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`EXHIBIT A
`
`Valtrus Innovations Ltd.'s Proposed Constructions and Intrinsic and Extrinsic Evidence
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`EXHIBIT A- Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`I. U.S. Patent No. 6,728,704
`
`Patent Claim(s)
`
`Term
`
`Valtrus's
`Proposed
`Construction
`
`'704
`
`1, 12
`
`Orde1ing of
`the method
`steps
`
`The steps do not all
`need to be
`perfo1med in order.
`
`'704
`
`1, 12
`
`sco1ing
`value(s)
`
`'704
`
`1, 12
`
`search
`engine(s)
`
`Plain and ordina1y
`meaning to a
`person of ordinaiy
`skill in the rut in
`light of the
`specification.
`
`A computer
`program designed
`to seek out
`info1mation based
`on a que1y from a
`user via a Web
`browser.
`
`Examples of Intrinsic Evidence
`
`Examples of Extrinsic Evidence
`
`"Search Engine." The New Oxford
`'704 Patent at 1:38-41 : "Seai·ch engines
`are computer programs designed to seek American Dictiona1y 1529 (Erin
`McKean ed., 2nd ed. 2005). V ALTRUS-
`out inf01mation based on instrnctions
`from the user. Typically, the user enters GOOGLE-NDTX-00007 588-00007 606.
`a set of instrnctions, often called a
`que1y, which instrncts the seai·ch engine
`to search for specific types of
`info1mation."
`
`"Search Engine." Steven M. Kaplan,
`Wiley Electiical and Electronics
`Enginee1ing Dictionaiy 687-88 (2004).
`V ALTRUS-GOOGLE-NDTX-
`00007435-00007455.
`
`'704 Patent at 1:21-31 : "Seai·ches for
`info1mation in the networked computer
`environment may be cumbersome due to "Seai·ch Engine." Hany Newton,
`the sheer amount of info1mation stored, Newton's Telecom Dictionaiy 822 (24th
`or due to the complexity of finding
`ed. 2008). V ALTRUS-GOOGLE-
`info1mation in large file strnctures.
`NDTX-00007 499-00007 516.
`Indeed, with the advent of the World
`Wide Web (WWW) as well as other
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`Tao Yang and Apostolos Gerasoulis,
`Web Search Engines: Practice and
`Experience, 2 in Computing Handbook
`(3rd ed. 2014). VALTRUS-GOOGLE-
`NDTX-00007182-00007202.
`
`Julia Kerr, What is a Search Engine? The
`Simple Question the Court of Justice of
`the European Union Forgot to Ask and
`What It Means for the Future of the
`Right to be Forgotten, 17 CHI. J. INT'L L.
`217, 220 (2016). VALTRUS-GOOGLE-
`NDTX-00007342-00007369.
`
`Sergey Brin and Lawrence Page, The
`Anatomy of a Large-Scale Hypertextual
`Web Search Engine (1998). VALTRUS-
`GOOGLE-NDTX-00007266-00007285.
`
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`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`forms of computer networking, and the
`corresponding explosion in the amount
`of information available, it is now
`simply impractical for users to search for
`information manually. The ability of
`search engines to analyze enormous
`amounts of data and isolate useful
`information thus becomes of paramount
`importance."
`
`'704 Patent at 1:52-2:33: "Almost all
`search engines work in this general
`manner. However, their architectures
`vary according to the context in which
`they operate. Search engines are
`currently constructed in at least three
`architectures: federated, peer-to-peer,
`and meta-search engines. Each is used to
`conduct different types of searches.
`Federated search engines are used in the
`client-server environment. A client or
`server may initiate a search for data
`located in various networked servers.
`Federated search engines are most
`commonly used in the WWW context,
`but need not be limited in this manner.
`Typical federated engines search the
`WWW by utilizing programs called bots
`or spiders to examine the content of
`information available on other
`computers and build an index consisting
`of the words or other data stored in these
`computers, as well as where they are
`located. Once users enter a query
`
`Shaon Tewari, How Search Engine
`Works, 2 INT'L J. OF RES. IN
`ENGINEERING, SCIENCE &
`MANAGEMENT 92, 92 (2019).
`VALTRUS-GOOGLE-NDTX-
`00007293-00007301.
`
`F. Anklesaria et al., The Internet Gopher
`Protocol (a distributed document search
`and retrieval protocol) 10 (1993).
`VALTRUS-GOOGLE-NDTX-
`00007902-00007917.
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`Wei Tang, Search Engine Survey 1
`(1999). VALTRUS-GOOGLE-NDTX-
`00008456-00008464.
`
`Jeffrey R. Bach et al., Virage image
`search engine: an open framework for
`image management (1996). VALTRUS-
`GOOGLE-NDTX-00008465-00008477.
`
`Odej Kao and Gerhard R. Joubert, A
`content based Internet search engine for
`analysis and archival of MPEG-1
`compressed newsfeeds (2002).
`VALTRUS-GOOGLE-NDTX-
`00008491-00008494.
`
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`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`consisting of words or data desired, the
`search engine searches its index for any
`locations that contain these words/data
`and returns a list of such locations. The
`result list returned is normally a list of
`each such returned location and any
`associated information, and may include
`Uniform Resource Locators (URLs) for
`finding WWW-based data, or other
`expressions of data location. The results
`or entries in this list are often ranked
`according to any of a number of criteria
`currently available, with the goal of
`presenting the most relevant results to
`the user first.
`
`One flaw in this type of search engine is
`the potential for inaccurate information.
`Because the WWW is so large, indices
`are updated only sporadically, meaning
`searches may not uncover the most
`recent information. Other types of search
`engines avoid the need for spiders and
`indices, and thus present users with up-
`to-date information more often. One
`example is the peer-to-peer search
`engine, which can also be used for other
`networks besides the WWW. These
`search engines operate in the peer-to-
`peer environment, where computers are
`simply linked together with no
`centralized servers and no distinct
`clients. They typically work by
`distributing a search to various peer
`
`Chee-Hong Chan et al., Automated
`Online News Classification with
`Personalization 7, in 4th International
`Conference on Asian Digital Libraries
`(2001). VALTRUS-GOOGLE-NDTX-
`00008445-00008455.
`
`
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`
`computers, each of which can in turn
`farm out the search to other computers
`in the same network. In this way,
`individual computers search only the
`current contents of a few peers and not
`the entire WWW or other network. This
`eliminates the need to build a large
`index, and delivers to the user a real-
`time snapshot of the content of the
`network or the WWW.
`
`Finally, web meta-search engines can
`operate in either the client-server or
`peer-to-peer environment. These search
`engines typically act as aggregators that
`farm a WWW search out to other public
`web search engines, then process the
`results."
`
`'704 Patent at 3:34-52: "FIG. 2 illustrates
`processing operations in accordance
`with an embodiment of the invention.
`
`FIG. 3 illustrates an example of merging
`multiple result lists into a single list in
`accordance with an embodiment of the
`invention.
`
`Like reference numerals refer to
`corresponding parts throughout the
`several views of the drawings.
`
`FIG. 1 illustrates a generalized computer
`network 5 that may be operated in
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`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`accordance with an embodiment of the
`present invention. This computer
`network 5 may operate in a client-server,
`peer-to-peer, or other configuration, and
`may also be considered a representation
`of the WWW. The network 5 includes at
`least one computer 10 connected by
`transmission channel 20 to a group of
`computers 30 and 50. Transmission
`channel 20 may be any wire or wireless
`transmission channel."
`FIGS. 1, 2, and 3.
`
`'704 Patent at 3:52-65: "Computer 10 is
`a standard computer controlled by a
`Central Processing Unit (CPU) 12 and
`connected to the rest of the computers in
`network 5 by network connection 14.
`Computer 10 also includes a memory 16
`that can be any form of computer-
`readable memory. Memory 16 contains a
`browser program 17 that allows users to
`browse the WWW. The memory 16 may
`also contain a search engine program 18
`and an associated merging program 19
`for merging different result lists,
`however in a client-server configuration
`the search engine is often resident on a
`different computer. The search and
`merging operations may be performed
`on any computer within the network 5."
`
`'704 Patent at 4:20-49: "The present
`invention operates within a network of
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`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`computers such as those shown in FIG.
`1. More specifically, the present
`invention operates by engaging multiple
`search engines to process a query and
`merge the result lists for presentation to
`the user. In a typical client-server
`configuration, a user operating client
`computer 10 sends queries through
`transmission channel 20 to search engine
`40, which is resident on server 30.
`Through the use of spiders or bots
`whose operations are known in the art,
`the search engine 40 typically will have
`already built up a collection of locations
`(which can include URLs), along with
`the data contained in those locations, in
`index 42. For example, the bots would
`have already searched the contents of
`memory 36 of computers configured like
`computer 30. They would have also
`searched the contents of WWW data
`pages 60 and databases 62 of computers
`configured like computer 50. The
`content from these computers would be
`stored in index 42. Search engine 40
`then cross-checks the words or other
`data contained in the query against the
`data contained in index 42 for matches.
`Locations in index 42 containing data
`that matches the query are compiled into
`a result list. Search engine 40 typically
`transmits the same query to other search
`engines resident on computers
`configured like computer 30 and
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`
`
`connected to transmission channel 20.
`These other search engines then perform
`separate searches in the same manner as
`above, compile their own result lists, and
`return these lists to the computer 30 that
`originated the search. The end result of
`the above is a set of result lists that must
`be merged by merging program 44 and
`returned to computer 10 for display to
`the user."
`
`'704 Patent at 5:1-15: "Search engine 18
`then performs two different tasks: it
`searches other computers on the network
`for data satisfying the query, and
`distributes that query to other search
`engines on the network 5. Here, search
`engine 18 searches the contents of
`memory 36 of peer computer 30, as well
`as memory 56 of peer computer 50, for
`data matching the query. A result list
`containing the locations of relevant data
`is then compiled. Search engine 18 also
`farms the same query out to the search
`engine 40 of peer computer 30, which
`conducts a search in similar fashion,
`examining the contents of peer
`computers like computers 30 and 50 and
`compiling the results into a list. Note
`that this process could continue
`recursively, with search engine 40
`farming out the same query to other
`search engines in network 20, which in
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`
`
`turn could farm the search out to other
`search engines, and so on."
`
`'704 Patent Prosecution History at
`VALTRUS-GOOGLE-NDTX-
`00000093-00000097: "Web search
`engines (WSE) use tools ranging from
`simple text-based search to more
`sophisticated methods that attempt to
`understand the intended meanings of
`both queries and data items."
`
`'704 Patent Prosecution History at
`VALTRUS-GOOGLE-NDTX-
`00000098-00000126.
`
`'704 Patent Prosecution History at
`VALTRUS-GOOGLE-NDTX-
`00000158-00000167: "Many sources on
`the Internet and elsewhere rank the
`objects in query results according to how
`well these objects match the original
`query."
`
`U.S. Patent No. 6,006,225, cited on face
`of '704 Patent, at 1:17-20: "With the
`increasing popularity of the Internet and
`the World Wide Web, it is common for
`on-line users to utilize search engines to
`search the Internet for desired
`information."
`
`U.S. Patent No. 6,102,969, cited on face
`of '704 Patent, at 6:43-7:19: "FIG. 2B
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`
`generally illustrates the user display
`from another example of a web-browser-
`based user interface embodiment, which
`in this case, is directed to an information
`domain of WWW indexes or search
`engines. This display is also generally
`divided into three sections. Section 71
`displays a title for the netbot; section 72
`displays the status of the current search;
`and section 73 displays the search
`results. In more detail, the display of
`section 71 includes a logo for this
`netbot, "MC" standing for
`"MetaCrawler," a name chosen since
`WWW search engines are also known as
`"web crawlers," and controls to access
`certain. system level presentation
`features, such the MetaCrawler home
`page and user feedback pages. The
`display of section 72 includes list 74 of
`the search engines being queried
`identified by their common names, the
`status of the current query in general and
`at each search engine, and common user
`controls. Generally, pie-chart icon 78
`summarizes that 7 of the 8 search
`engines queried have already responded
`to the query. At search engine 75,
`known as "Lycos," the check mark
`indicates that a response containing
`information items has already been
`received. At search engine 76, known as
`"Inktomi," the cross mark indicates that
`a response without any information
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`
`items has already been received. On the
`other hand, search engine 77, known as
`"Galaxy," is visibly distinguished from
`the other search engines to indicate that
`it has not yet responded to the query.
`The common controls of section 72
`include more-button 79 to request the
`display of newly arrived search results,
`and modify-search-button 80 to request
`a new or modified query be sent. Lastly,
`the display of section 73 includes the
`information items returned from the
`search engines. Each information item is
`displayed separately and includes title
`81, descriptive text 82 if available, and
`line 83 with the URL of the web page
`for this item and the estimated relevance
`of this item to the query, here
`"1000."The items are sorted for display
`by descending values of the estimated
`relevance. The displayed items are
`scrolled using controls provided by the
`web browser. This user interface is
`implemented as a Java applet
`downloaded from a netbot server and
`executed by the web-browser. In this
`manner, the interface of FIG. 2B is
`capable of greater interactivity than that
`of FIG. 2A. For example, it can poll the
`netbot server for current search status
`and update the status displays
`accordingly without user action."
`
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`U.S. Patent No. 6,327,590, cited on face
`of '704 Patent, at 1:41-63: "For
`individual search engines, there are
`many different techniques for ranking
`results, ranging from counting the
`frequency of the appearance of the
`various search terms in the search query
`to calculating vector similarities between
`a search term vector and each returned
`document vector. In a networked
`environment such as the World Wide
`Web, meta-searchers access different
`and often heterogeneous search engines
`and face the additional difficulty of
`combining the ranking information
`returned by the individual engines.
`Meta-searcher is a Web information
`retrieval system aimed at searching
`answers to a user's query in the
`heterogeneous information providers
`distribute over the Web. When a meta-
`searcher receives responses (usually in
`the form of HTML files) from the
`information providers, a special
`component of a meta-searcher called a
`wrapper, process the responses to
`answer the original query. Since many
`search engines, including meta-
`searchers, hide the mechanism used for
`document ranking, the problem of
`merging search results is compounded.
`A problem common to both individual
`search engines and meta-search engines
`is that these approaches ignore, or
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`knowing nothing about, the user
`conducting the search, or the user's
`context for conducting the search."
`
`U.S. Patent No. 6,546,388, cited on face
`of '704 Patent, at 1:61-2:9: "Users of the
`Web use tools to help find, location or
`navigate through the Web. These tools
`are known as Internet search engines or
`simply search engines. Almost all search
`engines provide graphical user interfaces
`(GUIs) for boolean and other advanced
`search techniques from their private
`catalog or database of Web sites. The
`technology used to build the catalog
`changes from site to site. The use of
`search engines for keyword searches
`over an indexed list of documents is a
`popular solution to the problem of
`finding a small set of relevant
`documents in a large, diverse corpus. On
`the Internet, for example, most search
`engines provide a keyword search
`interface to enable their users to quickly
`scan the vast array of known documents
`on the Web for the handful of
`documents which are most relevant to
`the user's interest."
`
`'704 Patent at 3:1-19: "The invention
`includes the step of transmitting a query
`to a set of search engines. Any result
`lists returned from these search engines
`is received, and a subset of entries from
`
`representative
`value
`
`A value that
`represents the
`scoring values of
`the entries of a list.
`
`"Representative." Collins English
`Dictionary 1372 (7th ed. 2005).
`VALTRUS-GOOGLE-NDTX-
`00007568-00007587.
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`each result list is selected. Each entry in
`this subset is assigned a scoring value
`according to a scoring function, and
`each result list is then assigned a
`representative value according to the
`scoring values assigned to its entries. A
`merged list of entries is produced based
`upon the representative value assigned
`to each result list.
`
`"Representative." The New Oxford
`American Dictionary 1437 (Erin
`McKean ed., 2nd ed. 2005). VALTRUS-
`GOOGLE-NDTX-00007588-00007606.
`
`The invention further includes a
`computer-readable memory to instruct a
`computer to merge multiple result lists
`from search engines. Executable
`instructions stored in the memory
`include instructions for selecting a
`subset of entries from each result list.
`Each entry in the subset is assigned a
`scoring value according to a scoring
`function. Each result list is assigned a
`representative value based on a function
`of scoring values assigned to its entries.
`The entries are then ranked based on the
`representative value assigned to their
`result list."
`
`'704 Patent at 5:65-6:5: "The next
`processing step is to determine, for each
`list, a representative score of all scoring
`values determined for its entries (block
`78). The representative score may be the
`arithmetic average or a value
`proportional to the average for a set of
`scoring values. The present invention
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`includes the step of determining this
`representative score according to any
`number of known techniques."
`
`'704 Patent at 7:10-14: "Result lists 110,
`112, 114 can now be merged into
`merged list 140. As above, this is
`accomplished using the representative
`value assigned to each list. In this
`example, the representative value
`assigned to each list is an average
`scoring value."
`
`'704 Patent at 7:14-34: "In this example,
`the list with the highest average scoring
`value is selected first. In FIG. 3, this is
`result list 112, having an average scoring
`value 132 of 14.95. The first unselected
`entry, 1B, is selected first. Average
`scoring value 132 is then decremented
`by some amount. If that amount is
`chosen to be 1.0, average scoring value
`132 takes on a value of
`14.95−1.0=13.95. Because 13.95 is still
`the highest average scoring value, 2B is
`chosen next and average scoring value
`132 is decremented by another 1.0 to
`take on a value of 12.95. Now, the
`highest average scoring value is value
`134, or 13.225. Entry 1C is thus the next
`entry selected. Scoring value 134 is then
`decremented to 12.225; value 132,
`which is at 12.95, is now the highest
`value again. Entry 3B is thus chosen
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`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`next, and value 132 is decremented to
`11.95. This means value 134 is now the
`highest value. Entry 2C is then chosen,
`and value 134 is decremented to 11.225.
`This means value 132 is again the
`highest value, so entry 4B is selected
`next and value 132 is decremented to
`10.95. Value 130 is now the highest
`value, so entry 1A is chosen and value
`130 is decremented to 11.25−1.0=10.25.
`This process repeats until all entries
`from all three lists are selected."
`
`7:35-54: "According to another
`embodiment, each list 110, 112, 114 is
`assigned a probability value equal to its
`average scoring value's percentage of the
`total of all average scoring values.
`Entries are then selected from each list
`based on its probability value. Here, for
`instance, the total of all average scoring
`values 130, 132, 134 is
`11.25+14.95+13.225=39.425. This
`means result list 110 is assigned a
`probability value equal to
`(11.25/39.425)100%=28.54%. In like
`manner, result list 112 is assigned a
`probability value of
`(14.95/39.425)100%=37.92%, and result
`list 114 is assigned a probability value of
`(13.225/39.425)100%=33.54%. Result
`lists are then selected in pseudorandom
`fashion, where at each selection result
`list 110 has a 28.54% chance of being
`
`11124723
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`
`

`

`
`Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 17 of 147 PageID 1313Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 17 of 147 PageID 1313
`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`picked, list 112 has a 37.92% chance,
`and list 114 has a 33.54% chance. Once
`a list is selected, the first entry that has
`not already been selected is picked.
`Once every entry in a list is selected, the
`total of all average scoring values is
`recalculated without that list's average
`scoring value, and the process continues
`until every entry of every list has been
`selected."
`
`'704 Patent Prosecution History at
`VALTRUS-GOOGLE-NDTX-
`00000098-00000126: "The
`representative of a database indicates
`approximately the contents of the
`database."
`
`'704 Patent Prosecution History,
`VALTRUS-GOOGLE-NDTX-
`00000306-00000315: "In essence, the
`result lists are merged with the goal of
`placing the most relevant entries first for
`the user's convenience. However, to
`reduce the associated computational
`overhead, lists are not merged based on
`an examination of every single entry.
`Rather, they are merged based on an
`examination of only a small number of
`entries from each list. Specifically, there
`is no requirement for examining the
`content of each result item."
`
`
`
`
`'704
`
`1, 12
`
`wherein the
`representative
`
`Plain and ordinary
`meaning to a
`
`11124723
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`
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`017
`
`

`

`
`Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 18 of 147 PageID 1314Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 18 of 147 PageID 1314
`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`
`value varies in
`accordance
`with
`predetermined
`manner /
`wherein said
`representative
`value varies in
`accordance
`with said
`predetermined
`manner
`
`predetermined
`manner
`
`person of ordinary
`skill in the art in
`light of the
`specification.
`
`Not indefinite.
`
`'704
`
`11
`
`
`
`'704 Patent at 5:28-43: "FIG. 2 illustrates
`one embodiment of the processing
`operations according to the present
`invention. In typical operation, a query
`is transmitted to a first search engine,
`which in turn transmits the query to
`other search engines (block 70).
`Eventually, each of these other search
`engines returns a result list that is
`received by the first search engine
`(block 72). The first search engine then
`begins to merge the result lists according
`to the processing steps of the present
`invention. In essence, the result lists are
`merged with the goal of placing the most
`relevant entries first for the user's
`convenience. However, to reduce the
`associated computational overhead, lists
`
`
`1 In the parties' July 5 meet and confer, Google stated that it believes there may be an antecedent basis issue with the last recitation of this
`term in claim 1, raising a separate potential dispute than the predetermined manner term already identified.
`
`11124723
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`- 18 -
`
`
`
`018
`
`

`

`
`Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 19 of 147 PageID 1315Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 19 of 147 PageID 1315
`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`are not merged based on an examination
`of every single entry. Rather, they are
`merged based on an examination of only
`a small number of entries from each list.
`Specifically, there is no requirement for
`examining the content of each result
`item."
`
`'704 Patent Prosecution History at
`VALTRUS-GOOGLE-NDTX-
`00000343-00000346: "Applicant's
`particular system and associated
`methods in the environment of ranking
`multiple subsets for a result list for a
`plurality of search engines is the
`combination of the limitations of
`selecting a subset of entries from each
`result list; determining a scoring value
`for each of the entries of said subsets;
`characterizing or assigning a
`representative value to each of said
`subsets; then merging entries in a
`predetermined manner in a single list
`based on said representative value; and
`wherein the representative value varies
`in according with said representative
`value in combination with the other
`limitations of the claims . . . ."
`
`'704
`
`1, 12
`
`selecting a
`subset of
`entries from
`each result list
`
`Plain and ordinary
`meaning to a
`person of ordinary
`skill in the art in
`
`
`
`Seymor Lipschutz and Marc Lipson,
`Schaum's Outline of Discrete
`Mathematics 2 (1997). VALTRUS-
`GOOGLE-NDTX-00008495-00008506.
`
`11124723
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`- 19 -
`
`
`
`019
`
`

`

`
`Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 20 of 147 PageID 1316Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 20 of 147 PageID 1316
`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`
`light of the
`specification.
`
`'704
`
`1, 12
`
`
`
`producing a
`merged list of
`entries /
`merging
`entries . . . into
`a single list
`
`Plain and ordinary
`meaning to a
`person of ordinary
`skill in the art in
`light of the
`specification.
`
`'704
`
`6, 17
`
`probability
`value
`
`A value
`representing how
`likely a list is to be
`selected.
`
`'704 Patent at 6:20-22: "Each list is
`assigned a probability value equal to its
`representative value's percentage of the
`total representative values for all lists."
`
`'704 Patent at 7:35-54: "According to
`another embodiment, each list 110, 112,
`114 is assigned a probability value equal
`to its average scoring value's percentage
`of the total of all average scoring values.
`Entries are then selected from each list
`based on its probability value. Here, for
`instance, the total of all average scoring
`values 130, 132, 134 is
`11.25+14.95+13.225=39.425. This
`means result list 110 is assigned a
`probability value equal to
`(11.25/39.425)100%=28.54%. In like
`
`Orlando A. Oronce and Marilyn O.
`Mendoza, Exploring Mathematics:
`Geometry III 11 (2003). VALTRUS-
`GOOGLE-NDTX-00008507-00008513.
`
`E. Kamke, Theory of Sets 6 (1950).
`VALTRUS-GOOGLE-NDTX-
`00008478-00008490.
`
`
`
`"Probability." Steven M. Kaplan, Wiley
`Electrical and Electronics Engineering
`Dictionary 605 (2004). VALTRUS-
`GOOGLE-NDTX-00007435-00007455.
`
`"Probability." Rudolf F. Graf, Modern
`Dictionary of Electronics 589 (1999).
`VALTRUS-GOOGLE-NDTX-
`00007456-00007470.
`
`"Probability." Microsoft Computer
`Dictionary 423 (Alex Blanton and
`Sandra Haynes, eds., 5th ed. 2002).
`VALTRUS-GOOGLE-NDTX-
`00007483-00007498.
`
`"Probability." Dick Pountain, The New
`Penguin Dictionary of Computing 386
`
`11124723
`
`
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`- 20 -
`
`
`
`020
`
`

`

`
`Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 21 of 147 PageID 1317Case 3:22-cv-00066-N Document 83-1 Filed 07/25/22 Page 21 of 147 PageID 1317
`EXHIBIT A – Valtrus's Proposed Constructions and Intrinsic and Extrinsic Evidence
`
`
`manner, result list 112 is assigned a
`probability value of
`(14.95/39.425)100%=37.92%, and result
`list 114 is assigned a probability value of
`(13.225/39.425)100%=33.54%. Result
`lists are then selected in pseudorandom
`fashion, where at each selection result
`list 110 has a 28.54% chance of being
`picked, list 112 has a 37.92% chance,
`and list 114 has a 33.54% chance. Once
`a list is selected, the first entry that has
`not already been selected is picked.
`Once every entry in a list is selected, the
`total of all average scoring values is
`recalculated without that list's average
`scoring value, and the process continues
`until every entry of every list has been
`selected."
`
`(2001). VALTRUS-GOOGLE-