difficulty
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`recognizing." [Ex. 2017 (internal citations omitted).]
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`1. Compaq Computer’s U.S. Patent No. 6,195,698, filed in 1998
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`Compaq detected bots by presenting to a would-be user a “riddle” derived
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`from a distorted and camouflaged text string on the hypothesis that only a human
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`user could identify the source text. Compaq’s FIG. 4. showing a riddle, is
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`reproduced below, left.
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`Compaq’s Abstract states, in
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`part:
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`The string is randomly
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`modified either visually or audibly
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`to form a riddle. The original string
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`becomes the correct answer to the
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`riddle…Hopefully, the answer is a user's guess for the correct answer. The server
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`determines if the guess is the correct answer, and if so, the access request is
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`accepted. If the correct answer is not received within a predetermined amount of
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`time, the connection…is terminated…on the assumption that an automated agent is
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`operating…on behalf of the user.”
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`2. Reshef et al. U.S. Patent Publication No. 2005/0114705, first
`filed in 1997
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`Reshef detects bots using its method for discriminating automatic
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`computerized action from a human performed action. Reshef, Abstract.
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`The method applies human advantage in applying sensory and cognitive
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`skills to solving simple problems that prove to be extremely hard for computer
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`software. Such skills include, but are not limited to processing of sensory
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`information such as identification of objects and letters within a noisy graphical
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`environment…The method for discriminating between humans and computerized
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`actions can be used during authentication, to limit access by automated agents, and
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`for confirmation of actions. Id.
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`Reshef FIG. 8a, below left, shows a “prior art” login web page that has only
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`standard fields for entry of user name and PIN number. Reshef FIG. 8b, below
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`right, shows the prior art login web page
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`with the addition of “human ability
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`challenge 506.” Reshef ¶ 70. Reshef
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`states, “[i]n order for a user to gain access
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`to a particular computer resource on
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`application server 100, the user must
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`provide a valid username, PIN# and an
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`answer to human ability challenge 506. The proxy program on proxy server 106
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`verifies that the answer provided in prompt 50S to human ability challenge 506 is
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`correct. If the answer is verified, the proxy program allows access for client 102 to
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`application server 100.” Id.
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`C. Recent Developments in Electronic Event-Ticket Sales
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`1. McEwen U.S. Patent Publication No. 2016/0078370
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`McEwen adopted CAPTCHA technology for identifying bot-based ticket
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`purchase attempts in an electronic event-ticketing system. McEwen FIG. 1,
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`reproduced at left, emphasis added,
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`shows an overview of McEwen ticket
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`interaction system 100, in which user
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`105 requests tickets for an event that
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`was established in ticket management
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`system 150 by event-provider (a
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`promoter or producer) 115. Ticket management system 150 is vulnerable to bot
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`scalpers via internet 130. System 150 can store a user’s request in a “queue,” for
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`that event, in queue database 180.
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`McEwen provides users with an opportunity to register for the event.
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`Registration requires completion of verification steps that are useful for
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`discriminating between human and robot users.
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`A user that passes the verification steps can then submit a ticket request prior
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`to an onsale period. Para 4. Bots are less likely to pass the verification steps, and
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`thus are less likely to be granted an opportunity to submit a ticket request prior to
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`the onsale period (a “pre-onsale request”). This handicaps a bot user, who must
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`not only wait until the actual onsale period, but also must still overcome the
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`verification hurdle. See, e.g., McEwen at para 4.
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`McEwen discloses that there can be multiple onsale periods. Each period
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`can be open to users having different characteristics. Exemplar characteristics
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`include “users having a credit card of a particular brand”, and the “general public.”
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`Para 100.
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`McEwen also can score requests based
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`on a user’s performance during the
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`verification steps. Para 5. McEwen ticket
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`requestor 230 (McEwen FIG. 2, right,
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`emphasis added), which is in direct
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`communication with CAPTCHA Engine 235,
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`and can generate the score. A score above a
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`threshold can indicate that registration is
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`complete. The score can be used for
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`“prioritization” of one or more user ticket requests. Para 76. (Elsewhere, in para
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`55, McEwen states that it is queue engine 245 that generates a score.)
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`McEwen discloses a long list (below, emphasis added) of techniques that
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`can be used for generating a score:
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`In connection with the list, McEwen states that “the score can optionally
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`provide more gradations of performance than a simple pass/fail indication…” For
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`example, the score may be valued at one of at least three possible values, such as:
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`“A,” “B,” or “C.” McEwen’s sole strictly ternary example of a “score” is: “likely a
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`robot, likely not a robot, and indeterminate as to whether the user is a robot or not.”
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`para 107.
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`Among McEwen’s items for “generating a score” are:
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`(1) Algorithm to rank users into multiple (e.g., 3) buckets; and
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`(2) Algorithm to order users within each bucket.
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`McEwen does not disclose how ranking into multiple buckets can be used to
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`generate a score. McEwen does not disclose how ordering users within each
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`bucket can be used to generate a score. McEwen does not disclose what a
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`“bucket” is or what a “bucket” does.
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`McEwen does disclose that different queues may be established based on a
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`user’s pricing “preferences or restrictions,” and on a user’s seating “preferences or
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`restrictions.” para 89.
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`Queue engine 245 can prioritize already-approved requests. The requests
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`can be ranked relative to each other. McEwen para 55.
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`The requests can be placed in “prioritization groups” by comparing its score
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`to one or more thresholds. Requests such as those associated with pre-onsale
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`Preliminary Response
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`registration, stronger estimation of being human, stronger estimation of not being a
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`threat, and reference to an event on a user social-network site can be assigned to
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`“higher priority” groups. Para 55.
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`Queue engine 245 can prioritize the requests based on their scores, and can
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`assign a request to a group in a queue. In “one instance,” requests having scores
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`below a threshold are not even added to the queue. Para 110.
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`McEwen states that “In some instances, all pending pre-onsale requests are
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`prioritized over all pending onsale requests within a given queue.” Para 110.
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`2. Scarborough U.S. Patent Publ’n. No. 2015/0066546
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`Scarborough, which is also owned by the Petitioner, discloses a “learning
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`model” that provides an additional level of power to segregate bot-based ticket
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`purchase attempts in an electronic event-ticketing system. Scarborough discloses
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`heuristically changing the criteria by which a user (an “actor,” in Scarborough’s
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`nomenclature) is determined to be a “good actor.” Scarborough Abstract. Because
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`the model is “dynamic,” Scarborough, Title, the model “can make it difficult for
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`actors to understand what attributes are being currently favored.” Scarborough
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`para 5. Good actors may be provided with preferential opportunities to purchase
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`tickets. Scarborough para 5.
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`Scarborough FIG. 1, reproduced at
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`left, emphasis added, shows an overview
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`of Scarborough ticket interaction system
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`100, in which actors 105 (-1 and -2)
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`request tickets for an event that was
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`established in ticket management system
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`150. Ticket management system 150 is vulnerable to bot scalpers via internet 140.
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`Scarborough FIG. 2, at right, emphasis
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`added, shows details of Scarborough ticket
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`management system 150.
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`Scarborough ticket management system
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`150 communicates with the actor via client
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`interface engine 205.
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`Model engine 215 evaluates attributes
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`of each actor. Scarborough para 43. The
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`attributes can include, for example,
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`membership in a group, speed of responding to presentations, scar para 44, whether
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`the actor positively responded to electronic presentation associated with the event
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`(a Facebook “like”), Scarborough para 45, and whether an actor attended a specific
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`event, Scarborough para 45.
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`Model engine 215 includes a learning algorithm that can detect when actors
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`“seem to be altering their activity in an attempt to cause system 150 to falsely
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`characterize” actors. If so, the learning algorithm can “initiate an adjustment in the
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`characterization-assessment procedure, such that the procedure evaluates different
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`attributes or changes weights of attributes” to properly characterize bots that learn
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`to behave more like humans. Scarborough para 55.
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`Scarborough good-actor detector 225 (shaded green) operates on output of
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`model engine 215 to provide a positive estimation that an actor is a good actor.
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`Scarborough para 56. The estimation may be a score that “reflects an estimate of
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`an extent to which an actor possesses [a] characteristic or a confidence of the
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`characteristic assessment made by good-actor detector 225.” Scarborough para 58.
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`Ticket data store 240 keeps track of ticket attributes, such as availability,
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`“on-reserve,” “on hold,” “assigned,” “purchased,” and “restricted.” Scarborough
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`para 62.
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`Pricing engine 230 “sets a price for each ticket” in data store 240.
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`Scarborough para 60. “Each ticket may be associated with one or more prices
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`(e.g., a normal price, a good-actor price and/or a bad-actor price; or a scale of
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`prices along which a price is to be selected based on an actor characterization
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`score)…” Scarborough para 63.
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`Pricing engine 230 does not determine good-actor status, but can “identify
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`variations to the price” based on a good-actor status previously determined by
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`good-actor detector 225. Scarborough para 60.
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`Ticket engine 235 manages, updates and monitors ticket data store 240.
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`Scarborough para 62. Scarborough FIG. 4, entirely reproduced, below right,
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`emphasis added, shows process 400 for
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`“biasing ticket offerings to favor good
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`actors.” Scarborough para 111.
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`In step 405 (“Define actor
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`characteristic”), actor characteristic
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`definer 210 (shown in Scarborough
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`FIG. 2) defines an actor characteristic
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`(such as “true fan,” “likely to attend an
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`event,” and “estimated to be a human
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`and not a robot”). Scarborough para
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`111.
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`In step 410 (“Determine characterization-assessment procedure”), model
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`engine (shown in Scarborough FIG. 2) selects a test for the presence of the defined
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`actor characteristic. The selection can be made to “make it difficult for actors to
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`guess the attributes being considered.” Scarborough para 112.
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`Preliminary Response
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`Steps 415 (“Determine value for each attribute for actor”) and 420 (“Perform
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`procedure”), and decision 425 (“Actor a good actor?”), are shaded green to show
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`that they are performed by good-actor detector 225, which is shaded green in
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`Scarborough FIG. 2.
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`At step 415, good-actor detector provides a value, whether binary or on a
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`scale, for each attribute that the selected test requires. Scarborough para 113.
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`At step 420, good-actor detector 225 performs the selected test. The outcome
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`of the test may be a score that relates to the “performance” of the actor under test.
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`Scarborough para 114. Based on the score, good-actor detector 225, at decision
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`block 420, “estimates whether the actor is a good actor.” Good-actor detector 225
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`may decide based on whether the score “exceeds a threshold in a good-actor
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`direction.” Scarborough para 115.
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`If good-actor detector 225 does not issue a good actor determination, ticket
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`engine subsequently proceeds with normal protocols at step 430a (“Proceed with
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`normal protocol”). “Normal protocols” include offering tickets in a “normal
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`order,” in a “normal fashion,” at “normal prices” and conforming to a “normal
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`selection of tickets.” Scarborough para 116.1
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`1 The Petitioner identified a typographical error in Scarborough para 116,
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`first sentence, and proposed to cure the error by adding the “not” between “is” and
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`Preliminary Response
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`If good-actor detector 225 does issue a good-actor determination, ticket
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`engine 235 proceeds with biasing step 430b (“Bias ticket offerings to favor actor”).
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`In the biasing, ticket engine 235 provides a bias—relative to the normal
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`protocol. The bias may be an offer of tickets that are not available to other actors,
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`“a good actor,” so that the sentence would read, “Upon estimating that actor is not
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`a good actor, ticket engine 235 proceeds with normal protocols at block 430a.”
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`Petitioner’s petition at 47. Patent Owner suggests that Scarborough’s teachings
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`about good-actor detector 225 compel a different cure: viz., the addition of “not”
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`between “upon” and “estimating,” so that the sentence would read, “Upon not
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`estimating that actor is a good actor, ticket engine 235 proceeds with normal
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`protocols
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