Northeastern University et al v. Google, Inc.,

Filing 62

NOTICE by Northeastern University, Jarg Corporation, Google, Inc., - Joint Claim Construction and Prehearing Statement (Attachments: # 1 Exhibit A, # 2 Exhibit B, # 3 Exhibit C)(Valek, Michael)

EXHIBIT B PLAINTIFFS' PROPOSED CONSTRUCTIONS FOR DISPUTED CLAIM TERMS AND EVIDENTIARY SUPPORT

US 53837v.1

No. Term 1. non-relational, distributed database system

Claim(s) claims 1, 8, 13

Plaintiffs' Proposed Construction & Evidentiary Support Construction: a database not using a relational model that is distributed among a plurality of interconnected computer nodes Intrinsic Support: Abstract; Fig. 1; 1:5-6; 1:10-62; 1:65-2:2; 2:3-18; 2:56-3:16; 3:17-3:36; 3:4650; 4:15-21; 4:22-55; 7:50-65; 10:4-17; 10:18-20 June 7, 1996 Amendment p. 7-19; December 4, 1996 Amendment p. 7-9; May 14, 1997 Amendment p. 3-4;

2.

a plurality of home nodes and a plurality of query nodes connected by a network a plurality of home nodes; and a plurality of query nodes; said plurality of home nodes and said plurality of query nodes connected by a network

claim 1

Construction: The claim language has its plain and ordinary meaning; no further construction necessary.

claims 8, 13

Intrinsic Support: Fig. 1-2; Abstract; 1:65-2:18; 2:66-3:36; 3:51-59; 4:8-14; 4:22-35; 7:23-49; 10:4-20. June 7, 1996 Amendment p. 16; May 14, 1997 Amendment p. 2-3;

2
US 53837v.1

No. Term 3. randomly selecting

Claim(s) claim 1

Plaintiffs' Proposed Construction & Evidentiary Support Construction: selecting without an apparent pattern Intrinsic Support:

Fig. 1; June 7, 1996 Amendment p. 13, 16. "High-Performance, Distributed Information Retrieval," Kenneth Baclawski and J. Elliott Smith. p. 3. "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 4 "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 4, 9, 13. "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 14, 19-20. "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 4.

Extrinsic Support: 3
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support "random adj 1 : lacking or seeming to lack a regular plan, purpose or pattern" WEBSTER'S THIRD NEW INTERNATIONAL DICTIONARY OF THE ENGLISH LANGUAGE 1880 (Phillip Babcock Gove ed., 1993).

4.

query fragment

claims 1, 8, 13

Construction: a sub-part or piece of a query Intrinsic Support: Figs. 4, 7, 8; Abstract; 2:4-17; 3:25-50; 4:60-66; 7:23-49; 8:66-9:5; 9:30-53; 9:61-10:3. May 4, 1997 Amendment at p. 2-3. Dec. 4, 1996 Amendment at p. 7. June 7, 1996 Amendment p. 7-17. "High-Performance, Distributed Information Retrieval," Kenneth Baclawski and J. Elliott Smith. p. 2. "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 8-9

4
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 4, 8-9, 14. "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 14, 19-21. "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 17-26. "KEYNET: Fast indexing for semantically rich information retrieval," Kenneth Bac1awski and J. Elliott Smith, p. 10-15. "High-Performance Indexing and Retrieval for Object-Oriented Databases," Kenneth Baclawski, p. 7, 10, 19 Extrinsic Support: "fragment n 1 : a part broken off : a small detached portion : an imperfect or incomplete part" WEBSTER'S THIRD NEW INTERNATIONAL DICTIONARY OF THE ENGLISH LANGUAGE 901 (Phillip Babcock Gove ed., 1993) Construction: a computer technique whereby one or more functions are used to transform values into corresponding values Intrinsic Support:

5.

hashing / hashes

claims 1, 8, 13

5
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support Figs. 4, 7a, 8; Abstract; 2:3-11; 3:25-50; 3:60-62; 5:2-5; 6:65-67; 7:23-38. June 7, 1996 Amendment, p. 7, 14-16. Dec. 4, 1996 Amendment, p. 7. May 14, 1997 Amendment, p. 4. "High-Performance, Distributed Information Retrieval," Kenneth Baclawski and J. Elliott Smith. p. 3. "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 4-5 "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 10, 17 "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 6., 1415, 21 "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 5. "KEYNET: Fast indexing for semantically rich information retrieval," Kenneth Bac1awski and J. Elliott Smith, p. 3, 8-15.

6
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support Extrinsic Support: "Hashing (or hash coding) is a word coined by computer programmers to describe a general class of operations done to transform one or more fields (usually a key) into a different (usually more compact) arrangement. Probably, "hashing" was first coined because it seemed that "hash" was being made out of integral pieces of data. The rationale for hashing is developed more fully in the article on table lookup, dealing with key transformation to convert naturally occurring, diverse, ill-structured, scattered key fields into compact, easily manipulated fields--usually some numeric, computer oriented field such as a word or double word, or a computer memory address to facilitate subsequent references. The transformation from the natural field to the hash address is only a one-way process, however; the natural field cannot be decoded or reconstructed from the hash. Also, the hashed field may not represent only one unique natural field; many natural fields could hash into the same value." ENCYCLOPEDIA OF COMPUTER SCIENCE AND ENGINEERING 681 (Anthony Ralston & Edwin D. Reilly, Jr. eds.,1983). "Hashing (1) (* Processing) A technique for converting the values within fields into more `compact' representations of the same values. Usually, the values being converted are keys. (2) (* Addressing) An addressing technique used to store and retrieve data in a file. Hashing uses keys in an index to determine the location of the data in the file." ROBERT A EDMUNDS, THE PRENTICE-HALL STANDARD GLOSSARY OF COMPUTER TERMINOLOGY, 191-192 (1985). "Hashing: Hashing refers to a storage mechanism where data items are stored at locations that are determined by a mathematical function of the data. For example, suppose you need to store a list of 100 numbers in memory locations whose addresses run from 1 to 100. One example of a hashing 7

US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support function is to calculate the remainder when the number is divided by 100 and then store it at that location. For example, the number 538 would be stored at memory location 38. The use of hashing makes it possible to store and retrieve the data items quickly since it is not necessary to search through the list in order to find the item. However, there is one complication: A hashing function will sometimes assign more than one data item to the same address. For example, using the rule given above, the number 638 would also be stored in location 38. To avoid that problem, a hashing system needs to be able to resolve collisions by storing the new data item in a separate place." DOUGLAS DOWNING & MICHAEL COVINGTON, BARRON'S DICTIONARY OF COMPUTER TERMS 158 (3d ed. 1992). "Hashing: (1) Key-to-address transformation in which the keys determine the location of the data. (2) The process of applying a formula to a record key to yield a number that represents a disk address." DONALD D. SPENCER, COMPUTER DICTIONARY (3d ed. 1992). "Hashing: In database management, an indexing technique in which the value of a key (record identifier) is numerically manipulated to directly calculate either the location of its associated record in a file or the starting point for a search for the associated record." MICROSOFT COMPUTER DICTIONARY 193 (2d ed. 1994).

6.

a first portion and a second portion

claims 1, 8, 13

Construction: The claim language has its plain and ordinary meaning; no further construction necessary. Intrinsic Support: 8

US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support 3:36-50; 4:64-5:2; 6:59-65; 7:23-38; 7:50-55.

7.

claim 1 transmitting, by said selected home node, each said hashed query fragment of said plurality of query fragments to a respective one of said plurality of query nodes indicated by said first portion of each said hashed query fragment transmits each said hashed query fragment to a claim 8 respective one of said plurality of query nodes indicated by said first potion of said hashed query fragment transmitting a query message containing every said hashed query fragment to a respective one of said claim 13 plurality of query nodes indicated by said first portion of said hashed query fragment using, by said query node, claim 1

Construction: The claim language has its plain and ordinary meaning; no further construction necessary. Intrinsic Support: Figs. 1, 2, 3, 8, 8a; Abstract; 2:3-18; 3:17-50; 4:23-36; 7:23-49. "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 3. "KEYNET: Fast indexing for semantically rich information retrieval," Kenneth Bac1awski and J. Elliott Smith, p. 9, 13. June 7, 1996 Amendment, p. 14.

8.

Construction: 9

US 53837v.1

No. Term said second portion of said respective hashed query fragment to access data according to a local hash table located on said query node each said query node uses said second portion of said hashed query fragment to access data according to a local hash table located on said query node said query node, upon receipt of said query message, using said second portion of said hashed query fragment to access data according to a local hash table located on said query node 9. local hash table

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support The claim language has its plain and ordinary meaning; no further construction necessary. Intrinsic Support: Figs. 1, 2, 3a; 2:12-18; 3:37-59; 6:1-28; 7:50-65

claim 8 "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 4-5. "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 10. claim 13 "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 14-15. "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 5. June 7, 1996 Amendment, p. 14. Construction: a table that associates hash values with other data Intrinsic Support:

claims 1, 8, 13

10
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support Figs. 1, 2, 3a; 2:12-18; 3:37-59; 6:1-28; 7:50-65; 8:64-66; "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 2, 4, 5 "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 2, 3, 10. "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 10, 12, 14-15. "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 3, 5. "KEYNET: Fast indexing for semantically rich information retrieval," Kenneth Bac1awski"and J. Elliott Smith, p. 6, 12, 17. May 14, 1997 Amendment, p. 4. June 7, 1996 Amendment, p. 14. Extrinsic Support: "Hash table: A table of information that is accessed by way of a shortened search key (the hash value). Using a hash table minimizes average search time." IBM DICTIONARY OF COMPUTING 309 (George McDaniel ed., 10th ed. 1994). 11

US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support "Hash Table: A data structure that maps a set of keys to slots from a hashing function." Ricardo A. Baeza-Yates, Introduction to Data Structures and Algorithms Related to Information Retrieval, in INFORMATION RETRIEVAL: DATA STRUCTURES AND ALGORITHMS 13, 20 (William B. Frakes & Ricardo A. Baeza-Yates, eds., 1992). Construction: The claim language has its plain and ordinary meaning; no further construction necessary. Intrinsic Support:

10.

returning, by each said query node each said query node ... returns said query node ... returning

claim 1

claim 8

claim 13 Figs. 1, 2, 3a; 2:12-18; 3:55-65; 4:2-7; 7:50-65; 8:8-15 June 7, 1996 Amendment, p. 18. Construction: a degree of relevance that is determined before returning accessed data to the user Intrinsic Support: 3:60-4:21; 9:44-47. "High Performance, Distributed Information Retrieval," by Kenneth Baclawski and J. Elliott Smith, p. 2.

11.

predetermined degree of relevance

claims 3, 9

12
US 53837v.1

No. Term

Claim(s)

Plaintiffs' Proposed Construction & Evidentiary Support "High-Performance Indexing and Retrieval for Object-Oriented Databases," Kenneth Baclawski, p. 24. "A distributed approach to high-performance information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 5, 7, 9. "A unified approach to high-performance, vector-based information retrieval," Kenneth Baclawski and J. Elliott Smith, p. 9, 10. "KEYNET: An architecture and protocol for high-performance semantically rich information retrieval," Kenneth Baclawski* and J. Elliott Smith, p. 4, 68, 10, 15, 20. "An abstract model for semantically rich information retrieval," Kenneth Baclawski and Dan A. Simovici, p. 1, 5, 14. "KEYNET: Fast indexing for semantically rich information retrieval," Kenneth Bac1awski and J. Elliott Smith, p. 2, 4-7, 9, 11, 14. June 7, 1996 Amendment, p. 16. December 4, 1996 Amendment at p. 8-9.

13
US 53837v.1