Ex Parte Jung et alDownload PDFPatent Trial and Appeal BoardJun 24, 201410909200 (P.T.A.B. Jun. 24, 2014) Copy Citation UNITED STATES PATENT AND TRADEMARK OFFICE UNITED STATES DEPARTMENT OF COMMERCE United States Patent and Trademark Office Address: COMMISSIONER FOR PATENTS P.O. Box 1450 Alexandria, Virginia 22313-1450 www.uspto.gov APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO. 10/909,200 07/30/2004 Edward K.Y. Jung SE1-0025-US 5385 80118 7590 06/24/2014 Constellation Law Group, PLLC P.O. Box 580 Tracyton, WA 98393 EXAMINER SYED, FARHAN M ART UNIT PAPER NUMBER 2165 MAIL DATE DELIVERY MODE 06/24/2014 PAPER Please find below and/or attached an Office communication concerning this application or proceeding. The time period for reply, if any, is set in the attached communication. PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte EDWARD K. Y. JUNG and CLARENCE T. TEGREENE ____________ Appeal 2011-011634 Application 10/909,200 Technology Center 2100 ____________ Before MAHSHID D. SAADAT, BRUCE R. WINSOR, and CATHERINE SHIANG, Administrative Patent Judges. WINSOR, Administrative Patent Judge. DECISION ON APPEAL Appellants appeal under 35 U.S.C. § 134(a) from the Final Rejection of claims 1-27. Claims 28-108 are withdrawn from consideration. We have jurisdiction under 35 U.S.C. § 6(b). We affirm. Appeal 2011-011634 Application 10/909,200 2 STATEMENT OF THE CASE Introduction Appellants’ disclosure relates to the retrieval of stored occurrence data matching a selected representative feature using pattern recognition criteria. (Spec. 5:18-29.) In a preferred embodiment, the occurrence data includes sensor data generated by a plurality of networked remote sensor nodes or motes. (Spec. 14:6-7.) A mote is typically composed of sensors, actuators, computational entities, and/or communications entities, and is typically understood to be a semi-autonomous computing, communication, and sensing device. (Spec. 4:17-26; 5:9-14; 14:6-11.) In Appellants’ disclosed invention, the motes use their sensors to acquire data related to one or more parameters of an environment in which they are located, store the acquired data in their own data storage device, and transmit the acquired data to a central computing device where it is stored in a central digital storage device. (Spec. 14:16-27; 15:10-26; 19:14-20:12; 21:4-10; 22:12-23:8.) The sensor data is acquired and stored as a chronological sequence that may have at least one representative signal feature defining an “occurrence”, such as a car accident, a siren sound, a gun shot, etc. (Spec. 16:22-17:28.) A target occurrence of interest is defined by an input selection performed by a user, machine, or computer program, and the input selection may include the selection of one or more representative features of the target occurrence. (Spec. 17:24-28; 21:25-26; 23:14-27; 24:18-24.) Pattern recognition criteria corresponding to the one or more representative features of the target occurrence is selected, and the stored chronological sequence of sensor data is automatically searched using the selected pattern recognition criteria to recognize when one or more signal features contained within the stored data Appeal 2011-011634 Application 10/909,200 3 sequence correlate to the signal features representative of the target occurrence. (Spec. 17:1-28; 24:5-25:5.) An output indicative of a result of the automatic search is generated. (Spec. 5:24-27; 21:26-27; 37:9-13; 40:27- 41:6; 41:15-42:15.) The functions of receiving an input selection, selecting pattern recognition criteria, automatically searching the sensor data, and providing an output are all performed by a computing device in accordance with programmed instructions. (Spec. 5:18-29; 20:9-21; 22:1-11; 28:3-12.) Exemplary independent claim 1 reads as follows: 1. An occurrence-data retrieval system, comprising: (a) a data storage operable to store a plurality of instances of occurrence-data provided by multiple sensors, each instance of the occurrence-data having a representative feature; (b) a central computing device operable to communicate with the data storage; and (c) instructions that cause a computing device to perform steps including: (i) receive from an input-selector an input selection corresponding to a target-occurrence having a representative feature; (ii) select a pattern recognition criteria corresponding to the representative feature of the target-occurrence, the pattern recognition criteria including a chronological sequence; (iii) automatically search the plurality of instances of stored occurrence-data for data correlating to the target- occurrence using the selected pattern recognition criteria; and (iv) provide an output indicative of a result of the automatic search. Applied Prior Art The Examiner relies on the following prior art in rejecting the claims: Lamming US 5,539,665 July 23, 1996 Appeal 2011-011634 Application 10/909,200 4 Sam Madden et al. TinyDB: In-Network Query Processing in TinyOS, September 2003 (hereinafter “Madden TinyDB”). Samuel Madden et al. Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks, June 21, 2002 (hereinafter “Madden 1”). The Examiner’s Rejections Claims 1-20 stand rejected under 35 U.S.C. § 103(a) as unpatentable over Madden TinyDB, and Lamming. (See Ans. 5-11.) Claims 21-27 stand rejected under 35 U.S.C. § 103(a) as unpatentable over Madden TinyDB, Lamming, and Madden 1. (See Ans. 11-16.) Appellants’ Contentions 1. With respect to the rejection of claim 1, Appellants contend the combination of Madden TinyDB, and Lamming is improper because: (a) there is no motivation to combine Madden TinyDB, and Lamming and these references may not be properly combined in the manner suggested by the Examiner because Madden TinyDB states that temporal queries are not supported and, therefore, cannot operate in the manner taught by Lamming (App. Br. 38);1 (b) Lamming stores a chronological sequence of data, but only searches for discrete events and, therefore, does not teach searching the stored data for data correlating to a target occurrence using a selected pattern recognition criteria including a chronological sequence (App. Br. 38-39 ); and 1 We refer to the Appeal Brief filed March 31, 2011. The pages of the Appeal Brief are not numbered. Consistently with the Table of Contents in the Appeal Brief (App. Br. 2-3), we refer to the pages of the Appeal Brief consecutively, with the page bearing the caption information referred to as page 1 and the page bearing the signature of Appellants’ attorney referred to as page 43. Appeal 2011-011634 Application 10/909,200 5 (c) the Examiner has not met his burden to establish a prima facie case of unpatentability by providing objectively verifiable evidence establishing that Lamming teaches searching the stored data using a selected pattern recognition criteria including a chronological sequence (App. Br. 39). 2. Appellants contend that dependent claims 2-4 are separately patentable, but rely only on contentions 1a and 1b and offer no additional arguments addressing the Examiner’s mapping of the teachings of Madden TinyDB and Lamming to the subject matter of claims 2-42 (App. Br. 39-41). Thus, claims 2-4 stand or fall with claim 1. 3. Appellants argue the patentability of dependent claims 5- 27based on the reasons asserted for base claim 1 (App. Br. 39). Thus, claims 5-27 also stand or fall with claim 1. The Examiner’s Findings – Claim 1 The Examiner finds (Ans. 5-6) Madden TinyDB teaches a query processing system for extracting stored occurrence data from a network of motes each containing one or more sensors (Madden TinyDB, § 2, 1st ¶). Given a query specifying the user’s data interests, i.e., specifying a representative feature of interest, the query processing system collects the queried occurrence data from the network of motes in the environment, filters it, and aggregates it together, i.e., the queried data matching a selected representative feature is collected and placed in data storage (id.). The aggregated data is routed out of the mote network to a central computing 2 To show error in the Examiner’s position, Appellants must explain why the relied-upon disclosure does not teach or suggest the claimed feature under its broadest reasonable interpretation in light of the Specification, rather than merely reciting the claimed feature and alleging that it is different. See discussion infra. Appeal 2011-011634 Application 10/909,200 6 device in the form of a PC (id.). The query is input using a simple SQL-type graphical user interface, i.e., an input selector, by which the user defines a target occurrence of interest having at least one representative feature (id., § 2, 1st ¶; § 2.1, last ¶; §§ 3.3-4.1). The query processor automatically searches the stored occurrence data using a selected pattern recognition criteria to determine which occurrence data matches the at least one representative feature of the target occurrence defined by the user’s query (id., § 2, 1st ¶; §§ 2.1, 4, 4.1). An output indicative of a result of the automatic search is generated (id., § 2, 2nd ¶; § 2.1, last ¶; §§ 3.3, 4.1). The functions of receiving an inputted query, selecting pattern recognition criteria, automatically searching the sensor data, and providing an output are all performed by the query processing system, i.e., a computing device, in accordance with programmed instructions (id., § 2, 1st ¶). The Examiner finds (Ans. 6) Madden TinyDB does not teach that the pattern recognition criteria used by the query processor include a chronological sequence. The Examiner further finds (Ans. 6-7) Lamming teaches an analogous system to that of Madden TinyDB for collecting, storing, and retrieving occurrence data detected by a plurality of sensors. In Lamming’s system, the stored occurrence data is organized as a set of threads, with each of the threads comprising a chronological sequence of time-stamped sensor data. (Lamming, col. 2, ll. 52-56; col. 2, l. 60-col. 3, l. 1; col. 5, ll. 38-59). The system is operable through a user interface to enable a user to retrieve from the stored occurrence data a chronological sequence of target occurrences defined by the user (id., col. 3, ll. 8-21, 43- 57). The user’s query may define the target occurrences of interest by specifying a pattern within the stored chronological sensor data (id., col. 6, Appeal 2011-011634 Application 10/909,200 7 ll. 52-58; col. 8, ll. 26-29; col. 9, ll. 65-67). The query processor automatically searches the stored occurrence data using selected pattern recognition criteria to determine which chronological sequences of occurrence data match the pattern defined by the user’s query, and the matching data sequences are displayed to the user (id., col. 7, ll. 1-19; col. 10, ll. 3-24; Fig. 5). Based on this teaching in Lamming, the Examiner concludes (Ans. 7) that it would be obvious to modify Madden TinyDB to include a chronological sequence in the pattern recognition criteria used by the query processor in order to provide information relevant to activities in a sensor network. That is, the stored sensor data can be used to recognize not only point-in-time events occurring in the sensor network but also activities or occurrences defined by a chronological sequence of sensor data. Issues on Appeal Has the Examiner erred in rejecting claims 1-27 as obvious over various combinations of Madden TinyDB, Lamming, and Madden 1? ANALYSIS We have reviewed the Examiner’s rejections in light of Appellants’ contentions that the Examiner has erred. We agree with the Examiner’s findings and conclusions with respect to the rejections under § 103. Appellants’ Reply Brief (filed Oct. 6, 2011) brings to our attention the adequate notice requirements discussed in In re Jung, 637 F3d 1356, 1362 (Fed. Cir. 2011). We find the rejections made by the Examiner give adequate notice to Appellants to determine whether to continue prosecution and to counter the rejections. See 35 U.S.C. § 132(a). Appeal 2011-011634 Application 10/909,200 8 35 U.S.C. § 103 Rejections As an initial matter, we note Appellants have styled their arguments as a challenge to the existence of a prima facie case of unpatentability of the claims at issue (App. Br. 39). With respect to the rejections of record, and as set forth supra, the Examiner specifically puts Appellants on notice that the Examiner considered the query processing system of Madden TinyDB as corresponding to the claimed occurrence-data retrieval system (see Ans. 5- 6). The Examiner similarly makes explicit findings regarding the elements disclosed by Lamming and the other prior art relied upon, and explains how the elements are interpreted as corresponding to the respective features of the appealed claims (see generally, Ans. 6-16). As explained in In re Jung, 637 F3d 1356 (Fed. Cir. 2011), these findings and explanations would have put reasonable Applicants on notice of the Examiner’s rejection, and given them ample information with which to counter the grounds of rejection, which is all that is required to establish a prima facie case of unpatentability. Moreover, in order to rebut a prima facie case of unpatentability, Applicants are required to distinctly and specifically point out the supposed errors in the Examiner’s action as well as the specific distinctions believed to render the claims patentable over the applied references. See 37 C.F.R. § 41.37(c)(vii) (“A statement which merely points out what a claim recites will not be considered an argument for separate patentability of the claim.”); see also In re Lovin, 652 F.3d 1349, 1357 (Fed. Cir. 2011) (“[W]e hold that the Board reasonably interpreted Rule 41.37 to require more substantive Appeal 2011-011634 Application 10/909,200 9 arguments in an appeal brief than a mere recitation of the claim elements and a naked assertion that the corresponding elements were not found in the prior art.”); cf. In re Baxter Travenol Labs., 952 F.2d 388, 391 (Fed. Cir. 1991) (“It is not the function of this [board] to examine the claims in greater detail than argued by an appellant, looking for [patentable] distinctions over the prior art.”). Appellants have failed to do this at least with respect to contention 1c, which ignores the Examiner’s mapping of the teachings of Madden TinyDB and Lamming to the subject matter of claim 1, and the articulated reasoning with specific rational underpinning provided by the Examiner to support the conclusion of obviousness (see Ans. 5-7). Therefore, with respect to Appellants’ contention 1c, in each of the rejections of record, the Examiner has cited specific portions of the prior art references, and provided an explanation as to how the disclosed elements are interpreted as corresponding to the features of the claims. The cited portions of the references and the accompanying explanations collectively constitute the “objectively verifiable evidence” which Appellants allege to be lacking. Additionally, we are unpersuaded by Appellants’ above-noted contention because the test of whether a reference teaches a claim limitation is not whether the exact language is present but rather whether the limitations are taught or suggested by the prior art when the claim is given its broadest reasonable interpretation in light of the Specification. Cf. In re Bond, 910 F.2d 831, 832-33 (Fed. Cir. 1990) (citing Akzo N.V. v. U.S. Int’l Trade Comm’n, 808 F.2d 1471, 1479 & n.11 (Fed. Cir. 1986)) (interpretation of references “is not an ‘ipsissimis verbis’ test.”); Standard Havens Prods., Inc. v. Gencor Indus., Inc., 953 F.2d 1360, 1369 (Fed. Cir. Appeal 2011-011634 Application 10/909,200 10 1991). “[A] . . . reference . . . need not duplicate word for word what is in the claims.”). Regarding Appellants’ contention 1a, we agree with the Examiner’s finding that Lamming teaches an analogous system to that of Madden TinyDB for collecting, storing and retrieving occurrence data detected by a plurality of sensors (Ans. 6-7). We also agree with the Examiner that Lamming’s teaching of pattern recognition criteria capable of recognizing a chronological sequence of stored occurrence data offers an improvement over the query processing system for extracting stored occurrence data of Madden TinyDB (id. 16-17). Specifically, Lamming’s system enables a user to specify a pattern representing target occurrences of interest to the user (Lamming, col. 3, ll. 8-21, 43-57; col. 6, ll. 52-58; col. 8, ll. 26-29; col. 9, ll. 65-67). The system automatically searches the stored occurrence data using a selected pattern recognition criteria to determine which chronological sequences of occurrence data match the pattern defined by the user’s query and displays the matching data sequences to the user (id., col. 7, ll. 1-19; col. 10, ll. 3-24; Fig. 5). One of ordinary skill in the art would have recognized from the teachings of Lamming that such an approach (using pattern recognition criteria including a chronological sequence to identify stored occurrence data matching a pattern defined by a query) could be applied to Madden TinyDB by using the query processing system of Madden TinyDB to apply a pattern recognition criteria including a chronological sequence to the stored sensor data collected by the motes of Madden TinyDB. Further, one of ordinary skill in the art would have appreciated that such a modification to Madden TinyDB would improve the query processing system by enabling it to identify occurrences defined by a chronological Appeal 2011-011634 Application 10/909,200 11 sequence of sensor data. That is, using pattern recognition criteria including a chronological sequence, as taught by Lamming, enables the system to recognize not only point-in-time events occurring in the sensor network but also activities or occurrences defined by a chronological sequence of sensor data. Thus, Appellants’ contention 1a, and specifically the argument that there is no motivation to combine Madden TinyDB and Lamming, does not persuasively rebut the articulated reasoning with specific rational underpinning provided by the Examiner to support the conclusion of obviousness (see Ans. 5-7). Further, the teaching-suggestion-motivation (TSM) test can be a helpful insight, but cannot be used as a rigid and mandatory formula. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 419 (2007). Rather, rejections based on obviousness must be supported by “some articulated reasoning with some rational underpinning” to combine known elements in the manner required by the claim. Id. at 418 (citing In re Kahn, 441 F.3d 977, 977 (Fed. Cir. 2006)). Other rationales for supporting the Examiner’s conclusion of obviousness include, inter alia, applying known techniques to improve similar devices, methods, or products in the same way. Id. at 417. Here, the Examiner reasons that it would have been obvious to use the known techniques of Lamming to improve the similar system of Madden TinyDB in the same way as Lamming’s system is improved (see Ans. 7, 17). Further, regarding Appellants’ contention 1a, and specifically the argument that Madden TinyDB cannot operate in the manner taught by Lamming because Madden TinyDB states that temporal queries are not supported, one cannot show nonobviousness by attacking references individually when the rejection is based on a combination of references. In Appeal 2011-011634 Application 10/909,200 12 re Keller, 642 F.2d 413, 425 (CCPA 1981). Each reference cited by the Examiner must be read, not in isolation, but for what it fairly teaches in combination with the prior art as a whole. See In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986). All of the features of the secondary reference need not be bodily incorporated into the primary reference (see Keller, 642 F.2d at 425) and the artisan is not compelled to blindly follow the teaching of one prior art reference over the other without the exercise of independent judgment (see Lear Siegler, Inc. v. Aeroquip Corp., 733 F.2d 881, 889 (Fed. Cir. 1984)). Thus, contention 1a does not persuade us of error in the Examiner’s position because Appellants have not persuaded us that the relied-upon disclosure of the combined references does not teach or suggest the claimed feature the Examiner mapped to that part of the reference. See KSR, 550 U.S. at 418. Further, it is noted that Madden TinyDB describes storing the sensor data in mote memory and retrieving it for a specified sample period (Madden TinyDB, § 4.6). This is consistent with Lamming’s system of storing and retrieving chronological sequences of sensor data. That is, sensor data retrieved for a specified sample period is similar to sensor data retrieved for a given chronological sequence. Still further, Madden TinyDB also teaches using a relational database to store all of the sensor data for later retrieval and analysis (id., § 6). Again, this is similar to Lamming’s system of storing chronological sequences of sensor data for later retrieval and analysis. This provides further evidence, in addition to that already discussed above, that one of ordinary skill in the art would have readily recognized the teachings of Lamming could be applied to Madden TinyDB. Appeal 2011-011634 Application 10/909,200 13 Regarding Appellants’ contention 1b, we agree with the Examiner’s findings that Lamming teaches occurrence data stored as a chronological sequence, a user-defined target occurrence of interest specifying a pattern within the stored chronological sensor data, and a query processor that automatically searches the stored occurrence data using a selected pattern recognition criteria including a chronological sequence to determine which chronological sequences of occurrence data match the pattern defined by the user’s query (Ans. 6-7). We further agree with the Examiner that this interpretation is consistent with Appellants’ disclosure (see Spec. 18:29- 19:13), which defines pattern recognition criteria as including anything that recognizes, identifies or establishes a correspondence with one or more representative features of an occurrence (Ans. 18; see Spec. 19:4-6). Further, Appellants’ argument that Lamming only searches for discrete events is not persuasive because Lamming’s system stores chronological sequences of occurrence data organized as threads (Lamming, col. 2, ll. 52-56; col. 2, l. 60-col. 3, l. 1; col. 5, ll. 38-59), and enables a user to retrieve from the stored occurrence data a chronological sequence of target occurrences defined by the user (Lamming, col. 3, ll. 8-21, 43-57). The matching chronological data sequence (not just discrete events) is displayed to the user (Lamming, col. 7, ll. 1-19; col. 10, ll. 3-24; Fig. 5). For these reasons, we are not persuaded that Lamming fails to teach searching the stored data for data correlating to a target occurrence using a selected pattern recognition criteria including a chronological sequence. In view of our analysis above, Appellants’ contentions have not persuaded us of error in the Examiner’s findings and conclusion that claims Appeal 2011-011634 Application 10/909,200 14 1-27 are obvious over various combinations of Madden TinyDB, Lamming, and Madden 1. DECISION The Examiner’s decision rejecting claims 1-27 under 35 U.S.C. § 103(a) is affirmed. No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv). AFFIRMED kme Copy with citationCopy as parenthetical citation